tag:blogger.com,1999:blog-343689592024-03-19T03:32:54.828-04:00Customer Experience MatrixThis is the blog of David M. Raab, marketing technology consultant and analyst. Mr. Raab is founder and CEO of the Customer Data Platform Institute and Principal at Raab Associates Inc. All opinions here are his own. The blog is named for the Customer Experience Matrix, a tool to visualize marketing and operational interactions between a company and its customers.David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.comBlogger939125tag:blogger.com,1999:blog-34368959.post-68642459771852733622024-02-16T08:50:00.052-05:002024-02-23T15:32:14.315-05:00Composable vs Packaged CDP: How Can We Help?<p style="text-align: left;"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhtEbpTtWdg2Qb56PyHGDH8eKyL89PQ2kgSELoCqH0jDtSxDkgiYuFPsNjRxgKsjhQ13JZEJ_KdJCuQChP2DP_H3hO7uFfw-vyWCcteNpF967-75K60hWke7ZgzHNwzZBAmp4AA7wNDhWA4C73_mikD7rLyGqHEAKbpR_v1fnvoPOholEf64_6j/s1800/CDP%20Path.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="304" data-original-width="1800" height="108" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhtEbpTtWdg2Qb56PyHGDH8eKyL89PQ2kgSELoCqH0jDtSxDkgiYuFPsNjRxgKsjhQ13JZEJ_KdJCuQChP2DP_H3hO7uFfw-vyWCcteNpF967-75K60hWke7ZgzHNwzZBAmp4AA7wNDhWA4C73_mikD7rLyGqHEAKbpR_v1fnvoPOholEf64_6j/w640-h108/CDP%20Path.png" width="640" /></a></div>“Composable CDP” has been consuming much attention at CDP
Institute as we wrestle with how best to help buyers who are considering it as
an option.<span style="mso-spacerun: yes;"> </span>The Institute published a
<a href="https://docs.google.com/forms/d/e/1FAIpQLSdqXqewXKajN_uoVIIrLCluSdBzJ4xv4ed8QTtjdioAeNQXWQ/viewform?usp=sf_link" target="_blank">Composable CDP Self-Assessment tool</a> a few weeks ago, which gives a checklist of the
functions required to replicate the profile-building capabilities of a standard
CDP.<span style="mso-spacerun: yes;"> </span>This was intended as the
centerpiece of our <a href="https://www.cdpinstitute.org/composable-cdp-knowledge-hub/" target="_blank">Composable CDP Knowledge Hub</a><span style="mso-spacerun: yes;"> </span>but has attracted fewer users than
expected.<span style="mso-spacerun: yes;"> </span><span style="mso-spacerun: yes;"> </span>The explanation I find most plausible is that
few people can answer the Self-Assessment’s detailed questions about existing
systems and requirements.<span style="mso-spacerun: yes;"> </span><p></p>
<p class="MsoNoSpacing" style="text-align: left;">In itself, this isn’t terrible: much of the value in the
Self-Assessment comes from giving users a comprehensive checklist of items to
consider, thereby highlighting the true scope of a Composable project.<span style="mso-spacerun: yes;"> </span>Still, it’s clear the Self-Assessment isn’t
helping buyers as much as we had expected, so we’re considering what other
tools might be more useful.</p>
<p class="MsoNoSpacing" style="text-align: left;"></p>
<p class="MsoNoSpacing" style="text-align: left;">The discussions surrounding this have helped to clarify my
own understanding of where Composable CDP fits into the greater scheme of
things.<span style="mso-spacerun: yes;"> </span>The key insight is that the
choice to use a Composable or packaged CDP is ultimately a tactical decision
that addresses just one stage of the CDP project.<span style="mso-spacerun: yes;"> </span>It doesn’t affect the previous stage, which
is requirements definition, or the following stages, which are deployment and
activation.<span style="mso-spacerun: yes;"> </span>To overstate the case just a
bit, marketers and other CDP users don’t care how their CDP gets built; they
just want to use the resulting profiles to do their jobs.<span style="mso-spacerun: yes;"> </span>To the extent that CDP users do care about
the Composable vs packaged decision, it’s because that choice impacts the cost,
timing, and quality of their system.</p>
<p class="MsoNoSpacing" style="text-align: left;"></p>
<p class="MsoNoSpacing" style="text-align: left;">This insight in turn clarifies the distinction between
knowing enough to make the Composable vs packaged decision, and actually deciding
which is the best choice.<span style="mso-spacerun: yes;"> </span>The knowledge
needed to make the decision includes:</p>
<p class="MsoNoSpacing" style="text-align: left;"></p><ul style="text-align: left;"><li>Defining business requirements, which requires
business users who understand their needs.<span style="mso-spacerun: yes;">
</span></li><li>Understanding existing systems, so you can
identify gaps that must be filled to meet business requirements</li><li>Understanding the organization’s capabilities
for filling the gaps with either a Composable or packaged solution.<span style="mso-spacerun: yes;"> </span>These capabilities relate to technical
resources including staff skills and budgets.<span style="mso-spacerun: yes;">
</span>I believe that assembling a Composable solution requires more extensive
technical resources than buying a packaged CDP, although some Composable
advocates might disagree.</li><li>
Estimating the costs of delivering a Composable
solution and a packaged solution so you can compare them.</li></ul>
<p class="MsoNoSpacing" style="text-align: left;">The Self-Assessment tool addresses only the first two of
those items: it asks users what they need and what their current systems can
provide.<span style="mso-spacerun: yes;"> </span>So, even if the users could
answer all the questions, it wouldn’t provide all the information needed to
make a sound decision.<span style="mso-spacerun: yes;"> </span>Again, this isn’t
exactly a flaw, since those are two important types of information.<span style="mso-spacerun: yes;"> </span>But it is a limitation.</p>
<p class="MsoNoSpacing" style="text-align: left;"></p>
<p class="MsoNoSpacing" style="text-align: left;">Only after all the information listed above has been
gathered can the company address the second question of whether Composable or
packaged is its best choice.<span style="mso-spacerun: yes;"> </span>Answering
this question depends on the combination of capabilities and costs.<span style="mso-spacerun: yes;"> </span>That is, for Composable to be a viable
option, the company needs to have adequate technical resources to assemble a
Composable solution, and for Composable to be the best option, it has to offer
the best combination of cost and value.<span style="mso-spacerun: yes;"> </span></p><p class="MsoNoSpacing" style="text-align: left;">(If we assume that either option delivers the same value, then the only
difference is cost.<span style="mso-spacerun: yes;"> </span>In theory, every
system that meets same requirements will deliver the same value.<span style="mso-spacerun: yes;"> </span>But in the real world, there will be
differences in the quality of the resulting systems.<span style="mso-spacerun: yes;">
</span>These will depend on the specific tools that are chosen, not simply on
whether the solution is Composable or packaged.<span style="mso-spacerun: yes;">
</span>This means that buyers must evaluate specific alternatives for either
approach.)<span style="mso-spacerun: yes;"> </span></p>
<p class="MsoNoSpacing" style="text-align: left;"></p>
<p class="MsoNoSpacing" style="text-align: left;">Circling back to the original question of how the
Institute can help users who are considering a Composable CDP, it’s clear that
few people will have all the information they need available when they start the process.<span style="mso-spacerun: yes;"> </span>So the best we can do is to
provide a framework that identifies the four kinds of information to collect and,
perhaps, provides a place to store it over time.<span style="mso-spacerun: yes;"> </span><span style="mso-spacerun: yes;"> </span>That
could be as simple as a spreadsheet, which isn’t very exciting from a technical
standpoint.<span style="mso-spacerun: yes;"> </span>But if it’s what our members
need the most, then that’s what we’ll deliver.</p><p class="MsoNoSpacing" style="text-align: left;"><b>Addendum: </b>Taking things a step further, here's what the tables described above could look like. It would be interesting to collect separate answers from business users and IT/data teams, who very likely would disagree in many places. I could easily convert these tables into an online format and have the system do some light analysis, including a comparison of answers from business users vs IT/data teams. But we'd still run into the same problem, that it takes time assemble answers. So this pushes me back to a spreadsheet that people can fill out at their leisure. You can download that <a href="https://www.cdpinstitute.org/wp-content/uploads/2024/02/Composable-vs-Packaged-Worksheets.xlsx" target="_blank">here</a>.<br /></p>
<table border="1" cellpadding="0" cellspacing="0">
<tbody>
<tr>
<td colspan="6" valign="top" width="623">
<p>
1 & 2: Requirements & Existing Systems
</p>
</td>
</tr>
<tr>
<td valign="top" width="176">
<p>
Data Sources
</p>
</td>
<td valign="top" width="93">
<p>
Not needed
</p>
</td>
<td valign="top" width="89">
<p>
Needed and Fully Available
</p>
</td>
<td valign="top" width="89">
<p>
Needed and Partly Available
</p>
</td>
<td valign="top" width="89">
<p>
Needed and Not Available
</p>
</td>
<td valign="top" width="89">
<p>
Don’t Know
</p>
</td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Website
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- CRM
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Data warehouse/
</p>
<p>
- data lake
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Email
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Third party data
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- eCommerce
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Web advertising
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Social media advertising
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Point of Sale
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<br /></td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
Profile Building Functions
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Capture data
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Ingest data
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Prepare data
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Store data
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Link data
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Build profiles
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Share profiles
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Integrate profiles
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Segment profiles
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<br /></td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
Activation Functions
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Audience selection
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Predictive analytics
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Campaign definition
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Real time interactions
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
<tr>
<td valign="top" width="176">
<p>
- Cross-channel orchestration
</p>
</td>
<td valign="top" width="93">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
<td valign="top" width="89">
<br /></td>
</tr>
</tbody>
</table>
<p> </p>
<table border="1" cellpadding="0" cellspacing="0">
<tbody>
<tr>
<td colspan="2" valign="top" width="455">
<p>
3. Resources
</p>
</td>
</tr>
<tr>
<td valign="top" width="228">
<br /></td>
<td valign="top" width="228">
<p>
Rating (1=low, 5=high)
</p>
</td>
</tr>
<tr>
<td valign="top" width="228">
<p>
- Customer data infrastructure
</p>
</td>
<td valign="top" width="228">
<br /></td>
</tr>
<tr>
<td valign="top" width="228">
<p>
- IT/Data team: customer data experience
</p>
</td>
<td valign="top" width="228">
<br /></td>
</tr>
<tr>
<td valign="top" width="228">
<p>
- IT/Data team availability for this project
</p>
</td>
<td valign="top" width="228">
<br /></td>
</tr>
<tr>
<td valign="top" width="228">
<p>
- Business users: customer data experience
</p>
</td>
<td valign="top" width="228">
<br /></td>
</tr>
<tr>
<td valign="top" width="228">
<p>
- Business user availability for this project
</p>
</td>
<td valign="top" width="228">
<br /></td>
</tr>
<tr>
<td valign="top" width="228">
<p>
- Training resources
</p>
</td>
<td valign="top" width="228">
<br /></td>
</tr>
<tr>
<td valign="top" width="228">
<p>
- Measurement resources
</p>
</td>
<td valign="top" width="228">
<br /></td>
</tr>
<tr>
<td valign="top" width="228">
<p>
- Budget for this project
</p>
</td>
<td valign="top" width="228">
<br /></td>
</tr>
<tr>
<td valign="top" width="228">
<p>
- Management Support
</p>
</td>
<td valign="top" width="228">
<br /></td>
</tr>
</tbody>
</table>
<p> </p>
<table border="1" cellpadding="0" cellspacing="0">
<tbody>
<tr>
<td colspan="3" valign="top" width="561">
<p>
4. Cost
</p>
</td>
</tr>
<tr>
<td valign="top" width="326">
<p>
Hard costs (enter dollar amounts):
</p>
</td>
<td valign="top" width="117">
<p>
Composable
</p>
</td>
<td valign="top" width="117">
<p>
Packaged
</p>
</td>
</tr>
<tr>
<td valign="top" width="326">
<p>
- Licenses/Vendor Fees
</p>
</td>
<td valign="top" width="117">
<br /></td>
<td valign="top" width="117">
<br /></td>
</tr>
<tr>
<td valign="top" width="326">
<p>
- Labor/Development & Integration Costs
</p>
</td>
<td valign="top" width="117">
<br /></td>
<td valign="top" width="117">
<br /></td>
</tr>
<tr>
<td valign="top" width="326">
<p>
- Operations (hardware, software, on-going
maintenance & upgrades)
</p>
</td>
<td valign="top" width="117">
<br /></td>
<td valign="top" width="117">
<br /></td>
</tr>
<tr>
<td valign="top" width="326">
<p>
Other Considerations: (1=disadvantage, 2=same, 3=advantage)
</p>
</td>
<td valign="top" width="117">
<br /></td>
<td valign="top" width="117">
<br /></td>
</tr>
<tr>
<td valign="top" width="326">
<p>
- Time to Value
</p>
</td>
<td valign="top" width="117">
<br /></td>
<td valign="top" width="117">
<br /></td>
</tr>
<tr>
<td valign="top" width="326">
<p>
- Delivery Risk (time, cost overruns)
</p>
</td>
<td valign="top" width="117">
<br /></td>
<td valign="top" width="117">
<br /></td>
</tr>
<tr>
<td valign="top" width="326">
<p>
- Performance Risk (scalability, performance)
</p>
</td>
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<p>
- Roadmap (control over features)
</p>
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- Security/privacy (control over data)
</p>
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- Quality (best components, competitive
advantage, vendor, usability/consistent interface)
</p>
</td>
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<br /></td>
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-
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<p></p>David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-53085510839178379422023-10-29T18:22:00.002-04:002023-10-29T18:22:31.145-04:00Does CDP Need a New Definition?<p>
The earliest Customer Data Platform systems were introduced before 2010; the term CDP was coined to describe this emerging class in <a href="https://customerexperiencematrix.blogspot.com/2013/04/ive-discovered-new-class-of-system.html" target="_blank">2013</a>. My definition had changed very little when we launched the CDP Institute in 2016, and has been the same ever since: “packaged software that builds a unified, persistent customer database accessible to other systems”. The Institute added the <a href="https://www.cdpinstitute.org/learning-center/what-is-a-realcdp/" target="_blank">RealCDP checklist</a>* in 2019 to attach more specifics to the definition in the hopes of helping buyers ensure a system that called itself a CDP could actually support the use cases they expected a CDP to support. By then, industry analysts were beginning to offer their own definitions which, while worded differently, were broadly consistent with the Institute definition. Even the major marketing suite vendors, who initially argued a separate (“persistent”) database wasn’t necessary, eventually discarded that position and introduced products that matched our criteria.
</p><p>A successful concept like CDP quickly takes on a life of its own. It soon became apparent that many people were using CDP in a much looser sense to mean any system that built and shared customer profiles. This extended past packaged software to include custom-built systems and included systems whose scope was more limited than a true CDP. This expansion skewed some survey resuls but otherwise seemed relatively harmless; in any case, resistance seemed both pedantic and futile. What really mattered was these systems still gave CDP users access to the unified profiles.
</p><p>
Unfortunately, the evolution of the term didn’t stop there. As CDP became popular, many vendors adopted the label whether or not they actually met even the looser definition. At the same time, legitimate CDP vendors offered additional capabilities to analyze and deploy the data in the profiles. The resulting confusion ultimately led some vendors to avoid the CDP label entirely because it no longer provided a useful way to differentiate their products. Today, vendors seeking the latest label are more likely to call themselves digital experience managers than a CDP, even if their products meet the CDP requirements.
</p><p>
But the greatest challenge to the utility of the CDP label arose in the past few years when a number of vendors chose to claim that the core feature of the CDP – a dedicated database built by importing data from other systems, a.k.a. “persistence” – could be abandoned while still calling the result a CDP. Their argument was the customer profiles could reside in a general purpose data warehouse, which most companies already had in place. </p><p>The claim gained some plausibility from the fact that modern cloud data warehouse technologies, such as Snowflake, Google Big Query, and AWS Redshift, are in fact used by some conventional CDP vendors. The problem was they implicitly assumed that every company’s data warehouse had organized the data into useable customer profiles. In fact, very few data warehouses perform the specific tasks, most notably identity resolution, customer-level aggregation, and real-time response, needed to support CDP use cases. While it’s technically possible to add those features to an existing data warehouse, it’s usually a major project that often costs more, takes longer, and delivers less useful results than installing a separate, conventional CDP. (As always, the details depend on the situation.)
</p><p>
One positive result of the interest in warehouse-based profiles has been the decision of some CDP vendors to break their systems into modules that let users buy the data preparation functions separately from the rest of the CDP. This lets companies that want a warehouse-based system to still benefit from the mature capabilities those CDP vendors have developed over many years. These vendors have also often added the ability to combine data from a warehouse or other external system with data stored in a conventional, persistent CDP database, without actually loading that external data into the CDP. This gains some advantages of the warehouse-based approach, such as reduced data movement and storage costs, while retaining the benefits of the persistent CDP, such as greater control and flexibility.
</p><p>
You’ll note that all these changes affect the input side of the CDP data flow. It was once a very simple process: data from other systems was copied into the CDP, where it was formatted into profiles and shared with other systems. Now, the input process may be some mix of copying data into the CDP, reading fully-formed profiles from a warehouse, or combining internal and external data. By contrast, the delivery side of the process has remained the same: the CDP shares its profiles with other systems. </p><p>In some cases, this has led to a subtle shift in perception of the CDP’s purpose: from a system that builds customer profiles, to a system that delivers those profiles to other systems. In this view, the core function of the CDP is to convert general purpose profiles into the specific formats needed by analytical, orchestration, and delivery systems (which we can call “activation systems” if you’ll trade some jargon for simplicity). This may still involve some data processing, such as advance calculation of model scores and aggregates to make them available in real time, and new data structures to hold the results of that processing. So there’s more happening here than a simple data transfer or API call.
</p><p>
Some people carry this shift even further and argue the CDP should really be defined as an activation system that reads profiles from somewhere else. Since three-quarters of the CDPs we track actually do have at least some activation capabilities, this isn’t quite as crazy as it may sound.
</p><p>
All that said, I’m not yet ready to redefine CDP. “Packaged software” and “persistent customer database” are meaningful terms that distinguish one configuration from other approaches (“custom software” and “external profiles”) that can also make profiles available to other systems. More important, the packaged/persistent configuration has significant cost and execution advantages over the custom/external alternative. So it’s important to avoid blurring the distinction between the two approaches. </p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgvDbynvNB9bsYLeudLhsL9Ejs5ay1XJ9nAMxSAcfknHLQRZ2Oy21UvQ-0j1YaXjAwnqA73uk-pJWoJ63umJBqq0pvWM97GhSDqVS60FoAaxmziDfmFTWqUEgQJT2TXpmCmwjJiPXaw0gsg0b8tO3lBs5oA0PqHpfR2HuQoDH4BxkVkVjxb1Anz/s1484/Packaged%20CDP.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="908" data-original-width="1484" height="245" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgvDbynvNB9bsYLeudLhsL9Ejs5ay1XJ9nAMxSAcfknHLQRZ2Oy21UvQ-0j1YaXjAwnqA73uk-pJWoJ63umJBqq0pvWM97GhSDqVS60FoAaxmziDfmFTWqUEgQJT2TXpmCmwjJiPXaw0gsg0b8tO3lBs5oA0PqHpfR2HuQoDH4BxkVkVjxb1Anz/w400-h245/Packaged%20CDP.png" width="400" /></a></div><p></p><p> At most, I’d offer retronyms that distinguish the “packaged CDP” (packaged/persistent) from the “warehouse CDP” (custom/external). By definition, the “warehouse CDP” doesn’t build its own profiles, so the term seems to ignore the fundamental function of the CDP. You might call that oxymoronic, if not just plain moronic. But if we slightly redefine the core function of the CDP to be delivery of profiles rather than creating them, we can overcome that objection and, perhaps, give the market terms it intuitively understands.
</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjIA7hvZ01W4hk5_EpRFHrUYxnCqz2lOmDOIV8wkXvLV_H9Q-gX7T-uorFkh7SqLxOgTybwttpDVsS-0Khy7BcWQmGowml9YB8mpppTVHcmLKwxjLoGIPZ38DeFY_z6aJB8IHLwK0_YK5rXlaa7tyo_n9Tv9IGIad6XVLRd3n67KJZpWB551eiD/s1484/warehouse%20CDP.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="908" data-original-width="1484" height="245" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjIA7hvZ01W4hk5_EpRFHrUYxnCqz2lOmDOIV8wkXvLV_H9Q-gX7T-uorFkh7SqLxOgTybwttpDVsS-0Khy7BcWQmGowml9YB8mpppTVHcmLKwxjLoGIPZ38DeFY_z6aJB8IHLwK0_YK5rXlaa7tyo_n9Tv9IGIad6XVLRd3n67KJZpWB551eiD/w400-h245/warehouse%20CDP.png" width="400" /></a></div><p></p><p>
You’ll also note the shift from profile creation to delivery puts the emphasis on the delivery side of the CDP flow, which we’ve noted has been the most stable and applies to both the packaged and warehouse approaches. This lets us join the trend to redefining CDP as a profile sharing system.
</p><p>
In short, the key distinction is whether the primary customer profiles are built and stored in the company data warehouse or in a separate CDP database. It’s worth maintaining that distinction because one approach relies on the company’s technical staff to assemble the functions needed to build and store the profiles, while the other relies on a packaged CDP to provide those functions. There are variations within each theme, including whether the warehouse uses modules from a CDP vendor to assemble its profiles or to help deliver them, whether real-time data is posted to the warehouse or held separately, and whether the CDP enriches its profiles with external data without importing that data. These are important from a practical standpoint, but do not affect the fundamental architecture of the system. Focusing first on where the profiles reside should help buyers understand the most important choice they have to make. This choice will in turn determine the other decisions they have to consider. </p><p> </p><p>_________________________________________________________</p><p>* ingest data from all sources, retain all details, keep the data as long
as desired, build unified profiles, share the profiles with any other
system, and enable real-time event-triggers and profile access. <br /></p>David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-56647600946602857322023-09-18T14:33:00.011-04:002023-09-18T15:49:14.421-04:00Do Self-Service Systems Really Lead to Better Results? Our Member Survey Offers Surprising Answers to Industry Questions<p>
The CDP Institute just published its annual Member Survey, which is always a treasure chest of interesting data. I’ve already published my primary analysis on the Institute site (you can download it <a href="https://www.cdpinstitute.org/resources/cdp-institute-2023-member-survey-success-in-an-era-of-limits/" target="_blank">here</a>) but wanted to call out a number of findings that either contradict or confirm martech industry conventional wisdom. After all, nothing’s more fun than tweaking the nose of authority.</p><p><b>
Data unification is growing: false.</b> Most of us have a deep-rooted confidence in progress, at least when it comes to technology (human nature is another matter). The need for unified customer data has now been so widely understood for so long that we just naturally assume that more companies will have developed it. That has been true since the survey began in 2017 through last year’s survey: both CDP deployment and presence of a unified customer database have increased steadily. But both measures fell in the current survey. It’s hard to imagine companies have actually abandoned their unified databases, but even if growth has only slowed, that would be a big surprise.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiRGeEncIrPpjnjHzj4zmFrA8AVoLaHHOe4-Kfq5OczRXOQEoyteAUC1Wb6oxMnRUBBCwwiEcW6BJm1NuWN-7S60dHt5cai3l6TT828ptjz_qePv-t0evjU4Cba33hMJGkrZr5xPsIhy-AzZwCVe-BvM7SHw6tpv0vzuPiEs1qPuCwlAfkAbrTR/s978/blog%201.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="785" data-original-width="978" height="514" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiRGeEncIrPpjnjHzj4zmFrA8AVoLaHHOe4-Kfq5OczRXOQEoyteAUC1Wb6oxMnRUBBCwwiEcW6BJm1NuWN-7S60dHt5cai3l6TT828ptjz_qePv-t0evjU4Cba33hMJGkrZr5xPsIhy-AzZwCVe-BvM7SHw6tpv0vzuPiEs1qPuCwlAfkAbrTR/w640-h514/blog%201.png" width="640" /></a></div><p> <br /></p><p><b>
Budget pressures are slowing industry growth: true.</b> Some industry vendors report business is booming, but most will admit buyers are taking longer to make decisions. Sure enough, the fraction of vendors who reported growth in CDP investment is down from last year’s survey, both for the past year and the current year. </p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhaAQZzLBMJ9d83eNcnJb17ImO_9u-VRqK6EFYqonF_yBBAz1afsWjL05VZF1H5_dOOJ2gK_Q4PbCuovRrFpangwcnS-PnVoOYSe9uYXYjKv7Fr-4jbJBXmE4kK2zkhR3sKNYANQTBQ_cp-IN-h866rPx--XopWMA33TIdZX86xkoA-qf6iKimM/s970/blog%202.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="390" data-original-width="970" height="258" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhaAQZzLBMJ9d83eNcnJb17ImO_9u-VRqK6EFYqonF_yBBAz1afsWjL05VZF1H5_dOOJ2gK_Q4PbCuovRrFpangwcnS-PnVoOYSe9uYXYjKv7Fr-4jbJBXmE4kK2zkhR3sKNYANQTBQ_cp-IN-h866rPx--XopWMA33TIdZX86xkoA-qf6iKimM/w640-h258/blog%202.png" width="640" /></a></div><p> <br /></p><p>
Budgets may not be the only reason for slower growth, but the budget pressures rose more quickly than any other obstacle (from 20% to 29%). Cooperation, which can also be a symptom of budget pressures, grew the second-fastest (36% to 43%). </p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhQC0DUu_WCG1cVCOBmcZm3dXpzSCzdGE2jnOsj5ge7EfByE7AkN62dLoJiwD5tTLzJ9Ifuw1k8uRmPdAO0FrkRR6IMgAThswEcML5gJex9uNAK_R_jbSF7deNjmDlKVCd2dXZp9l7r_sEqPmeefz7J_1-F3WnmO7sAgaBOJy-XT0OS5z8qx40m/s966/blog%203a.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="401" data-original-width="966" height="266" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhQC0DUu_WCG1cVCOBmcZm3dXpzSCzdGE2jnOsj5ge7EfByE7AkN62dLoJiwD5tTLzJ9Ifuw1k8uRmPdAO0FrkRR6IMgAThswEcML5gJex9uNAK_R_jbSF7deNjmDlKVCd2dXZp9l7r_sEqPmeefz7J_1-F3WnmO7sAgaBOJy-XT0OS5z8qx40m/w640-h266/blog%203a.png" width="640" /></a></div><p><b> </b></p><p><b>Budget-pressed buyers are making smarter decisions: false.</b> I can’t point to anyone who has made this exact claim, but think it’s implicit in reports that companies have more martech than they need and hope to simplify their stacks in the future. </p><p>The survey does show that budget pressures are changing martech selection methods: more are selecting on cost (up from 43% to 54% for operating cost and 42% to 51% for initial cost), while selection on feature sophistication and breadth have fallen the most (26% to 15% and 41% to 24%).</p><p>Unfortunately, our surveys have consistently found that selection on cost correlates with low satisfaction with martech investments, while selection on features correlates with high satisfaction. So it seems that a short-term focus on cost is likely to cause long-term problems with martech results.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhExyQtlL5owcDNvnpvMpFj4hnT44tZR1OtVWlriqnTNhuqKEB56G9My7RygqEjvoyoAFuVJArqU-n0Iu8eTKbDQqwS66LATLnoys7HePAdFBmCFqpn4MXr3V8AlhdPPL3t3u1qct378nLObGkA7tha1luRwfZ0tfZfNTk-qxC42i5Wct1oVj1Y/s975/blog%205.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="401" data-original-width="975" height="264" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhExyQtlL5owcDNvnpvMpFj4hnT44tZR1OtVWlriqnTNhuqKEB56G9My7RygqEjvoyoAFuVJArqU-n0Iu8eTKbDQqwS66LATLnoys7HePAdFBmCFqpn4MXr3V8AlhdPPL3t3u1qct378nLObGkA7tha1luRwfZ0tfZfNTk-qxC42i5Wct1oVj1Y/w640-h264/blog%205.png" width="640" /></a></div><p> <b> </b></p><p><b>Martech departments are growing: true.</b> The fraction of survey respondents who said they work in a martech department has more than doubled since the last survey, which was the first that listed martech as an option. It’s unlikely that the number of people working in martech has actually doubled in the past year, but it does seem reasonable to believe that some meaningful fraction of employees have been moved from marketing or IT into a dedicated martech department.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjzUtF1EcKlNlAxZ43XSKq_FguT4U0lSesVOd_YGrYF81xaL1LWJaYuep0FGDDfe8cVf69Cy0tUP6Nuo1lHMkDr1GqwSXQLHAn5WdhWesYIAgIVOM2sgHlwO3EONcb4mbthNl-V34xOqq0Lom-WOQw5fLmeSomxS__F4ctNdJaxCwOc3Gi7ek6a/s974/blog%206.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="599" data-original-width="974" height="394" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjzUtF1EcKlNlAxZ43XSKq_FguT4U0lSesVOd_YGrYF81xaL1LWJaYuep0FGDDfe8cVf69Cy0tUP6Nuo1lHMkDr1GqwSXQLHAn5WdhWesYIAgIVOM2sgHlwO3EONcb4mbthNl-V34xOqq0Lom-WOQw5fLmeSomxS__F4ctNdJaxCwOc3Gi7ek6a/w640-h394/blog%206.png" width="640" /></a></div><p> <b>IT staff is playing a larger role in martech decisions: true.</b> Despite the growth in respondents who work in martech departments, the current survey showed a small increase in the fraction of respondents who reported that that corporate IT manages their marketing technology (28% to 30%) and sharp declines in the fraction reporting that a martech team was in charge (43% to 32%) or each department runs its own martech (27% to 18%). This may be another cost-saving measure or it may reflect the growing importance attached to customer data (and martech in general) throughout the enterprise.</p><p>The bad news is that IT responsibility also correlates with lower martech satisfaction. Bear in mind that survey respondents are themselves mostly martech and marketing people, who are generally happiest when their own team is in charge. IT people probably give a different answer but are a small fraction of the survey respondents.<br /></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgpoABG1WJXASKvOH2sViRjTK19e2PBpYOkRh0FP5J0nSfsOkFPNkoTs3faqhRFGimMidFt1nwFLh9UTjAL-LnPfkYhmNTRKtw8pSWhYfHeNdr5kmCKYAN39k84Co5qLVDGuvn5fI75ddNTAizvAl5xL1u7Ebky4eR8mTW2LARp2Ly7uXtfhzAT/s986/blog%207.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="408" data-original-width="986" height="264" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgpoABG1WJXASKvOH2sViRjTK19e2PBpYOkRh0FP5J0nSfsOkFPNkoTs3faqhRFGimMidFt1nwFLh9UTjAL-LnPfkYhmNTRKtw8pSWhYfHeNdr5kmCKYAN39k84Co5qLVDGuvn5fI75ddNTAizvAl5xL1u7Ebky4eR8mTW2LARp2Ly7uXtfhzAT/w640-h264/blog%207.png" width="640" /></a></div><p> <br /></p><p><b>
CDP projects are easy: false.</b> Past surveys have found that about 60% of deployed CDPs are reported as delivering value, while the remaining 40% are struggling. I have always suspected most of the 40% are new projects that will deliver value eventually. The success rate is much higher in the current survey (80%), possibly because the slowdown in deployment has meant there are fewer new projects. But I'd want to see similar results in one or two other surveys before accepting that as a trend.<br /></p><p>
A separate question, asking vendors about success rates, finds the fraction reporting that say almost all or the majority of projects are successful has grown from 48% to 54%. While this might suggest some improvement in success rates, the more important message is that a bare majority of vendors say most CDP projects succeed. Even when answers from CDP vendors are tabulated separately, just 68% say nearly all or a majority of projects are successful. Figures are lower for service providers (45%) and other respondents (15%). </p><p>It's important to realize this is no worse than success rates for other large system deployments. In fact, it's apparently better than average, since most studies put failure rates at 60% to 70%. (See <a href="https://zipdo.co/statistics/software-project-failure/" target="_blank">this page</a> for a compendium.) But these findings should dispel any notion that CDP deployments are easy. </p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjPnNv_ndrLWdj2N2N1dtsnU6KY-aGnkiQXsjD7zqk1icAhcDFH_eIoJONDLplH_qIoT1q0kWQbxBTIjXiMCOPLdciQCQa4IzDDgdwtQj3UEBlWcj4TjEodkzuKlJJQ5QgQ_jjLQMz1ZriivF6rfChoZYqN34aKuVVGGPaIVD97ZNQn8DwEQKrv/s976/blog%208a.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="407" data-original-width="976" height="266" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjPnNv_ndrLWdj2N2N1dtsnU6KY-aGnkiQXsjD7zqk1icAhcDFH_eIoJONDLplH_qIoT1q0kWQbxBTIjXiMCOPLdciQCQa4IzDDgdwtQj3UEBlWcj4TjEodkzuKlJJQ5QgQ_jjLQMz1ZriivF6rfChoZYqN34aKuVVGGPaIVD97ZNQn8DwEQKrv/w640-h266/blog%208a.png" width="640" /></a></div><p><br /><b>CDP projects fail because of technical complexity: false.</b> It's also important to recognize that CDP failure rates are not due to any inherent problem with CDP technology. As in previous surveys, by far the top reason for CDP project failure is organization. This has grown even more prominent in the current survey, while problems with poor requirements and CDP performance have fallen.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg95zF5XFUTg3ywgIcAsqNfDFJq8sMvdhMo6c4Ah5rITP9nHKOLjz16Uw1xu7prd7i4lcYMY-s-pQbFHhToer64pWuLv72WeHTXCqjudotucZaZ-ueymtgFgt1CIkUFFWIVwTHFdYIWbBDdx3Bkwgc7-nGukSnGiO4MnzRqGMchJEW-JvDgG9KC/s962/blog%2010.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="400" data-original-width="962" height="266" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg95zF5XFUTg3ywgIcAsqNfDFJq8sMvdhMo6c4Ah5rITP9nHKOLjz16Uw1xu7prd7i4lcYMY-s-pQbFHhToer64pWuLv72WeHTXCqjudotucZaZ-ueymtgFgt1CIkUFFWIVwTHFdYIWbBDdx3Bkwgc7-nGukSnGiO4MnzRqGMchJEW-JvDgG9KC/w640-h266/blog%2010.png" width="640" /></a></div><p>Comparing CDP status with satisfaction offers additional insight. The satisfaction measure reflects success with martech in general, not with CDP in particular. In other words, companies with a high score are "good at martech". So, while it's not surprising that satisfaction rates are high among companies with a successfully deployed CDP and low among those who have not, this does support the position that CDP success is based more on the skills of the organization than CDP technology in particular. </p><p> </p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEie5uGSKKtIUoGHy8s_XqnzEdiWtTrB1axPEF9J7xgYGjjXwJ2RxO9J6G0sn55nqkG2sMtU6Hwy0dLF0l0Ac8fOsC__uhA7OoHlHJ69CCVz4wdfYozyEW8JmOGmCb4ZgHYoVIA0ZChQ6_NegWtkn6WRpGQ_Q79eEdtinLFmlokUZHlgqWugQ4gW/s986/blog%2011b.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="404" data-original-width="986" height="262" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEie5uGSKKtIUoGHy8s_XqnzEdiWtTrB1axPEF9J7xgYGjjXwJ2RxO9J6G0sn55nqkG2sMtU6Hwy0dLF0l0Ac8fOsC__uhA7OoHlHJ69CCVz4wdfYozyEW8JmOGmCb4ZgHYoVIA0ZChQ6_NegWtkn6WRpGQ_Q79eEdtinLFmlokUZHlgqWugQ4gW/w640-h262/blog%2011b.png" width="640" /></a></div><br /><b><br /></b><p></p><p><b>Privacy is growing more important: true.</b> Last year’s survey showed a distressing decline in the priority given to data privacy regulations, with the share of firms making little effort to comply growing from 12% to 20%. That trend has now reversed, with the share of companies using privacy as a selling point increasing from 21% to 27%. </p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiLXwtw1WLJb5VrSdEKhH7srmj-aDIksZTBBZhT7aHSBMg03QFMQI7Y4vz3AFhfMW_UwFJQX8fsOPKxLMBnN1K1pfLMoqh_OyB-iMrsxMQM0xy5l6ry5EgEebk0kuwTj7pqgJHCWqB-nNTCMYhhSwcGhKukeSlMgbCRYf_jfGTaNJw4OVLnL3gD/s975/blog%2011.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="405" data-original-width="975" height="266" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiLXwtw1WLJb5VrSdEKhH7srmj-aDIksZTBBZhT7aHSBMg03QFMQI7Y4vz3AFhfMW_UwFJQX8fsOPKxLMBnN1K1pfLMoqh_OyB-iMrsxMQM0xy5l6ry5EgEebk0kuwTj7pqgJHCWqB-nNTCMYhhSwcGhKukeSlMgbCRYf_jfGTaNJw4OVLnL3gD/w640-h266/blog%2011.png" width="640" /></a></div><p>Privacy was also listed in the CDP benefits question for the first time. It was cited by 22% of respondents, ranking seventh of eleven items, and shows a below-average satisfaction score. This may indicate that most CDP users are looking elsewhere for their primary privacy management solution.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgM--T5xe7veG6GjXKrW2NO3ikdJ5LBSQ7fXlHETsswkrxhS0MKoOIckbUSCX0BQOp-pamWnX7MeeVhvNGLmUBjAaVam8wNvlWgA-_z_zsmBzOnuqZRTYpX9v3BGlW-o_w-D8VX0o4ewZlLvUp3qfCPURLM3V2CLa7pp-BvDC0BabokHpZOof8F/s974/blog%2012.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="405" data-original-width="974" height="266" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgM--T5xe7veG6GjXKrW2NO3ikdJ5LBSQ7fXlHETsswkrxhS0MKoOIckbUSCX0BQOp-pamWnX7MeeVhvNGLmUBjAaVam8wNvlWgA-_z_zsmBzOnuqZRTYpX9v3BGlW-o_w-D8VX0o4ewZlLvUp3qfCPURLM3V2CLa7pp-BvDC0BabokHpZOof8F/w640-h266/blog%2012.png" width="640" /></a></div><p> <b> </b></p><p><b>Self-service leads to success: false.</b> The martech management section of this year’s survey added a new question about whether companies seek systems that empower business users to execute tasks without technical assistance. The concept of “no-code” is directly relevant here, although we didn't use the term. This capability was by far the most common management technique, which wasn’t surprising: “empowering end-users” is a popular goal that saves money and makes users more effective. But it also correlated with a low satisfaction score, which was surprising indeed. Is it possible that self-service doesn’t save money or make users more effective after all?</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh72HjYoQvrq2t2Ma-A6oLPYblBTDQdITo_1Ckzu2hny4iWrD0zioTBXwjsJ9CxaAAUlVuLHJ8c_nYOYIgvqPGaCqS5O0qe4lJAOdY9csYU4jxVfmASqMwuJn-4fY77CBMX2suO6GwoMkBKYi5QVMfxz3pRMQBTSe1DrkQRakk9U9BXWTs445rH/s974/blog%2013.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="405" data-original-width="974" height="266" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh72HjYoQvrq2t2Ma-A6oLPYblBTDQdITo_1Ckzu2hny4iWrD0zioTBXwjsJ9CxaAAUlVuLHJ8c_nYOYIgvqPGaCqS5O0qe4lJAOdY9csYU4jxVfmASqMwuJn-4fY77CBMX2suO6GwoMkBKYi5QVMfxz3pRMQBTSe1DrkQRakk9U9BXWTs445rH/w640-h266/blog%2013.png" width="640" /></a></div><p> </p><p>Answers to another survey question shed a bit more light. Among the options listed for CDP capabilities were self-service data extracts and self-service predictive models. Self service extracts were a common requirement (41%) correlated with a roughly average satisfaction rating, while self-service models were an uncommon requirement (8%) correlated with a very low satisfaction rating. My interpretation is that self-service extracts are a simple task that’s well understood by business users, so companies with well-run martech operations provide that capability. By contrast, self-service predictive models are a complicated task that few users are equipped to handle on their own, so they are prioritized largely by companies with a poor understanding of what makes for martech success. The larger message is that self-service should be deployed only when users are ready for it – and pushing beyond those limits can cause problems despite the apparent savings in cost and time.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhR67U1d73XjolpgNUforYgEaGKmOLUh_Dc5UT2dFBt5mcgE8srCfZiytKBd6JgIDZWMaSRXJpLN4q5u_qnNTOWz73IDd2bLFo1A5B7rSyilu6P7ydfTUIKrNW7PB5AS0V37_-Y-Oyp4L_2sNBWXlLugM2LR_L9uBPsMECra_SqkMOFUCnXS55m/s975/blog%2014a.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="399" data-original-width="975" height="262" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhR67U1d73XjolpgNUforYgEaGKmOLUh_Dc5UT2dFBt5mcgE8srCfZiytKBd6JgIDZWMaSRXJpLN4q5u_qnNTOWz73IDd2bLFo1A5B7rSyilu6P7ydfTUIKrNW7PB5AS0V37_-Y-Oyp4L_2sNBWXlLugM2LR_L9uBPsMECra_SqkMOFUCnXS55m/w640-h262/blog%2014a.png" width="640" /></a></div><p><b> </b></p><p><b>Real-time processing is a high priority for most users: mixed.</b> Real-time processing is commonly cited as an important CDP capability. In fact, the CDP Institute’s RealCDP requirements include real-time access to profiles and real-time event triggers. Again looking at the capabilities question, we see that real-time profiles are indeed a common requirement (42%), while real-time recommendations are much less common (15%) but correlate with very high satisfaction. The lesson here is that there are different types of real-time processing, and users consider some more important than others. Discussions about real-time should include similar nuance.</p><p><b>
CDP must load data from all sources and retain full details: mixed.</b> Both of these items are on the RealCDP requirements list. But loading data from all sources is the highest ranked capability (78%) and correlates with roughly average satisfaction, while loading full detail is cited by just 14% of respondents and correlates with much lower satisfaction. As with real-time and self-service, I take these answers to show that users have a mature understanding of what they do and don’t need from the CDP. Loading all data sources is a core CDP promise and goes back to the problem CDP was originally designed to solve: getting access to all customer data. Storing full detail is not needed in most situations. In practice, users choose which data elements are worth the cost. That said, I still believe that users want the option to store any particular details they need.</p><p><b>
CDP users want to access their data warehouse directly: false.</b> This year’s survey added a capabilities question about reading data from an external source without loading it into the CDP. This is a hot topic in the industry, both as a way of supplementing a traditional CDP (which maintains its own data store) and as a way of building a CDP-equivalent system that relies only on data in the enterprise data warehouse. Only a small fraction of respondents (14%) were interested in this and that group reported exceptionally low satisfaction levels. I interpret this to mean that, despite all the marketplace noise, there is actually very little interest among knowledgeable users in building a CDP that relies primarily on external data. </p><p><b>
Summary</b></p><p>
This report contains more bad news about industry growth and budget pressures than I like to see, but the tech sector’s troubles are well known and it’s not surprising that CDP projects should suffer with everyone else. I’m especially reluctant to highlight the data about success rates, because I know it will be taken out of context by vendors eager to promote alternatives to CDPs. I should stress again that the main obstacles to CDP success are organizational, not technical, and that success rates reported here are consistent with tech project success rates in general. Bottom line: CDPs are major projects that don’t always go smoothly but are not more failure-prone than any other major IT effort.</p><p>
What worries me more than bad news is the hints that companies are making poor decisions. Prioritizing cost over requirements will surely lead to more companies purchasing unsuitable products. Putting IT in charge of martech will almost surely lead to unhappy martech users. While budget issues are unavoidable, poor management decisions are unforced errors. Your company should work hard to avoid them.</p><p>
All that said, the most interesting results are the ones that challenge conventional wisdom. Do self-service systems really lead to bad results? Is the importance of real-time overstated? Is there really so little interest in reading data directly from a warehouse? These are headline topics in martech today, and are often accepted as truth with little discussion. The questions are worth asking because the reality is almost always more complicated than the simple answers. When is self-service useful and when is it over-used? What kinds of real-time processes are really important? How is external data best integrated with a conventional CDP? Answering those questions will help companies make better decisions and ultimately lead to greater martech success.</p><p>
</p>David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-60580575734513568822023-09-14T01:22:00.000-04:002023-09-14T01:22:03.270-04:00Unleashing the Power of Customer Data Platforms (CDPs) and AI: A Game-Changer for Modern Marketing<p>For some unknown reason, my last three presentations all started as headlines (two created by someone else) which I then then wrote a speech to match. This isn’t my usual way of working. It does add a little suspense to the writing process – can I develop an argument to match the title? It also dawns on me that this is the way generative AI works: start with a prompt and create a supporting text. That’s an unsettling thought: are humans now imitating AI instead of the other way around? Or have I already been replaced by an AI but just don’t realize it? How would I even know?</p><p>
The latest in this series of prompt-generated presentations started when I noticed that the title shown in a conference agenda didn’t match the speech I had prepared. When I pointed this out, the conference organizers said I could give any speech I want, but the problem was, I really liked their title: “Unleashing the Power of Customer Data Platforms (CDPs) and AI: A Game-Changer for Modern Marketing”. </p><p>The idea of “unleashing” a CDP to run wild and exercise all its powers, is not something I get to talk about very often, since most of my presentations are about practical topics like defining a CDP, selecting one, or deploying one. And I love how “and AI” is casually tucked into the title like an afterthought: “there’s this little thing called AI, perhaps you’ve heard of it?”</p><p>
And how about “game changer for modern marketing”? That’s an amazing promise to make: not just will you learn the true meaning of modern marketing, but you'll find how to change its very nature so it’s a game you can win. Who wouldn’t want to learn that?</p><p>
This was definitely a speech I wanted to hear. The only problem was, the only way for that to happen was for me to write it. So I did. Here’s a lightly modified version.</p><p>
Let’s start with the goal: winning the modern marketing game. The object of the game is quite simple: deliver the optimal experience for each customer across all interactions. And, when I say all interactions, I mean all interactions: not just marketing interactions, but every interaction from initial advertising impressions through product purchase, use, service, and disposal. I also mean every touch point, from the Internet and email through call centers, repair services, and the product itself.</p><p>
That’s a broad definition, and you will immediately see the first challenge: all the departments outside of marketing may not want to let marketing take charge of their customer interactions. Nor is your company’s senior management necessarily interested in giving marketing so much authority. So marketing’s role in many interactions may be more of an advisor. The best you can hope for is that marketing is given a seat at the table when those departments set their policies and set up their systems. This requires a cooperative rather than a controlling attitude among marketers. </p><p>
The second challenge to winning at marketing is the fragmented nature of data and systems. Most marketing departments have a dozen or more systems with customer data; at global organizations, the number can reach into the hundreds. Expanding the scope to include non-marketing systems that interact with customers adds still more sources such as contact centers and support websites. Again, marketing will rarely control these. At best, they may have an option to insert marketing recommendations directly into the customer experience, such as suggesting next best actions to call center agents. </p><p>
The third challenge is optimization itself. It’s not always clear what action will result in the best long-term results. A proper answer requires capturing data on how customers are treated and how they later behaved as a result. Some of this will come from non-marketing systems, such as call center records of actions taken and accounting system records of purchases. Again, those systems’ owners may not be eager to share their data, although it’s harder for them to argue against sharing historical information than against sharing control over actual customer interactions.</p><p>
But the challenge of optimization extends beyond data access. Really understanding the drivers of customer behavior requires deep analysis and no small amount of human insight. Some questions can be answered through formal experiments with test and control groups. But the most important questions often can’t be defined in such narrow terms. Even defining the options to test, such as new offers or marketing messages, takes creative thought beyond what analysis alone can reveal.</p><p>
And even if you could find the optimal treatment in each situation, the playing field itself keeps shifting. New products, offer structures, and channels change what treatments are available. New systems change the data that be captured for analysis. New tools change the costs of actions such as creating customer-specific content. These all change the optimization equation: actions that once required expensive human labor can now be done cheaply with automation; fluctuating product costs and prices change the value of different actions; evolving customer attitudes towards privacy and service change the appeal of different offers. The optimal customer experience is a moving target, if not an entirely mythical one. </p><p>
None of this is news, or really even new: marketing has always been hard. The question is how “unleashing” the power of CDPs and AI makes a difference.</p><p>
Let’s start with a framework. If you think of modern marketing as a game, then the players have three types of equipment: data systems to collect and organize customer information; decision systems to select customer experiences; and delivery systems to execute those experiences. It’s quite clear that the CDP maps into the data layer and AI maps into the decision layer. This raises the question of what maps into the delivery layer. We’ll return to that later.</p><p>
First, we have to answer the question: Why would CDP and AI be game changers? To understand that, you have to imagine, or remember, life before CDP and AI. Probably the best word for that is chaos. There are dozens – often hundreds -- of data sources on the data layer, and dozens more systems making choices on the decision layer. The reason there are so many decision systems is that each channel usually makes its own choices. Even when channels share centralized decision systems, those are often specialized services that deal with one problem, whether it’s product recommendations or churn predictions or audience segmentation.</p><p>
CDP and AI promise to end the chaos by consolidating all those systems into one customer data source and one decision engine. Each of these would be a huge improvement by itself:</p><ul style="text-align: left;"><li>
the CDP makes complete, consistent customer data available to all decision systems, and</li><li>
AI enables a single decision engine to coordinate and optimize custom experiences for each individual.</li></ul><p> Yes, we’ve had journey orchestration engines and experience orchestration engines available for quite some time, but those work at the segment level. What’s unique about AI is not simply that it can power unified decisions, but that each decision can each be tailored to the unique individual. </p><p>But we’re talking about more than the advantages of CDP and AI by themselves. We’re talking about the combination, and what makes that a game-changer. The answer is you get a huge leap in what’s possible when that personalized AI decisioning is connected to a unified source of all customer information. </p><p>Remember, AI is only as good as the data you feed it. You won’t get anything near the full value of AI if it’s struggling with partial information, or if different bits of information are made available to different AI functions. Connecting the AI to the CDP solves that problem: once any new bit of information is loaded into the CDP, it’s immediately available to every AI service. This means the AI is always working with complete and up-to-date data, and it’s vastly easier to add new sources of customer data because they only have to be integrated once, into the CDP, to become available everywhere.</p><p>
To put it in more concrete terms, one unified decision system can coordinate and optimize individual-specific customer experiences across all touch points based on one unified set of customer data. </p><p>That is indeed a game-changer for modern marketing, and it only reaches its full potential if you “unleash” the CDP to consolidate all your customer data, and “unleash” the AI to make all of your experience decisions.</p><p>
That’s a lot of unleashing. I hope you find it exciting but you should also be a little bit scared. The question to ask is: How can I take advantage of this ‘game changing potential’ without risking everything on technology that is relatively new and, in the case of AI, largely unproven. Here’s what I would suggest:</p><p>
Let’s start with CDP. So far, I’ve been using the term without defining it. I hope you know that a CDP creates unified customer profiles that can be shared with any other system. No need to get into the technical details here. What’s important is that the CDP collects data from all your source systems, combines it to build complete customer profiles, and makes them available for any and every purpose.</p><p>Some people reading this will already have a CDP in place but most probably do not. So my first bit of advice is: Get one. It’s not such easy advice to follow: a CDP is a big project and there are dozens of vendors to choose from, so you have to work carefully to find a system that fits your needs and then you have to convince the rest of your organization that it’s worth the investment. I won't go into how to do that right now. But probably the most important pro tips are: base your selection on requirements that are directly tied to your business needs, and ensure you keep all stakeholders engaged throughout the entire selection process. If you do those two things, you can be pretty sure you’ll buy a CDP that’s useful and actually used.</p><p>
That said, there are some specific requirements you’ll want to consider that tie directly to ensuring your CDP will support your AI system. One is make sure you buy a system that can handle all data types, retain all details, and handle any data volume. This does NOT mean you should load in every scrap of customer-related data that you can find. That would be a huge waste. You’ll want to start with a core set of customer data that you clearly need, which will be data that your decision and delivery systems are already working with. This lets the CDP become their primary data source. Beyond that, load additional data as the demand arises and when you’re confident the value created is worth the added cost.</p><p>
The second requirement is to ensure your CDP can support real time access to its data. That’s another complicated topic because there are different kinds of real time processes. What you want as a minimum is the ability to read a single customer profile in real time, for example to support a personalization request. And you want your CDP to be able to respond in real time to events such as a dropped shopping cart. Your AI system will need both of those capabilities to make the best customer experience decisions. What’s not included in that list is updating the customer profiles in real time as new data arrives, or rebuilding the identity graph that connects data from different sources to the same customer. Some CDPs can do those things in real time but most cannot. Only some applications really need them.</p><p>
The third requirement relates to identity management. The CDP needs to know which identifiers, such as email, telephone, device ID, and postal address, refer to the same customer. At CDP Institute, we don’t feel the CDP itself needs to find the matches between those identifiers. That’s because there’s lots of good outside software to do that. We do feel the CDP needs to be able to maintain a current list of matches, or identity graph, as matches are added or removed over time. That’s what lets the CDP combine data from different sources into unified profiles. </p><p>
My second cluster of advice relates to AI. I’d be surprised if anyone reading this hasn’t at least tested ChatGPT, Bing Chat Search, or something similar. At the same time, there’s quite a bit of research showing that relatively few companies have moved beyond the testing stage to put the advanced AI tools into production. </p><p>
And that’s really okay, so my first piece of advice is: don’t be hasty. You should certainly be testing and probably deploying some initial applications, but don’t feel you must plow full speed ahead or you’ll fall behind. Most of your competitors are moving slowly as well. </p><p>That said, you do need to train your people in using AI. They don’t necessarily need to become expert prompt writers, since the systems will keep getting smarter so specific prompting skills will become obsolete quickly. But they do need to build a basic understanding of what the systems can and can’t do and what it’s like to work with them. That will change less quickly than things like a user interface. The more familiar your associates become with AI, the less likely they are to ask it to do something it doesn’t do well or that creates problems for your company.</p><p>
Third, pay close attention to AI’s ability to ingest and use your company’s own data. Remember the game-changing marketing application for AI is to create an optimal experience for each individual. The means it must be able to access that individual’s data. This is an ability that was barely available in tools like ChatGPT six months ago, but has now become increasingly common. Still, you can bet there will be huge differences in how different products handle this sort of data loading. Some will be designed with customer experience optimization in mind and some won’t. So be sure to look closely at the capabilities of the systems you consider.</p><p>
Fourth, and closely related, the industry faces a huge backlog of unresolved issues relating to privacy, intellectual property ownership, security, bias, and accuracy. Again, these are all evolving with phenomenal speed, so it’s hard for anyone to keep up – even including the AI specialists themselves. Unless that’s your full time job, I suggest that you keep a general eye on those developments so you’re aware of issues that might come with any particular application you’re considering. Then, when you do begin to explore an application, you’ll know to bring in the experts to learn the current state of play.</p><p>
Similarly, keep an eye on the new capabilities that AI systems are adding. This is also evolving at incredible speed. Some of those capabilities may change your opinion of what the systems can do well or poorly. Some will be directly relevant to your needs and may form the basis for powerful new applications. We’re still far away from the “one AI to rule them all” that will be the ultimate game-changer for marketing. But it’s coming, so be on the alert.</p><p>
This brings us back to the third level of marketing technology: delivery. Will there be yet one more game-changer, a unified delivery system that offers the same simplification advantages as unified data and decision layers? The giant suite vendors like Salesforce and Adobe certainly hope so, as do the unified messaging platforms like Braze and Twilio. The fact that we can list those vendors offers something of an answer: some companies think a unified delivery layer is possible and would argue they already provide one. I’m not so confident because new channels keep popping up and it’s nearly impossible for any one vendor to support them all immediately. </p><p>
What seems more likely is a hybrid approach where most companies have a core delivery platform that handles basic channels like email, websites, and advertising and supports third-party plug-ins to add channels or features they do not. These platforms are already common, so this is less a prediction of the future than an observation of the present, which seems likely to continue. The core delivery platform offers a single connection to the decision layer run by the AI. This gives the primary benefits gained from a single connection between layers, although I wouldn’t call it a game-changer only because it already exists.</p><p>
My recommendation here is to adapt the delivery platform approach, seeking a platform that is as open as possible to plug-ins so it can coordinate experiences across as many channels as possible. In this view, the delivery platform is really an orchestration system. Which channels are actually delivered in the delivery platform and which are delivered by third-party plug-ins is relatively unimportant from an architectural point of view. Of course, vendors, marketers, and tech staff will all care a great deal about which tools your company chooses. </p><p>
While we’re discussing architectural options, I should also mention that the big suites would argue that data, decision, and delivery layers should all be part of one unified product, reducing integration efforts to a minimum. That may well be appealing, but remember that most of the integrated suites were cobbled together from separate systems that were purchased by the vendors over time. Connecting all the bits can be almost as much work as connecting products from different vendors. </p><p>
And, of course, relying on a single vendor for everything means accepting all parts of their suite – some of which may not be as good as tools you could purchase elsewhere. The good news is most suite vendors have connectors that enable users to use external systems instead of their own components for important functions. As always, you have to look in detail at the actual capabilities of each system before judging how well it can meet your needs.</p><p>
So, where does this leave us?</p><p>We’ve seen that the object of the modern marketing game is to deliver the optimal experience for each customer. And we’ve seen that challenges to winning that game include organizational conflicts, fragmented data, and fragmented decision and delivery systems.</p><p>
We’ve also seen that the combination of customer data platforms and AI systems can indeed be game-changing, because CDPs create the unified data that AI systems need to deliver experiences that are optimized with a previously-impossible level of accuracy.</p><p>
CDPs and AI won’t fix everything. Organization conflicts will still remain, since other departments can’t be expected to turn over all responsibility to marketing. And fragmentation will probably remain a feature of the delivery layer, simply because new opportunities appear too quickly for a single delivery system to provide everything.</p><p>In short, the game may change but it never ends. Build strong systems that can meet current requirements and can adapt easily to new ones. But never forget that change is inherently unpredictable, so even the most carefully crafted systems may prove inadequate or unexpectedly become obsolete. Adapting to those situations is where you’ll benefit from investment in the most important resource of all: people who will work to solve whatever problems come up in the interest of your company and its customers.</p><p>
And remember that no matter how much the game changes, the goal is always the same: the best experience for each customer. Make sure everything you do is has that goal in mind, and success will follow.</p>David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-48225564444509742592023-07-01T17:17:00.002-04:002023-07-01T17:17:16.704-04:00In a World Run by AI, The Best Data Wins<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi_TxmeDauVp0nCx2jLxpqIb20QBM82eCLQJYNK7FzmoE-ZqEuVcpYietJAvHjpSGyuVHmi_t40bPsSvxtRsm3UVp4WxvnuKCZovv4l41v1hXSmr_4XjtP2PH5dI9hQVF2ookbLlleJAP7kckR9tOiJj1lIB3AWeHN5LZHjXoQrfjOZR-Ga68x9/s1118/Robot%20with%20Data.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="1118" data-original-width="1103" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi_TxmeDauVp0nCx2jLxpqIb20QBM82eCLQJYNK7FzmoE-ZqEuVcpYietJAvHjpSGyuVHmi_t40bPsSvxtRsm3UVp4WxvnuKCZovv4l41v1hXSmr_4XjtP2PH5dI9hQVF2ookbLlleJAP7kckR9tOiJj1lIB3AWeHN5LZHjXoQrfjOZR-Ga68x9/s320/Robot%20with%20Data.png" width="316" /></a></div><p>Like everyone else in martech land, I’ve been pondering the future of marketing in a world populated with AI. Most research I’ve seen agrees with <a href="https://offers.hubspot.com/ai-marketing" target="_blank">this Hubspot report</a> that marketers’ top application for generative AI has been content creation (48%), followed closely by data analysis (45%) and learning how to things (45%). So it’s fairly clear that the immediate impact of AI will be to let marketers create vastly more copy, including real-time messages tailored to specific individuals. </p><p>Extending this a bit, we can expect those real-time messages to be delivered within ever-more-finely tailored campaign flows (something else that AI can easily generate) or without formal campaign structures at all. These messages will be orchestrated and optimized across all channels, fulfilling the omnichannel vision that has hovered like a mirage on the industry horizon for decades. </p><p>(Whether this results in more or fewer martech applications is a separate question. I tend to agree with <a href="https://insights.gpbullhound.com/report/ai-investing-during-a-technological-revolution/" target="_blank">this GP Bullhound</a> study, which argues that “Any application introduced as an AI-enhanced alternative to an existing application or as a feature to a platform will likely become redundant when that incumbent platform implements the same AI features.” This suggests that fewer specialist martech products will be needed, since truly new applications are relatively rare. Moreover, the productivity benefits of integrated suites are magnified when AI can easily orchestrate tasks within the suite, but not those on the outside. And, AI systems are inherently more adaptable than traditional software, which must be explicitly programmed for each new task. So extending existing applications becomes easier when AI enters the picture.) </p><p>
Of course, what the humans reading this piece really care about is the role that they will play in this AI-managed universe. (Come to think of it, the AIs reading this may also care more than we know.) We’re frighteningly close to living <a href="https://www.brainyquote.com/quotes/warren_bennis_402360" target="_blank">the old joke</a> about the factory of the future, where the one human employee's job is to feed the dog, and the dog's job is to keep the human away from the equipment. </p><p>
The conventional answer to that question is that humans will still be needed to provide the creative and emotional insight that AIs cannot deliver. (That’s what ChatGPT told me when I asked.) Frankly, I don’t buy it: just as <a href="https://www.goodreads.com/quotes/7132775-the-key-to-success-is-sincerity-if-you-can-fake" target="_blank">people who can fake sincerity, have it made</a>, the AIs will quickly learn to mimic creativity and find which emotion-based messages work best. </p><p>
Still, let’s be a bit optimistic and assume the marketing department of the future includes one human whose job is to feed the AI. Let’s even assume that human adds some creative and emotional value. The net result across the marketing industry as a whole will be that every company produces a wonderfully high and consistent level of marketing outputs. While that’s great in many ways, it also means that better marketing will no longer be a competitive differentiator. It’s a repeat of the situation in manufacturing since the 1970’s, when quality best practices were applied everywhere, so the differences between the best and worst products were often too small to matter. The winners in that world were companies who could use marketing to differentiate what in fact were commodity products. But when AI lets every company produce great marketing, then marketing itself is also a commodity. </p><p>
So, how will companies compete in this new world? I’ve already argued it won’t be through emotional insight or creativity. I briefly thought that people might do better than machines at adapting to rapid change. The theory was that AI can only be trained on historical data so it will flounder when faced with unexpected events – which are increasingly common. But, let’s face it: humans also flounder in the face the unexpected. It’s not at all clear they’ll be better than machines at predicting abrupt change, recognizing changed circumstances, collecting new evidence, and finding the new best actions. In fact, <a href="https://www.amazon.com/Thinking-Fast-Slow-Daniel-Kahneman/dp/0374533555" target="_blank">humans are heavily biased in favor of making decisions based on past experience</a>, so I’d probably bet on the AI. </p><p>
But all is not lost. I still see two ways for companies, and the humans who run them (for now), to distinguish themselves.</p><p>
The first is customer experience. If you consider the true commodity industries, such as telecommunications, air travel, hotels, and financial services, what makes customers loyal to one or another provider is rarely the actual product or price. Rather, it’s the way they are treated. As a baseline, customers expect reasonable service, delivered pleasantly. But loyalty is really won or lost when there’s a problem or special request. This gives the company a chance to distinguish itself, either against an actual competitor or against a customer’s expectations of how they should be treated. </p><p></p><p>Company policies and systems play a large role in what’s possible, but ultimately it’s the front-line employee whose training, attitudes, and choices make or break the experience. In the future, AI will surely play a larger role in managing these interactions. But, as with marketing, this will yield largely similar results because companies will be using similar AI systems. The differentiator will be the company’s people.</p><p>That's good to know, but customer experience is rarely under marketers’ direct control. This leaves one final straw for them to lean on: the data used to train their AIs.</p><p>
Remember, AI programs themselves will be widely available. As with any other technology, the difference in results will depend on how they’re used, not any difference in the technology itself. Once AI systems are fully deployed, most decisions about things like content and program design will be made by the system, limiting the impact of user choice on the outcomes. But the one thing that will remain under users’ control is the data fed into the AI systems. It’s differences in that data that will drive differences in outcomes. In short: whoever has the best data, wins.</p><p>
This is an area where marketers have a major role to play. They may not control the various internal and external systems who provide customer data. But they will have a large say in what those systems feed into the primary customer data store which, in turn, will feed the AI. Marketers who select the best data feeds will have a more effective AI and, thus, better final results. This will be true even though AI makes data collection easier: that will reduce the technical barriers to data gathering at all companies, but most barriers are actually organizational and budgetary. Those are company-specific decisions whose outcomes marketers can affect.</p><p>
This may not be the cheeriest news you’ve heard today. Few marketers chose their career because they wanted to fight political battles with IT and customer success teams. But it does mean that many marketers can continue to play a major role in the success of their organizations, even as most of the traditional marketing tasks are taken over by AI. No doubt, the marketers who remain employed will sneak a few of their creative and emotional insights into AI prompts. Maybe their inputs will even make a positive difference. But what will really matter is how good a job they do at feeding the AI with the best possible data. That's what will empower it to deliver better results than the competition.</p><p>
Remember: in a world run by AI, the best data wins.</p>
David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-89553581972410600932023-05-26T16:44:00.005-04:002023-05-26T16:47:18.889-04:00Chat-Based Development Changes the Build vs Buy Equation<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEikD4qzXPF1fqgx4lJ67IejiNnloRtdGDwUMoNlN9kvN4QVaocG48in8Kv-u6qo2tiU5PJQpCxZyourVLsTWVV1Rr1hTVxWLwCYkhHPYRsmf6scYy3W7m_jIGcz0Owl6u7aoNJ_2RfZ043qOMZ-8DR_n8mgIKTaydkXK0sVp6sLKR0JusNdpA/s1527/build%20vs%20buy%20scale.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="1020" data-original-width="1527" height="214" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEikD4qzXPF1fqgx4lJ67IejiNnloRtdGDwUMoNlN9kvN4QVaocG48in8Kv-u6qo2tiU5PJQpCxZyourVLsTWVV1Rr1hTVxWLwCYkhHPYRsmf6scYy3W7m_jIGcz0Owl6u7aoNJ_2RfZ043qOMZ-8DR_n8mgIKTaydkXK0sVp6sLKR0JusNdpA/s320/build%20vs%20buy%20scale.png" width="320" /></a></div>The size of most markets is governed by a combination of supply and demand. Typically one or the other is limited: while there may be a bottomless appetite for chocolate, there is only so much cocoa in the world; conversely, while there is a near-infinite supply of Internet content, there are only so many hours available to consume it. The marketing technology industry has been a rare exception with few constraints on either factor. The supply of new products has grown as software-as-a-service, cloud platforms, low-code development tools, and other technical changes reduce development cost to something almost anyone with a business idea can afford, and widely available funding reduces barriers still further. Meanwhile, the non-stop expansion in marketing channels and techniques has created an endless demand for new systems. Further accelerating demand, the spread of digital marketing to nearly all industries has powered development of industry-specific versions of many product types.<p></p><p>
Despite the visibility of these factors, the uninterrupted growth of martech has been accompanied almost from the start by predictions it will soon stop. This based less on any specific analysis than a fundamental sense that what goes up must come down, which is generally a safe bet. There’s also an almost esthetic judgement that a market so large is just too unruly, and that the growing number of systems used by each marketing department must indicate substantial waste and lost control.</p><p>
One common metric cited as evidence for excess martech is utilization rate: Gartner, the high priests of rational tech management, <a href="https://www.gartner.com/en/marketing/research/annual-cmo-spend-survey-research" target="_blank">reported last year</a> that martech utilitzation rates fell from 58% in 2020 to 42% in 2022 and ranted in a <a href="https://www.gartner.com/en/newsroom/press-releases/2023-05-22-gartner-survey-reveals-71-percent-of-cmos-believe-they-lack-sufficient-budget-to-fully-execute-their-strategy-in-2023" target="_blank">recent press release</a> that “The willingness to let the majority of their martech stack sit idle signifies a fundamental resource disconnect for CMOs. It’s difficult to imagine them leaving the same millions of dollars on the table for agencies or in-house resources. This trade-off of technology over people will not help marketing leaders accelerate out of the challenges a recession will bring.” They were especially incensed that their data showed CMOs are increasing martech’s share of the marketing budget, comparing them to “gamblers looking to write-off their losses with the next bet.” (They probably meant “recoup”, not “write-off” their losses.)</p><p>
This isn’t just a Gartner obsession. Reports from <a href="https://resources.integrate.com/all-resources/the-state-of-b2b-marketing-budgets" target="_blank">Integrate</a>, <a href="https://www.wildfirepr.com/labs/the-science-of-b2b-influence/wfm_2020_b2binfluencer_guide-5-pdf" target="_blank">Wildfire</a>, and <a href="https://ascend2.com/wp-content/uploads/2022/11/The-Future-of-the-Martech-Stack-221118.pdf" target="_blank">Ascend2</a> also cite low utilization rates as evidence of martech overspending.</p><p>
It aint necessarily so.</p><p>
For one thing, underutilization is common in all departments, not just marketing. <a href="https://www.nexthink.com/resource/unused-software-licenses" target="_blank">Nexthink found</a> half of all SaaS licenses are unused. <a href="https://zylo.com/reports/2023-saas-management-index/" target="_blank">Zylo</a> put the average company-wide utilization at 56% and <a href="https://productiv.com/blog/what-is-saas-sprawl-how-can-you-get-it-under-control/" target="_blank">Productiv</a> put it at 45%. (These studies measure app usage, not feature usage. But you can safely bet that feature usage rates are similarly low through the organization.)</p><p>
More fundamentally, there’s no reason to expect people to use all the features of the products they buy. What fraction of Excel or Powerpoint features do you use? What’s important is finding a system with the features you need; if it has other features you don’t need, that’s really okay so long as you’re not paying extra or finding they get in your way. Software vendors routinely add features required by a subset of their users. Since that helps them serve more needs for more clients, it’s a benefit, not a problem.</p><p>
The real problem isn’t presence of features you don’t need, but the absence of features you do. That’s what pushes companies to buy new systems to fill in the gaps. As mentioned earlier, the great and on-going growth of the martech industry is due in good part to new technologies and channels creating new needs which existing systems don’t fill. That said, it's true that some purchases are unnecessary: buyers don’t always realize that a system they own offers a capability they need. And, since vendors add new features all the time, a specialist system may become redundant if the same features are added to a company’s primary system.</p><p>
In both of those situations, avoiding unnecessary expense depends on marketers keeping themselves informed about what their current systems can do. This is certainly a problem: thanks to the miracle of SaaS, it’s often easier to buy a new system than the fully research the features of systems already in place. (Installing and integrating the new system will probably be harder, but that comes later.) So we do see reports of marketers trying to prune unnecessary systems from their stacks: for example, the previously-cited <a href="https://resources.integrate.com/all-resources/the-state-of-b2b-marketing-budgets " target="_blank">Integrate report</a> found that 26% of marketers expected to shrink their stacks. Similarly, the <a href="https://www.cmocouncil.org/thought-leadership/reports/outsmart-adversity" target="_blank">CMO Council found</a> 25% were planning to cut martech spend and <a href="https://www.nielsen.com/insights/2023/need-for-consistent-measurement-2023-nielsen-annual-marketing-report/" target="_blank">Nielsen said</a> 24% were planning martech reductions. (Before you sell all your martech stock, you should also know that each report found even more marketers were planning to increase their martech budgets: 32% for Integrate, 36% for CMO Council, and 56% for Nielsen.)</p><p>Let’s assume, if only for argument’s sake, that marketers are reasonably diligent about buying only products that close true gaps. New gaps will continue to appear so long as innovation continues, and there’s no reason to expect innovation will stop. So can we expect the growth in martech products will also continue indefinitely?</p><p>
Until recently I would have said yes (unless budgets are severely crimped by recession). But the latest round of AI-based tools has me reconsidering. </p><p></p><p>Specifically, I wonder whether marketers will close gaps by building their own applications with AI instead of buying applications from someone else. If so, industry expansion could halt.</p><p>Marketers have been using no-code technologies to build their own applications for some time. Some of these may have depressed demand for new martech products but I don’t think the impact has been substantial. That’s because no-code tools are usually constrained in some way: they’re an interface on top of an existing application (drag-and-drop journey builders), a personal productivity tool (Excel), or limited to a single function (Zapier for workflow automation). Building a complete application with such limited tools is either impossible or not worth the trouble. </p><p>
The latest AI systems change this. Chat-based interfaces let users develop an application by describing what they want a system to do. This enables the resulting system to perform a much broader set of tasks than a drag-and-drop no-code interface. That said, the actual capabilities depend on what the model is trained to do. Today, it still takes considerable technical knowledge to include the right technical details in the instructions and to refine the result. But the AIs will quickly get better at working out those details for themselves, drawing on larger and more sophisticated information about how things should be done. Microsoft’s latest description of <a href="https://blogs.microsoft.com/blog/2023/05/23/microsoft-build-brings-ai-tools-to-the-forefront-for-developers/" target="_blank">copilots and plug-ins</a> points in this direction: “customers can use conversational language to create dataflows and data pipelines, generate code and entire functions, build machine learning models or visualize results.” </p><p>
What’s important is that the conversational interface will drive a system that automatically employs professional-grade practices to ensure the resulting application is properly engineered, tested, deployed, and documented. In effect, it pairs the business user with a really smart developer who will properly execute what the user describes, let the user examine the results, and tweak the product until it meets her goals. Human developers who can do this are rare and expensive. AI-based developers who can do this should soon be common and almost free.</p><p>
This change overcomes the fundamental limitation of most user-built apps: they can only be deployed and maintained by the person who built them and are almost guaranteed to violate quality, security and privacy standards. This issue – let’s call it governance – has almost entirely blocked deployment of user-built systems as enterprise applications. Chat-built systems remove that barrier while fundamentally altering the economics of system development. More concretely: building becomes cheaper so buying becomes less desirable. This could significantly reduce the market for purchased software.</p><p>
Anyone who has ever been involved in an enterprise development project will immediately recognize the flaw in this argument: there’s never just one user and most development time is actually spent getting the multiple stakeholders to agree on what the system should do. Agile methodologies mitigate these issues but don’t entirely eliminate them. </p><p></p><p>Whether chat-driven development can overcome this barrier isn’t clear. It will certainly speed some things up, which might enable teams to build and compare alternative versions when trying to reach a decision. But it might also enable independent users to create their own versions of an application, which would probably lead to even stiffer resistance to adopting someone else’s approach. </p><p></p><p>One definite benefit should be that chat-based applications will learn to explain how they function in terms that humans can understand. Responsible tech managers will insist on this before they deploy systems those applications create.</p><p>
In any event, I do believe that chat-built systems will make home-built software a viable alternative to packaged systems in a growing number of situations. This will especially apply to systems that fill small, specialized gaps created as new marketing technologies develop. Since filling these gaps has been a major factors behind the continuous growth of martech, industry growth may slow as a result.<br /></p><p>
Incidentally, we need a name for chat-based development. I’ll nominate “vo-code”, as shorthand for voice-based coding, since it fits in nicely with pro-code, low-code, and no-code. I could be talked into “robo-code” for the same reason.</p><p>
</p>David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-82335702490455475662023-05-15T19:10:00.007-04:002023-05-16T14:21:10.263-04:00Let's Stop Confusing LEGO Blocks with Computer Software<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjvTnY9IS8bk-T5HDWaeJfPnOBknf3f87hN_LYx-dVDbZRGpmPPP7Iq5z_QYXRXQ5mwDPduwM_taafWcRE2BjPeAYkrQm8DNpjYareubxCm_tBfIwfR_iSUDJBJ20TxhDxLgIYPRK6GQ9ZjLU7_c750utAB8wDrN6HYXRELn8XjA9ZT4-VvMw/s426/blog%20lego%20no%20entry%20sign.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="317" data-original-width="426" height="238" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjvTnY9IS8bk-T5HDWaeJfPnOBknf3f87hN_LYx-dVDbZRGpmPPP7Iq5z_QYXRXQ5mwDPduwM_taafWcRE2BjPeAYkrQm8DNpjYareubxCm_tBfIwfR_iSUDJBJ20TxhDxLgIYPRK6GQ9ZjLU7_c750utAB8wDrN6HYXRELn8XjA9ZT4-VvMw/s320/blog%20lego%20no%20entry%20sign.png" width="320" /></a></div><p>Can we retire Lego blocks as an analogy for API connections? Apart from being a cliché that’s as old as the hills and tired as a worn-out shoe, it gives a false impression of how easy such connections are to make and manage. </p><p>Very simply, all Lego block connectors are exactly the same, while API connections can vary greatly. This means that while you can plug any Lego block into any other Lego block and know it will work correctly, you have to look very carefully at each API connector to see what data and functions it supports, and how these align with your particular requirements. Often, getting the API to do what you need will involve configuration or even customization – a process that can be both painstaking and time consuming. When was the last time you set parameters on a Lego block?</p><p>There’s a second way the analogy is misleading. Lego blocks are truly interchangeable: if you have two blocks that are the same size and shape, they will do exactly the same thing (which is to say, nothing; they’re just solid pieces of plastic). But no two software applications are exactly the same. Even if they used the same API, they would have different internal functions, performance characteristics, and user interfaces. Anyone who has tried to pick a WordPress plug-in or smartphone app knows the choice is never easy because there are so many different products. Some research is always required, and the more important the application, the more important it is to be sure you select a product that meets your needs. </p><p>This is why companies (and individuals) don’t constantly switch apps that do the same thing: there’s a substantial cost to researching a new choice and then learning how to use it. So people stick with their existing solution even if they know better options are available. Or, more precisely, they stick with their existing solution until the value gained from changing to a new one is higher than the cost of making a switch. It turns out that’s often a pretty high bar to meet, especially because the limiting factor is the time of the person or people who have to make the switch, and very often those people have other, higher priority tasks to complete first.</p><p>I’ve made these points before, although they do bear repeating at a time when “composability” is offered as a brilliant new concept rather than a new label for micro-services or plain old modular design. But the real reason I’m repeating them now is I’ve seen the Lego block analogy applied to software that users build for themselves with no-code tools or artificial intelligence. The general argument is those technologies make it vastly easier to build software applications, and those applications can easily be connected to create new business processes.</p><p>The problem is, that’s only half right. Yes, the new tools make it vastly easier for users to build their own applications. But easily connected? </p><p>Think of the granddaddy of all no-code tools, the computer spreadsheet. An elaborate spreadsheet is an application in any meaningful sense of the term, and many people build wonderfully elaborate spreadsheets. But those spreadsheets are personal tools: while they can be shared and even connected to form larger processes, there’s a severe limit to how far they can move beyond their creator before they’re used incorrectly, errors creep in, and small changes break the connection of one application to another. </p><p>In fact, those problems apply to every application, regardless of who built it or what tool they used. They can only be avoided if strict processes are in place to ensure documentation, train users, and control changes. The problem is actually worse if it’s an AI-based application where the internal operations are hidden in a way that spreadsheet formulas are not.</p><p>And don’t forget that moving data across all those connections has costs of its own. While the data movement costs for any single event can be tiny, they add up when you have thousands of connections and millions of events. This <a href="https://www.primevideotech.com/video-streaming/scaling-up-the-prime-video-audio-video-monitoring-service-and-reducing-costs-by-90" target="_blank">report from Amazon Prime Video</a> shows how they reduced costs by 90% by replacing a distributed microservices approach with a tightly integrated monolithic application. Look <a href="https://world.hey.com/dhh/even-amazon-can-t-make-sense-of-serverless-or-microservices-59625580" target="_blank">here for more analysis</a>. Along related lines, <a href="https://webcon.com/the-state-of-low-code-2023-study/" target="_blank">this study</a> found that half of “citizen developer” programs are unsuccessful (at least according to CIOs), and that custom solutions built with low-code tools are cheaper, faster, better tailored to business needs, and easier to change than systems built from packaged components. It can be so messy when sacred cows come home to roost.</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiTXdZDC5p4xNfJYMNGfDi4VHtUD1rPo03m_5YSPp8dSaI-t1uUhBKzjIgmRX1JMxGIpiz1AYl2-0KMhdJmNPQSmFdwULx4r_zyFG7g9H1djF07qN1hPP0GRoLiKdHwI0v-EzPebvaAzcO7BT0hlnFUw8Tol-utNU9Qhnh4L3UEsOzNsS_e_w/s1121/cows%20on%20roof%202.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="1117" data-original-width="1121" height="319" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiTXdZDC5p4xNfJYMNGfDi4VHtUD1rPo03m_5YSPp8dSaI-t1uUhBKzjIgmRX1JMxGIpiz1AYl2-0KMhdJmNPQSmFdwULx4r_zyFG7g9H1djF07qN1hPP0GRoLiKdHwI0v-EzPebvaAzcO7BT0hlnFUw8Tol-utNU9Qhnh4L3UEsOzNsS_e_w/s320/cows%20on%20roof%202.png" width="320" /></a></div><p>In other words, what’s half right is that application building is now easier than ever. What’s half wrong is the claim that applications can easily be connected to create reliable, economical, large-scale business processes. Building a functional process is much harder than connecting a pile of Lego blocks.<br /></p><p>There’s one more, still deeper problem with the Lego analogy. It leads people to conceive of applications as distinct units with fixed boundaries. This is problematic because it creates a hidden rigidity in how businesses work. Imagine all the issues of selection cost, connection cost, and functional compatibility suddenly vanished, and you really could build a business process by snapping together standard modules. Even imagine that the internal operations of those modules could be continuously improved without creating new compatibility issues. You would still be stuck with a process that is divided into a fixed set of tasks and executes those tasks either independently or in a fixed sequence, where the output of one task is input to the next. <br /></p><p>That may not sound so bad: after all, it’s pretty much the way we think about processes. But what if there’s an advantage to combining parts of two tasks so they interact? If the task boundaries are fixed, that just can’t be done. <br /></p><p>For example, most marketing organizations create a piece of content by first building a text, and then creating graphics to illustrate that text. You have a writer here, and a designer there. </p><p>The process works well enough, but what if the designer has a great idea that’s relevant but doesn’t illustrate the text she’s been given? Sure, she could talk to the writer, but that will slow things down, and it will look like “rework” in the project flow – and rework is the ultimate sin in process management. More likely, the designer will just let go of her inspiration and illustrate what was originally requested. The process wins. The organization loses.</p><p>AI tools or apps by themselves don’t change this. It doesn’t matter if the text is written by AI and then the graphics are created by AI. You still have the same lost opportunity -- and, if anything, the AIs are even less likely than people to break the rules in service of a good idea.</p><p>What’s needed here is not orchestration, which manages how work moves from one box to the next, but collaboration, which manages how different boxes are combined so each can benefit from the other.</p><p>This is a critical distinction, so it’s worth elaborating a bit. Orchestration implies central planning and control: one authority determines who will do what and when. Any deviation is problematic. It’s great for ensuring that a process is repeated consistently, but requires the central authority to make any changes. The people running the individual tasks may have some autonomy to change what they do, but only if the inputs and outputs remain the same. The image of one orchestra conductor telling many musicians what to do is exactly correct. </p><p>Collaboration, on the other hand, assumes people work as a team to create an outcome, and are free to reorganize their work as they see fit. The team can include people from many different specialties who consult with each other. There’s no central authority and changes can happen quickly so long as all team members understand what they need to do. There’s no penalty for doing work outside the standard sequence, such as the designer showing her idea to the copywriter. In fact, that’s exactly what’s supposed to happen. The musical analogy is a jazz ensemble although a clearer example might be a well-functioning hockey or soccer team: players have specific roles but they move together fluidly to reach a shared goal as conditions change.</p><p>If you want a different analogy: orchestration is actors following a script, while collaboration is an improv troupe reacting to each other and the audience. Both can be effective but only one is adaptable.</p><p>Of course, there’s nothing new about collaboration. It’s why we have cross-functional teams and meetings. But the time those teams spend in meetings is expensive and the more people and tasks are handled in the same team, the more time those meetings take up. Fairly soon, the cost of collaboration outweighs its benefits. This is why well-managed companies limit team size and meeting length.</p><p>What makes this important today is that AI isn’t subject to the same constraints on collaboration as mere humans. An AI can consider many more tasks simultaneously with collaboration costs that are close to zero. In fact, there’s a good chance that collaboration costs within a team of specialist AIs will be less than communication costs of having separate specialist AIs each execute one task and then send the output to the next specialist AI.</p><p>If you want to get pseudo-mathy about it, write equations that compare the value and cost added by collaboration, with the value and cost of doing each task separately. The key relationship as you add tasks is that <b>collaboration cost grows exponentially while value grows linearly.</b>* This means collaboration cost increases faster than value, until at some point it exceeds the value. That point marks the maximum effective team size. </p><p>We can do the same calculation where the work is being done by AIs rather than humans. Let’s generously (to humans) assume that AI collaboration adds the same value as human collaboration. This means the only difference is the cost of collaboration, which is vastly lower for the AIs. Even if AI collaboration cost also rises exponentially, it won’t exceed the value added by collaboration until the number of tasks is very, very large. </p><p></p><p></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiZisIURAGueXUozO8cm4j6scBB6GRApGEDfNFyXGZGfLk8qMArrxhoKHkWpzxwHRDOCyErttKhn0jSL69jmZn9Mi0eAqzb4QKK5ItEcN-1GfM2NlTnaodBw0jaCGJgDsDP-TM0yoEDA0oc5R8CL6QMAQjoLqQlQXESniR4R2MAqHkSk7AbTA/s1100/blog%20lego%20chart2.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="609" data-original-width="1100" height="354" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiZisIURAGueXUozO8cm4j6scBB6GRApGEDfNFyXGZGfLk8qMArrxhoKHkWpzxwHRDOCyErttKhn0jSL69jmZn9Mi0eAqzb4QKK5ItEcN-1GfM2NlTnaodBw0jaCGJgDsDP-TM0yoEDA0oc5R8CL6QMAQjoLqQlQXESniR4R2MAqHkSk7AbTA/w640-h354/blog%20lego%20chart2.png" width="640" /></a></div><br />Of course, finding the actual values for those graphs would be a lot of work, and, hey, this is just a blog post. My main point is that collaboration allows organizations to restructure work so that formerly separate tasks are performed together. Building and integrating task-specific apps won’t do this, no matter how cheaply they’re created or connected. My secondary point is that AI increases the amount of profitable collaboration that’s possible, which means it increases the opportunity cost of sticking with the old task structure. <p></p><p>As it happens, we don’t need to imagine how AI-based collaboration might work. Machine learning systems today offer a real-world example the difference between human work and AI-based collaboration. </p><p>Before machine learning, building a predictive model typically followed a process divided into sequential tasks: the data was first collected, then cleaned and prepped for analysis, then explored to select useful inputs, then run through modeling algorithms. Results were then checked for accuracy and robustness and, when a satisfactory scoring formula was found, it was transferred into a production scoring system. Each of those tasks was often done by a different person, but, even if one person handled everything, the tasks were sequential. It was painful to backtrack if, say, an error was discovered in the original data preparation or a new data source became available late in the process.</p><p>With machine learning, this sequence no longer exists. Techniques differ, but, in general, the system trains itself by testing a huge number of formulas, learning from past results to improve over time. Data cleaning, preparation, and selection are part of this testing, and may be added or dropped based on the performance of formulas that include different versions. In practice, the machine learning system will probably draw on fixed services such as address standardization or identity resolution. But it at least has the possibility of testing methods that don’t use those services. More important, it will automatically adjust its internal processes to produce the best results as conditions change. This makes it economical to rebuild models on a regular basis, something that can be quite expensive using traditional methods.</p><p>Note that it might possible to take the machine learning approach by connecting separate specialist AI modules. But this is where connection costs become an issue, because machine learning runs an enormous number of tests. This would create a very high cumulative cost of moving data between specialist modules. An integrated system will have fewer internal connections, keeping the coordination costs to a minimum.</p><p>I may have wandered a bit here, so let me summarize the case against the Lego analogy:</p><ul style="text-align: left;"><li>It ignores integration costs. You can snap together Lego blocks, but you can’t really snap together software modules. <br /></li></ul><ul style="text-align: left;"><li>It ignores product differences. Lego blocks are interchangeable, but software modules are not. Selecting the right software module requires significant time, effort, and expertise. <br /></li></ul><ul style="text-align: left;"><li>It prevents realignment of tasks, which blocks improvements, reduces agility, and increases collaboration costs. This is especially important when AI is added to the picture, because expanded collaboration is a major potential benefit from AI technologies.</li></ul><p style="text-align: left;">Lego blocks are great toys but they’re a poor model for system development. It’s time to move on.<br /> </p><p style="text-align: left;">___________________________________________________________<br />* My logic is that collaboration cost is essentially the amount of time spent in meetings. This is a product of the number of meetings and the number of people in each meeting. If you assume each task adds one more meeting and one more team member, and each meeting last one hour, then a one-task project has one meeting with one person (one hour, talking to herself), a two-task project has two meetings with two people in each (four hours), a three-task project has three meetings with three people (nine hours), and so on. When tasks are done sequentially, there is presumably a kick-off at the start of each task, where the previous team hands the work off to the new team: so each task adds one meeting of two people, or two hours, a linear increase. </p><p style="text-align: left;">There’s no equivalently easy was to estimate the value added by collaboration, but it must grow by some amount with each added task (i.e., linearly), and it’s likely that diminishing returns prevent it from increasing endlessly. So linear growth is a reasonable, if possibly conservative, assumption. It's more clear that cumulative value will grow when tasks are performed sequentially, since otherwise the tasks wouldn't be added. Let's again assume the increase is linear. Presumably the value grows faster with collaboration than sequential management, but if both are growing linearly, the difference will grow linearly as well. <br /> <br /><br /><br /><br /><br /><br /><br /><br /><br /></p><br /><br />David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-44846840934848850182023-05-05T16:38:00.009-04:002023-05-07T10:40:28.165-04:00Will ChatGPT Destroy Martech?<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhACpbQ-6kpl8_JQzxcKnZ1hY3rxIv5x22BcCexBNUk54-F-pIiRyGivqpSHPr2vbPZtnznDlgGBynUqJ8Vgq32Y_exBudcDRUnPrkNuNptGyUj_YnqeIrLnFRlKJyUJPhD_F8aFXKLRAcrTZPmMtvCO1wSYWYgqJLKOSDoUhfyllqeICSYBw/s423/Robot%20Market%20Dept.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="423" data-original-width="345" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhACpbQ-6kpl8_JQzxcKnZ1hY3rxIv5x22BcCexBNUk54-F-pIiRyGivqpSHPr2vbPZtnznDlgGBynUqJ8Vgq32Y_exBudcDRUnPrkNuNptGyUj_YnqeIrLnFRlKJyUJPhD_F8aFXKLRAcrTZPmMtvCO1wSYWYgqJLKOSDoUhfyllqeICSYBw/s320/Robot%20Market%20Dept.png" width="261" /></a></div><p>Like everyone else, I’ve been pondering what generative AI means for martech, marketing, and the world in general. My crystal ball is no clearer than yours but I’ll share my thoughts anyway.<br /><br />Let’s start by looking how past technology changes have played out. My template is the <a href="https://customerexperiencematrix.blogspot.com/2016/02/why-designing-your-marketing-technology.html" target="_blank">transition from steam to electric power</a> in factories. This happened in stages: first, the new technology was used in exactly the same way as the old technology (in factories, this meant powering the shafts and belts that previously were powered by waterwheels or steam engines). Then, the devices were modified to make better use of the new technology’s capabilities (by attaching motors directly to machine tools). Finally, the surrounding architecture was changed to take advantage of the new possibilities (freed from the need to connect to a central mechanical energy source, factories went from being small, vertical structures to large horizontal ones, which allowed greater scale and efficiency). We should probably add one more stage, when the factories started to produce new products that were made possible by the technology, such as washing machines with electric motors.<br /><br />During the earliest stages of the transition, attention focused on the new technology itself: factories had “chief electricians” and companies had “chief electricity officers”, whose main advantage was they were among the few people who understood new technology. Those roles faded as the technology became more widely adopted. The exact analogy today is “prompt engineer” in AI, and will likely be even shorter-lived as a profession. <br /><br />Most of the discussion I see today about generative AI is very much stuck in the first phase: vendors are furiously releasing tools that replace this worker or that worker, or even promising a suite of tools that replace pretty much everyone in the marketing department. (See, for example, this announcement from Zeta Global https://zetaglobal.com/press-releases/zeta-introduces-generative-ai-agents-powered-by-zoe/ .) Much debate is devoted to whether AI will make workers in those jobs more productive (hurrah!) or replace them entirely (boo!) I don’t find this particular topic terribly engaging since the answer is so obviously “both”: first the machines will help, and, as they gradually get better at helping, they will eventually take over. Or, to put it in other terms: as humans become more productive, companies will need fewer of them to get their work done. Either way, lots of marketers lose their jobs. <br /><br />(I don’t buy the wishful alternative that the number of marketers will stay the same and they’ll produce vastly more, increasingly targeted materials. The returns on the increasing personalization are surely diminishing, and it’s unrealistic to expect company managers to pass up an opportunity to reduce headcount.)<br /><br />While the exact details of the near future are important – especially if your job is at stake – this discussion is still about the first stage of technology adoption, a one-for-one replacement of the old technology (human workers) with the new technology (AI workers). The much more interesting question is what happens in the second and third stages, when the workplace is restructured to take full advantage of the new technology’s capabilities.<br /><br />I believe the fundamental change will be to do away with the separate tasks that are now done by specialized individuals (copywriters, graphic designers, data analysts, campaign builders, etc.). Those jobs have evolved because each requires complex skills that take full time study and practice to master. The division of labor has seemed natural, if not inevitable, because it mirrors the specialization and linear flow of a factory production line – the archetype for industry organization for more than a century. <br /><br />But AI isn’t subject to the same constraints as humans. There’s no reason a single AI cannot master all the tasks that require different human specialists. And, critically, this change would bring a huge efficiency advantage because it would do away with the vast amount of time now spent coordinating the separate human workers, teams, and departments. There would be other advantages in greater agility and easier data access. Imagine that the AI can get what it needs by scanning the enterprise data lake, without the effort now needed to transform and load it into warehouses, CDPs, predictive modeling tools, and other systems. Maintaining those systems takes another set of specialists whose jobs likely to vanish, along with all the martech managers who spend their time connecting the different tools. <br /><br />Of course, the vision of “citizen developers” using AI to create sophisticated personal applications on the fly is entirely irrelevant when the citizen developers themselves no more have jobs. Thousands of independent applications that make up today’s martech industry may vanish, unless the marketing Ais build and trade components among themselves – which could happen.<br /><br />So far, I’ve predicted that monolithic AI systems rather than teams of (human or robotic) specialists will create marketing programs similar to today’s campaigns and interactions. But that assumes there’s still a demand for today’s types of campaign and interactions. This brings us to the final type of change: in the outputs themselves.<br /><br />Again, we may be in for a very fundamental transformation. The output of a marketing department is ultimately determined by the how people buy things. It’s a safe bet that AI will change that dramatically, although we don’t know exactly how. For sake of argument, let’s assume that people adopt AI agents to manage more of their personal lives for them (pretty likely) and that they delegate most purchasing decisions to those agents (less certain but plausible, and again already happening to a limited degree). If that happens, our AI marketing brains will be selling to other Ais, not to people. To imagine what that looks like, we again have to move beyond expecting the AI to do what people do now, and look at the best way for an AI to achieve the same results.<br /><br />If you think about marketing today – and for all the yesterdays that ever were – it’s based on the fundamental fact that humans have a limited amount of attention. Every aspect of marketing is ultimately aimed to capturing that attention and feeding the most effective information to the human during the time available. <br /><br />But AIs have unlimited attention.<br /><br />If an AI wants to buy a product, it can look at every option on the market and collect all the information available about each one. Capturing the AI’s attention isn’t an issue; presenting it with the right information is the challenge. This means the goal of the marketing department is to ensure product information is available everyplace the AI might look, or maybe in just one place, if you can be sure the AI will look there. Imagine the world as a giant market with an infinite number of sellers but also buyers who can instantly and simultaneously visit every seller and gather all the information they provide. The classical economist’s fantasy – perfect market, perfect information, no friction – might finally come true.<br /><br />And, also as the classical economists dream, the buyers will be entirely rational, not swayed by emotional appeals, brand identity, or personal loyalties. (At least, we assume that AI buyers are rational and objective, although it won’t be easy to ensure that’s the case. That relates to trust, which is a topic for another day.)<br /><br />If the role of marketing is to lay out virtual products on a virtual table in a virtual market stall, there’s no need for advertising: every buyer will pass by every stall and decide whether to engage. With no need for advertising, there’s no need for targeting or personalization and no need for personal data to drive that targeting or personalization. Privacy will be preserved simply because advertisers will no longer have any reason to violate it.<br /><br />The key to business success in this world of omniscient, rational buyers is having a superior product, and, to a lesser extent, presenting product information in the most effective way possible. There’s still some room for puffery and creativity in the presentation, although presumably mechanisms such as consumer reviews and independent research will keep marketers reasonably honest. (Trust, again.) There’s probably more room for creativity in developing the products themselves and constructing a superior experience that extends beyond the product to the full package including pricing, service, and support.<br /><br />We can expect the AIs to play a major role in developing those new and optimal products and experiences, although I suspect the pro-human romantic in everyone reading this (except you, Q-2X7Y) hopes that people will still have something special to contribute. But, wherever the products themselves come from, it will be up to the marketing AI to present them effective to the AI shoppers.</p><p></p><p></p><p>(Side note: today’s programmatic ad buying marketplace comes fairly close to the model I’m proposing. The obvious difference is the auction model, where buyers bid for a limited supply of ad impressions. It’s conceivable that the consumer marketplace would also use an auction. Again, just because most of today’s shopping is based on a fixed price
model, we shouldn’t assume that model will continue in the future. Come to think of it, an auction would probably be the best approach, since buyers could adjust their bids based on their current needs and preferences, and sellers could adjust them based on inventory and current demand. In the traditional marketplace, this would be called haggling, or negotiating, and it's the way buying has been done for most of history. With perfect information on both sides, the classical economists would be pleased yet again. It could be fruitful to explore other analogies with the programmatic marketplace when trying to predict how the AI-to-AI marketplace will play out.)</p><p></p><p>(You could also argue that Amazon, Expedia, and similar online marketplaces already offer a place for virtual sellers to offer their virtual wares to all comers. Indeed they do, but the exact difference is that searching on Amazon requires a painfully inefficient use of human time. If Amazon evolves a really good AI-based search method, and can convince users to share enough data to make the searches fully personalized, it could indeed become the basis for what I’m proposing. The biggest barrier to this is more likely to be trust than technology. It’s also worth noting that traditional marketing barely exists on those marketplaces. The travel industry, where most marketing is centered on loyalty programs, may be an early indicator of where this leads.)<br /><br />So what role, exactly, do humans play in this vision? <br /><br />As consumers, humans are no longer buyers. Instead, they receive what’s purchased on their behalf. So, their main role is to pick an AI and train it to understand their needs. Of course, most of that training will happen without any direct human effort, as the AI watches what its owner does. (I almost wrote “master”, but it’s not clear who’s really in charge.) For people with disposable income, purchases are likely to move away from basic goods to luxury goods and experiences. Those are inherently less susceptible to purely rational buying decisions, so there’s a good chance that conventional buying and attention-based marketing will still apply.<br /><br />As workers, humans are in trouble. Farming and manufacturing have been shrinking for decades and AI is likely to take over many of the remaining service jobs. Some conventional jobs will remain to do research and supervise the AI-driven machines, and there may more jobs where it matters that the worker is human, such as sports and handcrafts, and where human interaction is part of the value, such as healthcare. But total employment seems likely to decrease and income inequalities to grow. It’s possible that wealthy nations will provide a guaranteed annual income to the under-employed. But even if that happens, meaningful work will become harder to find. <br /><br />I’ll admit this isn’t a terribly pleasant prospect. The good news is, predictions are hard, so the odds are slim that I’m right. I’m also aware that we’re at the peak of the hype cycle for ChatGPT and perhaps for AI in general. Maybe what I’ve described above isn’t technically possible. But, given how quickly AI and the underlying technologies evolve, I wouldn’t bet on technology bottlenecks blocking these changes indefinitely. Quantum AI, anyone?<br /><br />All that said, of the three major predictions, I’m most confident about the first. It’s pretty likely that a monolithic marketing AI will emerge from the specialized AI bots that are being offered today. The potential benefits are huge, the path from separate bots to an integrated system is an incremental progression, and some people are already moving in that direction. (Pro tip: it’s easier to predict things that have already happened.)<br /><br />The emergence of an AI-driven marketplace to replace conventional human buying is much less certain. If it does happen, the delegation will emerge in stages. The first will cover markets where the stakes are low and buying is boring. Groceries are a likely example. How quickly it spreads to other sectors will depend on how much time people have and how much intrinsic enjoyment they derive from the shopping itself.<br /><br />The role of humans is least predictable of all. Mass under-employment probably isn’t sustainable in the long run, although you could argue it’s already the reality in some parts of the world. The range of possible long-term outcomes runs from delightful to horrific. Where we end up with depend on many factors other than the development of AI. The best we can do is try to understand developments as they happen and try to steer things in the best directions possible. <br /><br /><br /></p>David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-68511888596986321322022-08-04T17:18:00.001-04:002022-08-04T18:42:08.296-04:00Martech in the Apolocalypse<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhTKi2qJ6C0-gyewMAbWp4PSaLxwdxqREy3ph6oBOe6NKMhqHGlCBNLfMMYLvoqlokdX2QCQg14vlWaGZkTuL2pa7tzlZdRQyg66vvgDigktB7j_AqQU6iFrF8BFrMrgThbHloY6aB-PKTcsIPdfXI2rLgRipIseCPZl19JD3oxJheRYe643Q/s1751/Building_for_Agility.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="922" data-original-width="1751" height="210" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhTKi2qJ6C0-gyewMAbWp4PSaLxwdxqREy3ph6oBOe6NKMhqHGlCBNLfMMYLvoqlokdX2QCQg14vlWaGZkTuL2pa7tzlZdRQyg66vvgDigktB7j_AqQU6iFrF8BFrMrgThbHloY6aB-PKTcsIPdfXI2rLgRipIseCPZl19JD3oxJheRYe643Q/w400-h210/Building_for_Agility.png" width="400" /></a></div>I once read that the most accurate weather forecast is tomorrow will be the same as today. That may or may not be true, but it doesn’t matter. What’s really important is predicting when the weather will change. That’s what warns you to bring an umbrella for the afternoon when it’s sunny in the morning, or buy milk before a blizzard, or evacuate before a hurricane. <br /><br />The marketing industry is about to face not one but several of those seemingly sudden brutal changes. <br /><br /><b>Privacy </b><br /><br />Privacy is the change that gets the most industry attention. Our long season of customer data raining freely from the heavens is being replaced – seemingly overnight – by a customer data drought. This is one change we can’t blame on global warming, although it is man-made. <br /><ul style="text-align: left;"><li>It’s people’s concerns about privacy that prompts governments to pass laws like GDPR and CCPA, and their counterparts around the globe. </li></ul><ul style="text-align: left;"><li>It’s people’s concerns about being tracked that leads Chrome, Safari, Firefox, and every other major browser to block third party cookies, although you all know that Google just deferred that on Chrome yet again. </li></ul><ul style="text-align: left;"><li>It’s people’s concerns about controlling their data that leads Apple and Android to shut down unauthorized data sharing by smartphone apps. </li></ul><p style="text-align: left;"></p><ul style="text-align: left;"><li>And it’s people’s concerns about identity theft and fraud, that leads companies to impose ever-tighter tighter controls on how they collect, use and share customer data, even when it is legally permitted. </li></ul><p style="text-align: left;"> The immediate impact of the data drought falls on advertisers. They now find it much harder to assemble audiences, target individuals programmatically, and to connect media impressions with sales results. <br /><br /> <b>Big Tech Under Siege </b><br /><br />But there’s an arguably larger, secondary impact. Privacy changes threaten the flow of third-party data into the surveillance marketing engines of Google and Facebook, which between them capture nearly 70% of global digital ad revenues and 50% -- half – of all global advertising revenues. <br /><br /> And privacy is just one of the challenges facing Google, Facebook, and other Big Tech companies like Apple and Amazon. Governments everywhere are reining in the almost unbridled power they’ve let those firms accumulate. Power over not just advertising, but news and commerce, and future technologies like cloud hosting and artificial intelligence. </p><ul style="text-align: left;"><li>Europe recently enacted its Digital Services Act and Digital Markets Act, which are squarely targeted at reducing the power of Big Tech. </li></ul><p style="text-align: left;"></p><ul style="text-align: left;"><li>The U.S. Congress may or may not pass new privacy and anti-trust laws, but anti-trust legal action is ramping up either way. </li></ul><p style="text-align: left;"></p><ul style="text-align: left;"><li>China has had its own Big Tech clampdown under way for several years now, and Russia has recently followed suit. </li></ul><p style="text-align: left;">What these changes mean is more work for marketers. In recent years, they've been happy to shovel dollars into one end of the Big Tech black box and pipe revenue out from the other, without worrying much about what happened in between. In fact, Big Tech channels have already become less efficient and harder to use, and their volumes are falling. The Duopoly’s share of US digital ad market actually peaked in in 2017, if we believe eMarketer. </p><p style="text-align: left;"> This means marketers have to look at new channels, which indeed are flourishing. Retail media, connected TV, podcasts, in-game ads, in-app ads, social commerce, even out-of-home media and humble direct mail, are all gaining attention as marketers are forced to scramble for alternatives to walled gardens that they weren’t really all that eager to escape. <br /><br />But there’s more. <br /><br /><b>Pandemic </b><br /><br /> You may have heard of this little thing called the pandemic? <br /><br />It certainly accelerated the growth of digital media, contributing to marketers’ search for outlets to supplement Google and Facebook. But the larger impact was a growth in digital commerce, as consumers were forced to buy more online than they had ever intended. This, in turn, accelerated companies’ interest in digital transformations of all sorts: in ecommerce, in hybrid retail like curbside pickup of online purchases, and in remote work for employees. <br /><br /> The result was a crisis-driven acceleration in the pace of change for corporate systems and processes, and dare I even say culture, as companies were forced to adapt to ever-changing customer expectations, working conditions, and supply chain bottlenecks. For truly deep thinkers, I’ll throw in a power shift towards junior employees, who were more attuned than their doddering elders to digital technologies and less committed to old school, top-down management styles. <br /><br /> Fortunately for doddering elders like myself, the kids were trapped at home with us, so they could explain how to use that damned remote. <br /><br /> These changes have very specific technical implications. <br /></p><ul style="text-align: left;"><li>Adoption of no code and low code systems, which make it easier for business users to make changes without help from corporate IT teams. </li></ul><p style="text-align: left;"></p><ul style="text-align: left;"><li>Broader access to corporate data to feed the low/no code systems, and to provide feedback about how the new processes were working out. </li></ul><p style="text-align: left;"></p><ul style="text-align: left;"><li>Growth of the Customer Data Platform industry, as companies quickly realized that a foundation of clean, accessible customer data was essential to reach many of their new business goals. </li></ul><p style="text-align: left;"> <b>Everything Else </b><br /><br /> The pandemic has faded into the background while our attention shifts to new crises: war, political unrest, inflation, recession, and the unignorable symptoms of accelerating climate change. It feels a bit silly to do this, but let’s look only at how these affect marketing. <br /></p><ul style="text-align: left;"><li>From a tech vendor perspective, uncertainties make it harder to raise new capital or increase prices, and we’ve begun to see some scattered industry layoffs as a result. </li></ul><p style="text-align: left;"></p><ul style="text-align: left;"><li>From a tech buyer perspective, company belts are being tightened. While we haven’t yet seen a slowdown in martech revenues, we do know that buyers are more cautious than before. </li></ul><p style="text-align: left;"></p><ul style="text-align: left;"><li>Consumers are feeling the pinch of inflation and higher interest rates today, and worry about a recession tomorrow. </li></ul><p style="text-align: left;">It’s an environment where marketing plans are less a roadmap than a deck of contingency cards, any one of which might be played depending on how things develop. <br /></p><p style="text-align: left;"></p><p style="text-align: left;"><b>Welcome to the Future</b><br /></p><p style="text-align: left;">So welcome to the apocalypse: a chaotic present on an unstable path to an uncertain future. What’s a marketer to do? <br /><br /> The best advice anybody can give comes from <i>The Hitchhiker’s Guide to the Galaxy</i>: Don’t Panic. </p><p style="text-align: left;">The second-best advice is to recognize that change right now is both inevitable and unpredictable. So rather than picking one most likely future, preparing for it, and hoping you’re right, your best bet is to be agile, so you can adapt effectively to whatever the future may be. <br /></p><p style="text-align: left;"></p><p style="text-align: left;"></p><p style="text-align: left;"> This is more than just a platitude. We don’t know which channels will dominate as the roles of Facebook and Google continue to diminish. But we do know that there will be a lot of channels, certainly in the immediate future, as media fragmentation grows. <br /><br /><b>Fragmented Media </b><br /><br />There are specific things you can do to deal with that fragmentation. <br /></p><ul style="text-align: left;"><li>Build more adaptable customer data systems. This means systems that can easily connect to any new channel, both to collect data from the channel as customers interact, and feed data to the channel to support personalized interactions. Easy connection implies open APIs, schema-free data storage, and low- and no-code interfaces.</li></ul><p style="text-align: left;"></p><ul style="text-align: left;"><li>Build scalable systems for higher volume and variety of data. Schema-free data stores are part of the solution, but so are cloud platforms that allow effortless, affordable growth on demand. </li></ul><p style="text-align: left;"></p><ul style="text-align: left;"><li>Even more important, rely on artificial intelligence to make sense of all this new, ever-changing data. AI removes, or at least eases, the critical bottleneck of using human experts to map each new data source. Remember you might be able to capture data in a raw form without mapping it to a schema, but you still need to identify the data elements before you can use them in a customer profile. </li></ul><div style="margin-left: 40px; text-align: left;">And AI can go beyond just identifying a data type, to extracting meaning from unstructured or semi-structured contents. This might be understanding relations among entities mentioned in call notes, or classifying the sentiment expressed in a social media post. This sort of tagging is critical to extracting value from unstructured and semi-structured data.<br /></div><p style="margin-left: 40px; text-align: left;">Doing those things automatically, at scale, and with a minimum of set-up for new data feeds and types, is critical to adapting to change as it happens. <br /></p><p> </p><ul style="text-align: left;"><li>Find ways to create more content. You’ll almost certainly need a greater variety of messages, as you communicate to customers in a greater variety of circumstances – which is what happens when you have more change. You’ll also need to distribute each message in more channels, because everything no longer goes to just Facebook and Google.</li></ul><div style="text-align: left;"><div style="margin-left: 40px; text-align: left;">Our friend artificial intelligence will be critical here as well. It might actually create messages, although so far those capabilities are limited. But it will definitely to convert messages from one format to another, either entirely unsupervised or as a productivity multiplier for skilled humans.<br /></div><ul><li>Finally, you’ll need to do a better job of measurement. That’s always been a challenge for marketers, although third-party cookies and convenient if self-serving attribution reports from the walled garden vendors made it easier in recent years. But those cookies and those attribution reports are exactly what we’re losing as we enter the new era. So we’ll have to work harder to find measurement methods that apply to different situations: some where we can identify each customer from start to finish, others where each audience is entirely anonymous, and everything in between. <br /></li></ul><p style="margin-left: 40px; text-align: left;">Anonymous measurement is not a new problem. Some of the old-school methods will still apply, like test/control experiments and media mix models. You’ll also have new options, like data clean rooms and probabilistic models, that old-school marketers couldn’t imagine. The key here is to find techniques that apply to many different channels, because the number of channels will continue to increase. <br /></p><p style="text-align: left;"><b>More First Party Data </b><br /><br /> The second thing we know for certain is that in the future your own data – first-party data – will become more important. <br /><br />In part, that’s a natural consequence of the loss of third-party data: you make the best use of what’s available. When you can no longer just buy and test different prospect lists to see which work, you build look-alike models based on your existing customers and have media partners use those models to select your best prospects from their own lists. Or you use a data clean room to match your customers with their customers, which is a more effective way of doing the same thing. <br /><br /> But there’s more to it than that. Consumers are increasingly wary of sharing their data with strangers, but your customers don’t see you as a stranger. They see you as someone they’ve chosen to do business with, in part because they think they can trust you to handle their data properly, and to use that data in their interest. <br /><br />Let me be clear: this isn’t an option: customers know you have their data and they expect that you’ll use it to help them. <br /><br /> Now, your customers’ definition of ‘help’ isn’t to send them personalized advertisements: in fact, survey after survey shows that most customers say they don’t want any advertisements at all. </p><p style="text-align: left;">What they do want is better service, which in their minds means greater convenience for things like placing an order, receiving a delivery, or making a return. The good news is they also want marketing that looks like good service, such as suggesting a useful product upgrade or more suitable pricing plan. If you do that kind of marketing really well, they’ll rave about how great it is to be your customer. <br /><br />Let me make my point more directly: the only way you can give really terrific service is to have really terrific customer data. That data is what lets you understand and anticipate what each customer wants and how they want it delivered. <br /><br />You’ll also need powerful analytics to make sense of that data, which brings us back once more to our friend artificial intelligence. <br /><br /> <b>The Story So Far </b><br /> <br />Let’s stop here for a quick recap. <br /></p><ul style="text-align: left;"><li>Change is accelerating. And while we can’t predict the specifics, we do know that channels will fragment, and personal data will be harder to get. </li></ul></div><div style="text-align: left;"><ul style="text-align: left;"><li>Agility is the key to dealing with both those changes. Agility allows you to exploit whichever channels turn out to be important. And agility lets you take full advantage of whatever data you’re able to collect. </li></ul><p style="text-align: left;"><b>Building for Agility </b><br /><br /> The final question, then, is how do you build for agility? And that’s a paradox, right? How can you build a solid, stable platform that allows you to be flexible? <br /><br /> Well, it’s not really a paradox. Imagine you want to build a ballet theater. <br /><br />The stage of that theater will feature the world’s most agile, flexible, dynamic ballerinas, appearing in a wide variety of shows. In fact, that same stage may also host operas, orchestras, musical theater, and maybe even a circus or two. The theater has to be flexible and adaptable to accommodate these diverse uses, but also strong and solid enough to support leaping ballerinas and trudging elephants without collapsing under the strain. <br /><br /> It’s the same for your marketing stack. The customer-facing systems in your stack are ballerinas: talented, hard-working, and exciting, they’re the stars of the show. </p><p style="text-align: left;"></p><p style="text-align: left;">But they’re also replaceable. Dancers come and go from season to season and even one performance to the next. Lose any single dancer, and the show will go on without missing a performance. Lose the entire corps de ballet, and at worst you’re delayed a few weeks as you rehearse their replacements. <br /><br />But if the theater burns down, you’re out of business for a very long time. <br /><br /> As you’ve no doubt figured out by now, your Customer Data Platform is that theater. It’s the repository of accumulated knowledge and resources that enable your business applications to adapt and thrive in the face of change. <br /><br /> We’ve already listed technical features that make this possible: </p><ul style="text-align: left;"><li>easy connectivity to new sources and destinations </li><li>flexible, scalable data storage </li><li>efficient content creation and conversion from one channel to another </li><li>alternative measurement techniques for different situations </li><li>privacy-supporting technologies such as data clean rooms and policy enforcement, and </li><li>enabling technologies like artificial intelligence, composable architectures, and no code/low code tools </li></ul><p style="text-align: left;">The key point here is you need a clear separation between your stable, central customer data system and the applications which depend on that system. That separation is what makes it relatively easy to add or change applications without disrupting other parts of the operation. Violating the separation is dangerous because any application that also stores centralized data cannot easily be replaced. <br /><br /> The ballet analogy for that is a principal dancer who’s also the director and choreographer. Losing that person shuts down the operation, or at least requires a long period of retooling while you recruit replacements for both jobs. </p><p style="text-align: left;">There may be times where you want to take this risk, because the one person, or one system, is so good at both roles that you build your organization around it. But you should at least recognize what you’re doing is dangerous, and be sure it’s really worth the taking the chance. You’ll also want to take out the technology equivalent of key person insurance, doing whatever you can to minimize your reliance on that dual purpose system and to separate the platform from application functions. <br /> <br />I should also stress that separating the platform from applications doesn’t free you to use any applications you want. You still need applications that integrate well with your platform, and ideally will complement the strengths and weaknesses of the applications already in place. <br /><br /> We could say it’s like hiring ballerinas who fit the company style, but let’s give those poor ballerinas a rest. <br /><br /> <b>Agility Beyond Technology </b><br /><br />You should also recognize that technology is just one part of agility. People and process are at least as important. You have to support the technology with training, governance, organizational alignment, leadership, recruiting, and reward systems that empower staff respond effectively as new conditions arise. <br /><br /> A few guidelines include: <br /></p><ul style="text-align: left;"><li>Decide on data, not intuition. That’s been the goal forever. But it’s much more important when intuitions are less reliable because they've developed under conditions that no longer exist. </li></ul></div><div style="text-align: left;"><ul style="text-align: left;"><li>Experiment, don’t optimize. Optimization is based on fine-tuning parameters over time. This only works when the underlying conditions are stable. In a period of rapid change, it’s more important to uncover big new opportunities than to squeeze small improvements out of the old ones. </li></ul><ul style="text-align: left;"><li>Measure outcomes, not inputs. When conditions are stable, the relation between inputs and outcomes can be discovered. This means you can measure one to predict the other. In unstable conditions, the old assumptions don’t apply. So you’ll have to put in the extra effort it takes to measure outcomes directly. </li></ul><ul style="text-align: left;"><li> Manage customers, not departments. What I really mean here is to manage the customer experience across all departments. Customers don’t know or care which department controls which part of their experience. They only judge the company on their experience as a whole. Departments working in isolation from each other cannot see the ultimate results of their actions. The only way to deliver good outcomes is to cooperate and manage from the customer’s view. <br /></li></ul><p style="text-align: left;">Let me focus on that last point for a moment, because it’s especially important. <br /><br /> <i>Agility cannot be just random leaping about. It needs a purpose, so you know which direction to leap. And in business, the purpose of agility is to deliver great customer experience. <br /></i><br /> <b>Summary </b><br /><br />I hope the core message is clear: </p><ul style="text-align: left;"><li>In uncertain times, agility is the key to success. </li></ul><ul style="text-align: left;"><li>Agility is more than being flexible. Quick, effective response to change requires strong, stable base of technical and human resources – including a solid Customer Data Platform. </li></ul><ul style="text-align: left;"><li>The purpose of agility is meet the one goal that never changes: delivering a great customer experience. </li></ul></div>David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-26042076322146993342021-12-30T14:55:00.003-05:002021-12-30T14:56:23.262-05:00Game of Thrones Meets Big Bang Theory: Welcome to CDP Industry's Next Phase<p>The CDP Institute just published its latest Industry Update, our semi-annual overview of CDP vendors with data on employment, funding, locations, and more. (<a href="https://www.cdpinstitute.org/resources/industry-update-january-2022/" target="_blank">Download here.</a>) There were three pieces of information that stood out:
</p><ul style="text-align: left;"><li>Only four new vendors were added, compared with an average of fifteen in past reports.</li></ul><p></p><ul style="text-align: left;"><li>four companies reported funding rounds over $100 million, compared with one round that size across all past reports</li></ul><p></p><ul style="text-align: left;"><li>nearly all employment growth (85%) came from previously listed vendors, compared with just 36% in past reports</li></ul><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEiSEv5wuNvg4hIqyhGJgFpnkqhsIOL5Z4A3EcCgOAM7pfF11ttiTwHIBVWVFycW-KET9ZG8u0jZHaxHdmJxXyQDe00OUask9jWeSklFx7BdNsUxQ4YXRIN5qEzXLt5Y7Q66ixE4OxVdg28U9VTy_AHkcYjPcjO5rVjvAcH6pxSmNpya5Ddq5Q=s977" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="377" data-original-width="977" height="246" src="https://blogger.googleusercontent.com/img/a/AVvXsEiSEv5wuNvg4hIqyhGJgFpnkqhsIOL5Z4A3EcCgOAM7pfF11ttiTwHIBVWVFycW-KET9ZG8u0jZHaxHdmJxXyQDe00OUask9jWeSklFx7BdNsUxQ4YXRIN5qEzXLt5Y7Q66ixE4OxVdg28U9VTy_AHkcYjPcjO5rVjvAcH6pxSmNpya5Ddq5Q=w640-h246" width="640" /></a></div><br />Of course, it makes sense that most growth would come from existing vendors if we added few new ones. But industry growth over-all was in line with past trends, and actually a bit stronger: up 12% over the past six months. This meant that the growth rate of existing vendors was high enough to compensate for the “loss” of new vendors. In fact, the existing vendor growth of 11% was the highest since 2018, when the industry was just taking off.
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Connecting these dots reveals a clear picture: an industry that has stopped attracting new entrants but is now growing strongly on its own – with leading vendors stockpiling funds to compete against each other an elimination round where only a few can emerge as winners. Think <i>Survivor</i> meets <i>Game of Thrones</i> with a dash of <i>Big Bang Theory</i>.
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It’s a picture that makes a lot of sense. Customer Data Platforms are now widely accepted as an essential component of a modern data architecture, so it’s a market worth fighting for. But the situation facing potential entrants is daunting:
</p><ul style="text-align: left;"><li>the leading independent CDP vendors now have mature products, big customer bases, high brand recognition, and lots of funding. </li></ul><p></p><ul style="text-align: left;"><li>enterprise software companies, including Salesforce, Adobe, Oracle, Microsoft, and SAP, are chipping away at the market by selling CDPs as part of their packages. </li></ul><p></p><ul style="text-align: left;"><li>marketing automation, customer support, ecommerce, and other vendors increasingly offer CDP modules baked into their own systems<br /></li></ul><p></p><ul style="text-align: left;"><li>IT teams show growing interest in building their own CDP equivalent, supported by a growing array of components that make the job easier. </li></ul><p>
Some mid-tier CDP vendors have already given up the fight and been acquired, most often by firms needing a CDP to anchor a multi-channel customer experience suite. The acquisition wave may have peaked, since there were just three acquisitions in the latest report, compared with a dozen over the previous two. </p><p>Among the remaining firms, some may compete successfully as generalists. But the more promising path in most cases will be to offer specialized products that can be the best in a particular niche. Those niches might be defined by a particular industry, region, company size, or CDP function.
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The functional niches are most intriguing because they serve the growing market for CDP components. We’ve seen some movement in that direction, as vendors offer parts of their CDP as stand-alone modules for identity resolution, data collection, data distribution (“reverse ETL”), and campaign management. Those vendors see their modules as a point of entry into clients who will later buy more of their products. They may be right, but I wonder how many companies that buy best-of-breed components will reverse course by favoring components from a single source. What’s certain is that this approach exposes the CDP vendors to competition from point-solution specialists in each area while discarding the advantage of that comes from purchasing a CDP with a full range of pre-integrated functions. Here's a sampling of that competitive landscape:<br /></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjD7l8-o52T5NPqeTBPlGBnGD2YE3uqc1YSpZc3JvXutjmWz8FF2Unbnvhr7OrgMegaGhj4BXKu7Xoa4tf54is9kfPfPg1wwz79kF-tMfQhIjoAXKIj1i93Bh5fvB3OsXYIVXCMu_V2MnqgFUD6pxq9qXweUBy6KdyBQwZyEZB8WmMQ8vH9WA=s3909" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="2100" data-original-width="3909" height="288" src="https://blogger.googleusercontent.com/img/a/AVvXsEjD7l8-o52T5NPqeTBPlGBnGD2YE3uqc1YSpZc3JvXutjmWz8FF2Unbnvhr7OrgMegaGhj4BXKu7Xoa4tf54is9kfPfPg1wwz79kF-tMfQhIjoAXKIj1i93Bh5fvB3OsXYIVXCMu_V2MnqgFUD6pxq9qXweUBy6KdyBQwZyEZB8WmMQ8vH9WA=w536-h288" width="536" /></a></div><p>
I also suspect that companies like Snowflake, Amazon Web Services, and Google Cloud Services, which now position themselves as providing one piece of a “composable” solution, will eventually add features that match what the independent component providers now offer. Actually, that’s already happening, so I don’t get much credit for predicting it. It’s a dynamic we’ve seen repeatedly in other markets: primary vendors expand their features to secure their position with clients by adding more value (hooray!) and increasing the cost of switching (boo!).
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Let’s be clear: both the added value and the switching costs are the result of integration cost. No matter how many promises are made about easy integration, the fact remains that any non-trivial connection between two systems takes skill to plan, deploy, and maintain. Integration is often the top-ranked vendor selection criterion in surveys, which some see as showing that problem is well understood. I draw the opposite conclusion: people list integration as a consideration because they know it’s poorly understood. This forces them to invest time in trying to avoid integration problems and even sacrifice other benefits to achieve it. If integration were really easy, no one would worry about it.
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Right now, someone reading this is saying, “Ah, but no-code changes everything”. I don’t think so. No-code works best when automating simple processes with a few users where flaws are acceptable. The more complex, widely-deployed, and mission-critical a process is, the more important it is to deploy professional-grade design and quality control. Prebuilt components doesn’t change this: configuring those components and connecting them to each other still takes great care and understanding.
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This isn’t (just) a cranky-old-man digression. CDP functions rank high in complexity, scale, and risk, so they are poor candidates for no-code development. CDPs certainly can have no-code interfaces that empower business users to do things that might otherwise require a developer. But those interfaces will control carefully defined and constrained tasks, not create core functionality. Assembling CDP-equivalent systems from composable functions is a different matter, and, yes, that should become increasingly possible for people with the right integration skills. What I doubt is that selling modules with those functions will be good business for CDP vendors: they are likely to commoditize their products and ultimately to be pushed aside by platform developers who integrate key functions directly.
</p><p>I'm not saying that CDP vendors who can’t raise several hundred million dollars are doomed. I am saying that most will have to pick a niche to succeed. One promising option is building customer data profiles, especially for big enterprises. It’s a single function that incorporates enough separate components for CDP vendors to provide value by avoiding integration costs.
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The other big niche, or set of niches, is integrated customer experience solutions. Our latest report already shows systems that campaign and delivery CDPs, our name for systems that do this, account for two-thirds of the industry vendor count and funding, and nearly three-quarters of employment. Their actual share may be greater still: immediately after completing the latest report, I happened to glance at <a href="https://www.g2.com/categories/customer-data-platform-cdp#grid" target="_blank">G2 Crowd’s list of CDPs</a> and found our reports doesn't include several large, fast-growing retail marketing automation or messaging specialists (Insider, Listrak, SALESmanago, Klaviyo, and Ometria) that offer what looks like CDP-grade multi-source profile building.
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Whether those vendors are true CDPs depends on whether they make those profiles available to other systems. Either way, the point is that there’s a large and growing market for cross-channel retail marketing systems with unified customer profiles at their core. There are similar markets outside of retail, where we already see specialist campaign and delivery CDPs in hospitality, financial services, telecommunications, healthcare, education, and elsewhere.
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The value of industry-specific systems is, once again, reduced integration cost. In this case, the key integration is with industry-specific operational systems such as airline reservations, core banking, phone billing, health records, and student management. Vendors in these niches compete primarily on the marketing functions they offer, which makes them more departmental than enterprise solutions and pushes them to add marketing features tailored to their particular industry. Systems like this are hard to dislodge once they’re deployed because they are populated with many complex, difficult-to-replicate campaigns, reports, predictive models, and content libraries. This stickiness is what enables many successful vendors to co-exist in each niche, and what makes it hard for non-specialists to enter.
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The division of the CDP industry into enterprise-level data CDPs and industry-specific, department-level campaign and delivery CDPs is not a new trend. What is new is the maturity of the competitors within many niches, which will make it increasingly difficult for new entrants to succeed. What’s also new is that building CDP-style customer profiles is increasingly common, making it a standard feature rather than a product differentiator. This encourages vendors to position themselves as something other than a CDP, even though they need to show buyers that their CDP features are first-rate.
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My final conclusion is this: the CDP industry will continue grow, and it will remain important for buyers to find the right CDP, even as the CDP itself slips from the spotlight.
</p>David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-55327559177520531152021-11-21T14:38:00.002-05:002021-11-21T14:39:37.602-05:00Build vs Buy in a Nutshell<p> <img alt="" height="504" 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" width="656" /></p>David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-44318143543326035472021-06-27T17:13:00.007-04:002021-07-28T13:45:14.217-04:00Where Do Low-Code and No-Code Fit in the Build vs Buy Debate?<p>I thought it might be my imagination, but Google Trends confirms that “build vs buy” really is coming up more often these days that it had in recent years. </p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgj-Sw74FKSTNRoh_ufg7OqUlLzlW48JC7pp5zVUbiIVvDh5HYmjBsZxylYq1XYCjf-MHMIBULioEl2rG7b-InY2ZEi59-1TFT3MiXRU2m4AEAR0Ry_OsN3ILoglpBbYMh2jTWR/s752/Google+Trends+Buy+Buy.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="452" data-original-width="752" height="257" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgj-Sw74FKSTNRoh_ufg7OqUlLzlW48JC7pp5zVUbiIVvDh5HYmjBsZxylYq1XYCjf-MHMIBULioEl2rG7b-InY2ZEi59-1TFT3MiXRU2m4AEAR0Ry_OsN3ILoglpBbYMh2jTWR/w428-h257/Google+Trends+Buy+Buy.png" width="428" /></a></div>This surprises me, since I had thought that debate was over. It seemed that most organizations had accepted the default position of buying when possible and building only when necessary.<p></p><p>
In the world of customer data management, I’d say the reason for the new interest in building systems is that corporate IT is more involved than before. Remember the growth in marketing and sales technologies was due mostly to the fact that marketing and sales teams could often buy what they wanted as a Software-as-a-Service subscription, with little or no involvement of the central IT team. Marketing and sales leaders had no interest in building software, and even martech and salestech groups were more oriented to selecting and implementing systems than building their own. But as customer experience rose to become a primary corporate priority, a trend accelerated by the pandemic, it became more important to integrate marketing and sales systems with the rest of the company. Privacy rules created a parallel pressure for corporate-level involvement. And corporate IT groups are much more likely to at least consider building their own system.</p><p>
It’s a less obvious why interest in build vs buy should have also grown in the broader IT world. One possibility is that the growth of low-code and no-code options made building your own systems more attractive alternatives. I can’t really say the Google Trend data bear this out, since the dip in build vs buy interest happened after the initial growth in low-code development. The correlation is better for no-code although there are probably other, equally plausible explanations. </p><p>
What’s more important is that no-code and low-code do change the terms of the build vs buy debate. One way to think about this is to break the software development process into four main components and see who does what under each approach. Those four components are:</p><ul style="text-align: left;"><li>requirements definition: this is identifying system goals and what functions it needs to reach those goals. It is (or should be) done by users regardless of the development approach.</li></ul><ul style="text-align: left;"><li>process creation: this is the design and building of the system processes that meet the requirements. It’s the core of the development process. It’s done by IT under “build” and “low-code” approaches, by the users themselves under “no-code”, and by the software vendor under “buy”.</li></ul><ul style="text-align: left;"><li>platform creation: the is the development and maintenance of the structures that support the system processes. It doesn’t really have a separate existence in “build” or “buy” approaches, where it’s tightly intertwined with process creation. But it’s distinctly separate in “no-code” and “low-code”, since it’s purchased from the software vendor rather than built by either users or IT. (Note: even “built” systems rely on purchased technology, such as operating systems and database engines. We can ignore that for present purposes.)</li></ul><ul style="text-align: left;"><li>integration: this is connecting the system with other corporate systems. It’s always done by the IT department, which often argues it’s more work to integrate a purchased system than to connect a home-built one. (In many cases, they’re probably right.)</li></ul><p style="text-align: left;">
You can visualize the four development approaches as a column chart showing the distribution of work by department: <br /></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi8lUFTc0eFbUh3M8Ho4_5OyHw7x4TsJtbkGR5Zh9cRabLJJaYxGchnjs5iVE8aV_ONz61XJDsnDx1GatW7oNIdgNlE7xPvT22ietaY0pr8CGKnvZ_90NCbzx12sK9hT4PRT9Uu/s629/Build+vs+buy2.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="405" data-original-width="629" height="258" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi8lUFTc0eFbUh3M8Ho4_5OyHw7x4TsJtbkGR5Zh9cRabLJJaYxGchnjs5iVE8aV_ONz61XJDsnDx1GatW7oNIdgNlE7xPvT22ietaY0pr8CGKnvZ_90NCbzx12sK9hT4PRT9Uu/w400-h258/Build+vs+buy2.png" width="400" /></a></div><p></p><p></p>This diagram highlights several points worth considering:<p></p><ul style="text-align: left;"><li>users are always responsible for defining their requirements. That should go without saying, but there’s sometimes an assumption that a highly-rated package will have all the features they want, so there’s no need to define their requirements or check out the package in detail. In fact, it’s probably the most common mistake that people make when buying software. It applies equally to “no-code” systems, where a particular required feature may not be built into the system’s capabilities. Best to know that in advance.</li></ul><ul style="text-align: left;"><li> IT is always involved. If nothing else, they’ll do the integration work. In practice, they’ll also be vetting any purchased system against security, privacy, reliability, and other standards. This is another thing that should go without saying, but they should be part of the any process from the start. </li></ul><ul style="text-align: left;"><li>The real battle is over process creation. This is where either users, vendors, or IT may end up doing the work. The key question then becomes which group is best equipped to handle this task for any particular project. If the processes are mostly limited to individual users, who may each have their own preferred way of doing things, then letting the users define their own process with no-code makes sense. If the processes are shared by many users and fairly standard for the industry, a purchased package will likely work the best. If the processes are unique to the company, but not exceptionally odd, a low-code package should work. If the processes require capabilities that won’t be well supported in a low-code package, then custom-building the system is probably the best choice. </li></ul><p style="margin-left: 40px; text-align: left;">This thinking is summarized in the matrix below, which proposes that the appropriate choice depends on two binary variables: process variety (is the process the same for all users or do they each want their own) and process difficulty (either defined in terms of absolute complexity, or of how unique the process is to the company compared with others in the industry). </p><p style="margin-left: 40px; text-align: left;"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhZoKiabGCRHpdVgSXXsng9gtuUoB81NaD4ycjho97Mt3e8nlYA7pwadkZulg9RMnA7KiUBHJdPJf-_zA-2HyB8ZPKgx9fc3lqPu-qZ1wOVllhxObtBP_96oVr5paCLhhdAvW47/s712/build+vs+buy+matrix.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="332" data-original-width="712" height="186" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhZoKiabGCRHpdVgSXXsng9gtuUoB81NaD4ycjho97Mt3e8nlYA7pwadkZulg9RMnA7KiUBHJdPJf-_zA-2HyB8ZPKgx9fc3lqPu-qZ1wOVllhxObtBP_96oVr5paCLhhdAvW47/w400-h186/build+vs+buy+matrix.png" width="400" /></a></div><br /><div style="margin-left: 40px; text-align: left;">The underlying logic of the matrix is that packaged systems, whether no-code or bought, make the most sense when the underlying processing is simple or common to the industry, while IT involvement via low-code or build is required to create complex or unique processes. When IT is involved, the productivity advantages of low-code are most important where individualized processes mean a great deal of customization is needed. The productivity advantages of low-code are less critical where there is one shared process to create, and any limits on the complexity imposed by low-code may be more important. (I’m not entirely convinced this matrix is correct, but it’s a good starting place for discussion.)</div><p></p><ul style="text-align: left;"><li>The magnitude of each component has an unclear role in the choice. It might seem that projects with very complex integration requirements should be built by IT, while those with extensive user requirements should let users create their own no-code solutions. But if a purchased platform supports the integration needs, their complexity doesn’t matter. Similarly, if the extensive user requirements are well understood and stable, it’s a waste of time for users to build them for themselves. If anything, extensive and stable user requirements would favor a purchased system, assuming the system is already meets them.</li></ul><p>
Beyond these specific propositions, I think it’s generally helpful to break a project into these components when thinking about the right development approach. They’re all important and are different enough to treat separately to ensure they’re all considered. If you have other thoughts on how to apply them, or can offer a better approach, please share your comments.</p>
David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-59219708973070972892021-05-07T09:29:00.001-04:002021-05-07T09:29:14.528-04:00Seers Offers Easy-to-Use Cookie Consent<p>The growth in global privacy regulation has created an immense headache for thousands of businesses – and, thus, an immense opportunity for systems that offer relief. Small businesses in particular need simple, low-cost solutions to comply with rules that require gathering consumer consent to data collection and giving consumers access to data that’s been collected. An ideal small-business solution leads users through the set-up process without requiring technical skills or expertise in privacy rules. (Come to think of it, so does an ideal large-business solution.)<br /></p><p><a href="https://www.seersco.com " target="_blank">Seers</a> is one of many vendors addressing this market. Its core products are systems to collect cookie consent and data access requests. It supplements these with products for access request fulfillment, data privacy impact assessments, GDPR compliance assessment, data breach reporting, policy creation, data discovery, and on-demand privacy training. Several of the products are engineered to support mid-size and large enterprises as well as small business.
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Set-up of the Seers consent manager follows step-by-step process. It starts with the user specifying the domain to be supported. The system then automatically scans this domain to identify the cookies and scripts currently installed. It will later compare the results to a list of hundreds of cookies that Seers has evaluated and classified, and generate a list of the specific cookie consent requests the site must present to visitors. Before this step, users set preferences for the cookie banner appearance, cookie policy URL, treatment of unconsented visitors, and other options. The interface includes handy explanations of each item so that users can make informed choices. The system will detect site visitor location and can use this to present consents in 30 local languages and in different formats for GDPR, California’s CCDP, and Brazil’s LGPD.
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Once settings are complete, Seers will generate code to insert in the user’s Web site to deploy the consent system. It will then keep track of consents as these are received, providing an audit trail should documentation be needed. The system will regularly rescan the user’s Web site to identify the cookies currently in use and adjust the cookie consent table accordingly. A limited free version is available and the full module starts at $9 per month for a single Web domain.
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Seers’ other main customer-facing module is Subject Request Management, which offers a portal that lets customers ask to see, change, or delete data a company holds about them. This is similarly easy to configure, letting users control the appearance, identify verification requirements, and other options. It feeds requests into a queue which lets users manually assign them to departments and individuals for resolution, tracks their status, and stores notes and attachments. Again, a limited free version is available while a single-user full version costs just under $50 per year.
</p><p>Seers also offers a large number of interactive templates, assessments, and policy generators. These lead users through processes including data privacy impact assessment (DPIA), privacy policy creation, and GDPR compliance assessment. The ones I saw were all easy to follow and included impressive amounts of information. The privacy impact assessment module is priced at $46.99 per year while most of the other tools are bundled into a package starting at $129.99 one-time fee.
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So far so good. I really liked what I saw from Seers. But there are gaps in its product line that mean most companies would need additional products for a complete solution. The company is closing one gap with a data discovery tool, now in beta and set for July release, which will let users build an inventory of personal data is stored in its systems. The first release, at least, will be limited to having users review field names and mapping these to standard categories. One nice touch is that the inventory will connect with data subject requests, so the system will be able to automatically pull information about an individual. But field names are not an entirely reliable source of information and Seers does not have the data scanning capabilities of a system like BigID.
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Other gaps include consent management beyond cookie consent; records of processing activity (ROPA) reports; ensuring that processing is legally justified; monitoring vendors who process company data; and automatic policy updates as rules change. Whether you need these depends on what other resources you have available. But they’re all required under current privacy regulations.
</p>David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-82438254105300026042021-05-02T20:07:00.001-04:002021-05-02T20:14:48.276-04:00Build vs Buy Your Customer Data Platform?<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjyRJSzlLhYzh0J1bBCqJfmqoXHACRpf0W15DFTVxs6DRBKoh4XvdajL2NYfWI1IeGG0lJ293bJnf_xHr4ka8NU0ByItG5z8QhkZzzh9KPUq5NEWjVyzgjTFEZjXWpATO8ax6by/s315/build+vs+buy.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="225" data-original-width="315" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjyRJSzlLhYzh0J1bBCqJfmqoXHACRpf0W15DFTVxs6DRBKoh4XvdajL2NYfWI1IeGG0lJ293bJnf_xHr4ka8NU0ByItG5z8QhkZzzh9KPUq5NEWjVyzgjTFEZjXWpATO8ax6by/s0/build+vs+buy.png" /></a></div>The build vs buy debate has existed as long as packaged software itself. Any serious discussion quickly concludes that there’s no one right answer and real question is when to do one or the other. That discussion, in turn, usually leads to a recommendation that companies build software which will create unique competitive advantage and otherwise buy when a satisfactory option exists. The implicit assumption behind that recommendation is that buying is cheaper than building. This isn’t always true but it will apply in most cases, especially when cost calculations include staff cost, on-going maintenance and feature updates, the risk of project failure or under-performance, and the opportunity cost of not using scarce developers to build other systems that do create unique advantages. <p></p><p></p><p>But the discussion changes when Customer Data Platforms are involved.* I’ve recently heard pro-build arguments that raise other valid issues. The two big ones are: </p><ul style="text-align: left;"><li>most of the work in deploying a CDP is in data collection, which includes identifying source systems, understanding their contents, deciding which elements to include, and defining transformations to make the data usable. This work is the same whether you’re building or buying. Since it accounts for the bulk of the project cost, the cost to build or buy can’t be very different. </li></ul><ul style="text-align: left;"><li>many companies (especially big ones) already have systems that do much of what a CDP is intended to offer. In these situations, the incremental cost to extend existing systems will be less than the cost of adding a separate CDP, which will unnecessarily (and expensively) duplicate many existing functions. </li></ul><p>Both arguments attack the “buying is cheaper” assumption. Neither should be summarily dismissed. Rather, let’s fit them into a larger framework that looks at more factors to consider in a CDP build vs buy decision. To make things manageable, this framework identifies items that are the same for both, favor build, and favor buy, and groups them based on whether they apply to data collection, processing, or outputs. The table below summarizes my list; I’m sure there are others. </p>
<table border="1" cellpadding="1" cellspacing="1">
<tbody>
<tr>
<td> </td>
<td>same for both<br /></td>
<td>build advantage</td>
<td>buy advantage</td>
</tr>
<tr>
<td>data collection</td>
<td>source analysis</td>
<td>existing connectors</td>
<td>prebuilt connectors</td>
</tr>
<tr>
<td>processing</td>
<td>define requirements</td>
<td>less redundancy, <br />incremental development,<br /> custom features for <br /> competitive advantage</td>
<td>prebuilt processes,<br /> more mature,<br /> continuous updates, <br />lower risk</td>
</tr>
<tr>
<td>outputs</td>
<td>define requirements</td>
<td>existing connectors</td>
<td>prebuilt connectors</td>
</tr>
</tbody>
</table>
<p>
</p><p></p><p></p><p>Exploring this in more detail: </p><ul style="text-align: left;"><li><b>Data Collection/Same:</b> as already mentioned, much of the work in assembling a CDP is understanding source data. This is required regardless of whether the CDP is built or bought. If the data is well understood, a purchased CDP benefits as much as a built system – so long as IT staff who understand the data are available to the project. </li></ul><ul style="text-align: left;"><li><b>Data Collection/Build Advantage:</b> a built CDP will take advantage of whatever connectors have already been created to feed existing systems. Note that any relative advantage is diminished if a purchased CDP can also use these connectors, either to create direct feeds or by reading data the connectors have pulled into existing data lakes or warehouses. That should be true in most cases.<br /></li></ul><ul style="text-align: left;"><li><b>Data Collection/Buy Advantage:</b> a purchased CDP will have prebuilt connectors for many source systems and often a standard API for creating new connectors. The value of this depends on how many of your company’s systems are covered, which is likely to depend on how modern they are. </li></ul><ul style="text-align: left;"><li><b>Processing/Same: </b>both built and purchased systems depend on effective requirement definition, another critical task that can consume substantial project resources. There may be some additional work bringing vendor staff up to speed for a purchased system, compared with having a system built by internal staff who already understand the business. But this is probably balanced by CDP vendor staff having deeper experience with CDP-specific issues. </li></ul><ul style="text-align: left;"><li><b>Processing/Build Advantage:</b> extending existing systems may mean that existing data stores can expanded, rather than copying data into a separate CDP database. This is especially important for companies with massive data volumes. But the actual advantage depends on the technical details, since the CDP often needs data placed into a different format from existing systems, in which case both build and buy solutions will require a new data store. </li></ul><p style="margin-left: 40px; text-align: left;">A built system will only add features that are not already available in existing systems, so there’s less potential redundancy compared with a purchased system. This could mean lower operating costs but, again, it depends on how much of what the CDP does is really new. And there's a reasonable chance a purchased CDP will actually reduce operating costs by enabling the company to sunset some existing systems or processes.<br /></p><p style="margin-left: 40px; text-align: left;">A built system can include features that are not available in a purchased CDP, creating unique competitive advantage. How much this matters will depend on how unique the company’s requirements really are, and whether a purchased CDP can also be extended to meet them. CDP vendors would argue that their systems are extremely flexible and extensible. </p><ul style="text-align: left;"><li><b>Processing/Buy Advantage:</b> a purchased CDP will have core CDP processes already built, saving the cost of custom development. This is probably the strongest argument for a purchased system. Of course, it depends on how many new processes are needed and how hard it would be for the company to build them on its own. </li></ul><p style="margin-left: 40px; text-align: left;">A purchased system will also include advanced features that wouldn’t be delivered in early versions of a built system, which will inevitably focus on meeting basic requirements as quickly as possible. It could easily take years for the in-house system to catch up with the refinements of a mature purchased product, and the purchased product will also be improving during that time. There’s a reasonable argument that a purchased CDP is likely to add features even before any particular company knows it needs them, in which case it would be impossible for the built CDP to ever meet user needs as quickly as the purchased product. One caution: purchased CDPs themselves vary greatly in their maturity, so this will apply more to some than others. </p><p style="margin-left: 40px; text-align: left;">Build and buy choices both have their risks. There’s always a chance that a purchased system won’t perform as expected, won’t evolve to meet future needs, or will be discontinued if its developer runs into business problems. But these risks can be limited through careful vendor selection and contracts. By contrast, development failures for custom software are almost the norm: industry lore is filled with high-priority projects that ran over time and over budget and still failed to meet expectations. The risk is greater for systems like CDPs, which have requirements that are less familiar to many corporate IT groups than operational systems like order processing or CRM. So, on balance, I think it’s fair to say that built solutions are higher risk – even though I realize that many in-house IT teams would disagree. Perhaps we can all agree that this is something to be assessed on a case-by-case basis. </p><p style="margin-left: 40px; text-align: left;">In making all these assessments, it’s important to look at the full scope of long-term CDP requirements. It might be relatively easy to extend existing systems to meet a handful of initial requirements, but there would then be a backlog of further enhancements that would each require additional investments. A purchased CDP should deliver a much broader set of features from the start and within its original purchase price. </p><ul style="text-align: left;"><li><b>Outputs/Same:</b> again, the work to define output requirements will be pretty much the same whether a system is built or bought. </li></ul><ul style="text-align: left;"><li><b>Outputs/Build Advantage:</b> existing systems may have connectors in place to deliver outputs to company reporting, marketing, messaging, analytical, and other systems. This is especially helpful if the targets are legacy systems that are difficult to work with. As with inputs, a purchased CDP should also be able to take advantage of many existing connectors, so the net advantage for built systems is limited. </li></ul><ul style="text-align: left;"><li><b>Outputs/Buy Advantage:</b> as with data collection connectors, purchased CDPs will have a library of prebuilt connectors for output systems. This could save considerable effort, especially if the CDP project requires large numbers of connections that don’t already exist and the CDP vendor can provide them. </li></ul><p>Summing all this up: some issues, such as data preparation, are less relevant to the build/buy choice than it might seem. The main factor driving the decision is the incremental work needed to build and maintain an internal solution compared with the cost of adding a purchased system. If existing systems can meet CDP requirements with relatively few changes, a built solution makes sense. If a major development project is needed, it’s probably better to buy. Because CDPs are inherently flexible, it’s unlikely that a built solution will truly provide any competitive advantage that a purchased CDP cannot duplicate with the same or less development effort. </p><p>One important caveat to all this is that build vs buy is less a choice than a continuum. Even built solutions rely heavily on purchased components, such as data storage platforms, function libraries, and external services (e.g. third party identity resolution). Many purchased CDPs use exactly the same tools. To the degree that builders can rely on purchased tools, they get the same benefits of using pre-built components that they would get from a purchased CDP. </p><p></p><p>The tools available for purchase continue to improve: platforms like Google Cloud keep adding new CDP-supporting services; databases like Snowflake make it easier to manage CDP data structures; applications like Rudderstack and Informatica provide complex process flows. But assembling a functional CDP will never be as simple as snapping these together like the proverbial Lego Blocks. Then again, deploying a CDP also takes more than just plugging it in. </p><p>What matters is that the tools keep getting better, meaning the cost of building is reduced. At the margin, this shifts the balance towards built systems, at least for companies with the resources to use the tools effectively. But in many cases – perhaps the vast majority – a purchased CDP still makes the most sense. </p><p>In other words: it depends. <br /></p><p>_____________________________________________________</p><p>* <a href="https://www.cdpinstitute.org/cdp-basics" target="_blank">CDP Institute defines</a> a CDP as "packaged software that creates a persistent,
unified customer database that is accessible to other systems"." This means that, strictly speaking, there is no such thing as a custom-built CDP. But we'll use "CDP" here to refer to any system that performs the functions listed in the definition: "creates a persistent, unified customer database that is accessible to other systems." In practice, many home-built "CDP" systems won't be fully accessible to other systems, either. We'll ignore that here but note that ease of connecting with new systems is one of the advantages of buying rather than building.<br /></p><p> </p>David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-66602378161124237462021-03-16T15:33:00.003-04:002021-03-18T18:36:56.518-04:00ActiveNav Automates Data Inventory Updates<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjI50MDgT90C8qm7Ivqkfv3TyyQ9ilpbo2hvA-47crrP1w55OKXFuJVyoGi32YC17qdWP-GxscQJEvwNCndXm1Wxko2d-PcLQLeglOiyTg_PEFMx0eNGZQS2Z626rnv3Oqyd2TA/s621/Robot+Inventory.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="516" data-original-width="621" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjI50MDgT90C8qm7Ivqkfv3TyyQ9ilpbo2hvA-47crrP1w55OKXFuJVyoGi32YC17qdWP-GxscQJEvwNCndXm1Wxko2d-PcLQLeglOiyTg_PEFMx0eNGZQS2Z626rnv3Oqyd2TA/s320/Robot+Inventory.png" width="320" /></a></div>Our on-going tour of privacy systems has already included stops at <a href="https://customerexperiencematrix.blogspot.com/2020/08/software-review-bigid-for-privacy-data.html" target="_blank">BigID</a> and <a href="https://customerexperiencematrix.blogspot.com/2020/11/trust-hub-maps-company-data-for-privacy.html " target="_blank">Trust-Hub</a>, which both build inventories of customer data. Apparently I’m drawn the topic, since I recently found myself looking at <a href="https://activenav.com/" target="_blank">ActiveNav</a>, which turns out to be yet another data inventory system. It’s different enough from the others to be worth a review of its own. So here goes.
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Like other inventory systems, ActiveNav builds a map of data stored in company systems. In ActiveNav’s case, this can literally be a geographic map showing the location of data centers, starting from a global view and drilling down to regions, cities, and sites. Users can also select other views, including business units and repository types. The lowest level in each view is a single container, whose contents ActiveNav will automatically explore by reading metadata attributes such as field names and data formats.
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The system applies rules and keywords to the metadata to determine the type of data stored in each field, without reading the actual file contents. (A supplementary module that allows content examination is due for release soon.) It stores its findings in its own repository, again without copying any actual information – so there’s no worry about data breaches from ActiveNav itself.
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One disadvantage of ActiveNav’s approach is that relying only on metadata limits the chances of finding sensitive information that is not labeled accurately, something that BigID does especially well. Similarly, ActiveNav doesn’t map relations between data stored in different containers, so it cannot build a company-wide data model. This is a strength of Trust-Hub.
</p><p>
Still, ActiveNav’s ability to explore and classify data repositories without human guidance is a major improvement over manually-built data inventories. Its second big benefit is a “data health” score based on its findings. This is calculated for each container with scores for factors including: risk, including intellectual property and security issues; privacy compliance, based on presence of IDs and other data types; and data quality, including duplicate, obsolete, stale and trivial contents. Scores for each container are combined to create scores for repositories, locations, business units, and other higher levels. This gives users a quick way to find problem areas and track data health over time.
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ActiveNav addresses what may be the biggest data inventory pain of all: keeping information up-to-date. The system automates the update process by receiving continuous notifications of metadata changes from systems that are set up to send them. In other cases, ActiveNav can query repositories to look for metadata that has been updated since its last visit. Of course, this requires providing the system with credentials to access that information.
</p><p>
ActivNav was founded in 2008. Until recently, it offered only a conventional on-premise software license with one-time costs starting around $100,000. This is sold this primarily through partners who work on data management projects for heavily regulated industries and governments. The company has recently introduced a SaaS version of its data inventory system that starts at $10,000 per year. It also offers data governance and compliance modules.
</p>David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-882345973135082312021-02-17T18:31:00.005-05:002021-08-16T11:58:34.721-04:00Is Peak Martech Approaching At Last?<p></p><div class="separator" dir="rtl" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiarEcTDtEeXoz8AAQfQ0LCE9iKSNtphYkpjEyJWztJYQah6ZuT7-flQvdY4aJcnX_-PkYZ7DMBfD1K2ZaCXTMxceHx4fUjzpzKdZm-7VIfawdY0LuL4lBdHyzkLn0CKZrgd-Va/s512/Greek+Oracle.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="295" data-original-width="512" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiarEcTDtEeXoz8AAQfQ0LCE9iKSNtphYkpjEyJWztJYQah6ZuT7-flQvdY4aJcnX_-PkYZ7DMBfD1K2ZaCXTMxceHx4fUjzpzKdZm-7VIfawdY0LuL4lBdHyzkLn0CKZrgd-Va/s320/Greek+Oracle.png" width="320" /></a></div><br />Contrary to popular belief, forecasting is easy: tomorrow is nearly always like today. What’s hard is predicting when something will change: a snowstorm, stock market crash, or disruptive technology. Of course, predicting change is also where forecasting is most useful.
<p></p><p>
In marketing technology, we’ve seen a long succession of sunny days. Every year, the number of systems grows, fed by a proliferation of channels, declining development costs, and easily available funding. The safe bet is the number will grow next year, too. But I think one day soon the pendulum will reverse direction.
</p><p>
I can’t point to much data in support of my position. Surveys do show that<a href="https://www.gartner.com/en/marketing/research/2020-marketing-technology-survey " target="_blank"> marketers don’t use the full capabilities of their existing stacks</a>, which might mean they’re inclined to take a break before making new purchases. But marketers have never used every feature in their old systems before buying new ones. The pandemic probably led to a temporary <a href=" http://10fold-6173816.hs-sites.com/cmosurvey13-pr" target="_blank">dip in martech purchases </a>
but <a href="https://93x.agency/b2b-technology-marketing-trends-in-2021/ " target="_blank">budgets appear to be opening up again</a>. So the appetite for new martech will likely reappear. </p><p></p><p>(Update, August 2021: a <a href="https://www.mulesoft.com/lp/reports/connectivity-benchmark" target="_blank">Mulesoft survey</a> released in August 2021 did show the average number of apps used by large enterprises had fallen from a peak of 1,020 in 2018 to 843 in 2021, and <a href="https://www.gartner.com/en/marketing/research/annual-cmo-spend-survey-research" target="_blank">Gartner's July 2021 CMO Spend Survey</a> reported a sharp drop in marketing spending from 11% of company revenue in 2020 to 6.4% in 2021, which would imply a drop in martech spending too. I'm skeptical of the Gartner data but both studies support the idea that martech proliferation may decrease.)<br /></p><p>My prediction is based less martech trends than a general impression that many people feel the world is spinning out of control and want to rein it in. Tech in particular is having impacts that no one fully understands. Concerns about disinformation, social media-induced radicalization, lost privacy, and biased artificial intelligence are all part of this. Even in the narrower spheres of martech and adtech, many users feel their systems are too complicated to really understand. Worries about ad fraud, ads appearing in the wrong places, inappropriate personalization, and unintended campaign messages all come down to the same thing: people worry their systems are making unseen bad decisions.
</p><p>
Technologists tend to feel the cure is more technology: smarter AI, systems checking on other systems, and democratized development to let more people build systems for themselves. But there’s an air of hubris to this. Stories from Daedalus to Frankenstein to Jurassic Park warn us advanced technology will ultimately destroy its creators. Every data breach and wifi outage reminds us no technology is entirely reliable and fixing it is beyond most people’s control.
</p><p>
As a result, non-technologists increasingly doubt that technology can solve its own problems. Some people will bury their worries, accepting technology’s risk as the price for its benefits. Others will take the opposite extreme, rejecting technology altogether, or at least to the great degree possible.
</p><p>
But there’s a middle ground between blindly surfing the net and leaving the grid entirely. This is to consciously seek technology that’s simpler and more controllable than current extremes, even if it’s also less powerful as a result. The key is willingness to make that trade-off, which in turn implies willingness to invest the effort needed to assess the relative value vs. risk of different technical options.
</p><p>
Making that investment is probably the biggest change from the current default of accepting technical progress as inevitable and trusting the technologists to appropriately balance risk against rewards when they decide which products to release. In many cases, the cost of assessment will probably be higher than the cost of using the diminished technology itself: that is, the difference in value between a more secure system and a less secure one may be less than the value of the time I spend comparing them. This means the main cost of making this adjustment isn’t the lost value from using safer technology, but the cost of assessing that technology.
</p><p>
In theory, the assessment cost might be reduced by splitting it among many people who would share their results. But here’s where trust comes back into play: if you can’t trust someone else to do accurate research, you can’t decide based on their results. Since loss of trust is arguably the defining crisis of today’s society, you can’t just wave it away with an assumption that people will trust others’ assessments of technology tradeoffs. Rather, the need will be to build technology that is self-evidently understandable, so that each person can assess it for herself. This will reduce the assessment cost that blocks them from choosing simpler solutions.
</p><p>
So here’s where I think we’re headed: away from ever-increasing, and increasingly opaque, technical complexity, and towards technology that’s simpler and more transparent. Remember: simplicity is the goal, and transparency is what makes it affordable. I call this the “new pragmatism”, although I doubt the label will catch on. As the word “pragmatism” suggests, it’s rather boring and a lot of hard work. But compared with the chaos or authoritarianism that seem to be the main alternatives, it’s about the best way we can hope our current chapter will end. After we turn the page, people may later learn to rebuild the presumption of trust that enables non-verifiable relationships.
</p><p>
If you’re still reading this, thanks for your indulgence; I know you don’t come to this blog for half-baked social theories. But these ideas do have direct implications for marketing and martech. If I’m right, both consumers and martech buyers will want simpler, more transparent products. For marketers, this means:
</p><li style="margin-left: 40px; text-align: left;">The time may finally have come when stripped-down versions replace feature-rich products, with a stress on ease of use rather than power. I know this idea has been tried before without success. But that was during the earlier age of techno-optimism.
</li><div style="margin-left: 40px; text-align: left;"><br /></div><li style="margin-left: 40px; text-align: left;">Buyers may be more interested in products whose actual operation is transparent. This will usually mean status indicators, meters, and diagnostics to show’s happening. In some cases may literally mean see-through designs that let users watch, say, as the dishes are cleaned or the vacuum bag fills with dirt. Whatever it takes for a feeling of control.</li><div style="margin-left: 40px; text-align: left;"><br /></div><li style="margin-left: 40px; text-align: left;">Privacy will continue to gain importance, with particular emphasis on systems that are private by design rather than user choice. Privacy policies and options are poorly understood and mistrusted, so many consumers would rather buy a system that makes them unnecessary because it can’t collect data or connect to the internet. Of course, they need to be confident the system behaves as promised.
</li><div style="margin-left: 40px; text-align: left;"><br /></div><li style="margin-left: 40px; text-align: left;">Marketing messages should also switch from promoting advanced technology to promoting simplicity, reliability, and clarity. Explanations about what’s inside a product, in terms of the technology, design and manufacturing processes, materials, and people may be more important to buyers looking for reasons to trust.
</li><div style="margin-left: 40px; text-align: left;"><br /></div><li style="margin-left: 40px; text-align: left;">Marketing methods should match the claims, avoiding unnecessary personalization and staying away from mistrusted media. This is a tricky balance because few marketers will want to sacrifice the performance benefits that come from data-driven targeting. But they do need to weigh long-term brand value against short-term campaign results. For what it’s worth, relying more on basic branding and less on advanced technology is itself consistent with the return to simplicity.
</li><p>
Martech vendors will want to make all these adjustments in their own marketing. Other considerations include:
</p><ul style="text-align: left;"><br /><li>
Artificial intelligence must be understandable. It’s tempting to suggest discarding AI altogether, since it may be the ultimate example of complicated, opaque, and ungovernable technology. But the apparent benefits of AI are too great to discard. The pragmatic approach is to demand proof that AI really delivers the expected benefits. Then, assuming the answer is yes, find ways to make AI more controllable. This means building AI systems that explain their results, let users modify their decisions, and make it easy to monitor their behaviors. These are already goals of current AI development, so this is more a matter of adjusting priorities than taking AI in a fundamentally different direction.
</li><br /><li>
Reconsider the platform/app model. This may be blasphemy in martech circles, since the martech explosion has been largely the result of platforms making it easier to sell specialized apps. But the platform/app model relies on trust that apps are effectively vetted by platform owners. If that trust isn’t present, assessment costs will pose a major barrier to new app adoption. At best, buyers with limited resources (which is everyone) would be able to afford fewer apps. At worst, people will stop using apps altogether. So the pragmatic approach for platforms and app developers alike is to work even harder at trust-building. We already see this, for example, in Apple’s new requirements for data privacy labels and tracking consent rules. https://developer.apple.com/app-store/user-privacy-and-data-use/ What Apple hasn’t done is to aggressively audit compliance and publicize its audit programs. The dynamic here is that users will make more demands on platforms to prove they are trustworthy and will concentrate their purchases on platforms that succeed. Since selecting a platform carries its own assessment cost, we can expect users to deal with fewer platforms in total. This means the trend for every major vendor to develop its own platform ecosystem will reverse. Looking still further ahead: fewer platforms gives the remaining platforms have more bargaining power with the app developers, so we can expect higher acceptance standards (good) and higher fees (not so good). The ultimately is fewer app developers as well.
</li><br /><li>Rebirth of suites. That’s not quite the right label since suites never died. But the point is that buyers looking for simplicity and facing higher assessment costs will find suites more appealing than ever. Obviously, the suites themselves must meet the new standards for simplicity, value and transparency, so integrated-in-name-only Frankensuites don’t get a free pass. But once a buyer has decided a suite vendor is trusthworthy, it’s far more attractive to use a module from that suite than to assess and integrate a best-of-breed alternative. Less obvious but equally true: building a system in-house also becomes less attractive, since in-house developers will also need to prove that their products are effective and reliable. This will necessarily increase development costs, so the build/buy balance will be tilted a little more towards buying – especially if the assessment costs of buying are minimal because the purchased option is part of a trusted suite. It’s true that this doesn’t apply if companies require users to accept whatever their in-house developers deliver. But that doesn’t sound like a viable long-term approach in a world where the gap between poor in-house systems and good commercial products will be larger than ever.
</li><br /><li>Limits on citizen developers. If blasphemy comes in degrees, this takes me to the professional-grade, eternal-damnation level. On one hand, nothing is more trusted than something a citizen developer creates for herself: she certainly knows how it works and can build in whatever transparency and monitoring she sees fit. So the new-pragmatic world is likely to see more, not fewer, user-built systems. But if we’re learned anything from decades of using Excel, it’s that complex spreadsheets almost always contain hidden errors, are opaque to anyone except (maybe) the creator, and are exceedingly fragile when change is required. Other user-built solutions will inevitably have similar problems. So even if users trust whatever they’ve built for themselves, everyone else in the organization will, and should be, exceedingly cautious in accepting them. In other words, the assessment cost will be almost insurmountably high for all but the simplest citizen-developed applications. This puts a natural, and probably shrinking, limit on the ability of citizen-developed systems to replace commercial software or in-house systems built by professional developers. In practice, citizen development will be largely limited to personal productivity hacks and maybe some prototyping of skunkworks projects. This doesn’t mean that no-code and low-code tools are useless: they will certainly be productivity-enhancers for professional developers. Don’t sell those Airtable options just yet.
</li></ul><p>
I’ll caution again that the picture I’m drawing here is far from certain to develop. I could be wrong about the change in social direction – although the alternative of continued disintegration is ugly to contemplate. Even if I’m right about the big shift, I could be wrong about its exact impact marketing and martech. Still, I do believe that current trends cannot continue indefinitely and it’s worth considering what might happen after their limits are reached. So what I’ll suggest is this: keep an eye out for developments that fit the pattern I’m suggesting and be ready with suitable marketing and martech strategies if things move in that direction. </p>
<p style="text-align: center;">* * *<br /></p><p>Addendum: The core argument of this post is “people feel the world is spinning out of control and trust will solve that problem”. That feels like a non sequitur, since it’s not obvious how trust creates control. It also feels uncomfortably hierarchical, and perhaps elitist, if “control” implies a central authority. (Note: you might read “control” as referring to people controlling their own personal technology and data. But fully self-sovereign individuals can still cause chaos if there’s not some larger control framework to constrain their actions.)
</p><p>But it's not a non sequitur because there is in fact a clear relationship between trust and control. Specifically:</p><ul style="text-align: left;"><li>Trust can be defined as the belief that someone will act in the way you want them to </li><li>Control is a way to force someone to act in the way you want. </li><li>Thus, trust and control are complementary: the greater trust you have in someone, the less control you need over them (to still ensure they act the way you want). </li></ul>Although power-hungry people might enjoy control for its own sake, most people will care only about achieving the desired result. So the solution to a world “spinning out of control” isn’t necessarily reinstating hierarchical, elite authority; it can also be generating trust. Both yield the same outcome of predictable desired behaviors. <p>This applies in particular to the discussion of citizen development and no-code software, which seems to imply that applications can only be used by more than one
person if there’s a central authority to coordinate and approve them. This is where "governance" comes in. It's correct that self-built software needs to meet certain standards to be safely used and shared. But "governance" can be achieved either through control (a central authority enforces those standards) or through trust (convincing users to apply those standards by themselves). Either approach can work but trust is clearly preferable. <br /></p><p> </p>
David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-84947699437758856912021-01-27T11:05:00.010-05:002021-01-28T13:43:57.226-05:00Lego Blocks, Pickup Trucks, and Why Bloomreach Bought Exponea<br /><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgF_JCRmER7t_NL5liYkiiazri61Q1AOx9hCufgU8KgpPz92CM42Ox5wMf0Yp8fZpgGAu7VdJiei1DGDhmzeAGWVMgdfNyHiVuPJdJSaAypmIUV1gwk1R51DOfKgurR4IFDxzUe/s1489/lego+pickup2.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="866" data-original-width="1489" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgF_JCRmER7t_NL5liYkiiazri61Q1AOx9hCufgU8KgpPz92CM42Ox5wMf0Yp8fZpgGAu7VdJiei1DGDhmzeAGWVMgdfNyHiVuPJdJSaAypmIUV1gwk1R51DOfKgurR4IFDxzUe/s320/lego+pickup2.png" width="320" /></a></div>Yesterday brought news that <a href="https://www.bloomreach.com/en/news/2021/bloomreach-poised-to-be-the-next-e-commerce-saas-unicorn-with-acquisition-of-exponea.html" target="_blank">CDP Exponea had been purchased by ecommerce recommendation engine Bloomreach</a>. The deal almost exactly parallels last year’s merger between RichRelevance and Manthan, as well the smaller-scale combination of CrossEngage with Gpredictive. It also recalls other recent CDP acquisitions including Acquia buying AgilOne, Chapsvision buying NP6, SAP buying Emarsys, and Wunderkind buying SmarterHQ. <p></p><p>
It’s easy (and correct) to see these deals as efforts to assemble comprehensive marketing suites. But it's not just that the buyers want to add a CDP their collection. These deals all involve CDPs with marketing automation functions (that is, segmentation, message selection, campaigns, personalization, and cross-channel orchestration). <a href="http://www.cdpinstitute.org" target="_blank">CDP Institute</a> labels these as “campaign" or "delivery”; others sometimes refer to them as “activation” or “execution” CDPs. This type of CDP provides the biggest headstart towards building a marketing suite. The drive to build suites clarifies why predictive analytics vendors Bloomreach, RichRelevance, and Gpredictive are such frequent partners: stand-alone predictive tools are missing nearly all the features needed for a full marketing platform, so they have the most to gain from buying a CDP that fills those gaps. </p><p>
Of course, the biggest CDP acquisition of all, Twilio’s purchase of Segment, doesn’t fit this mold. Segment was more of a pure-play or "data" CDP, limited to assembling and sharing customer profiles. But Twilio isn’t looking to build a marketing suite; their core business is call centers and (after buying SendGrid) email messaging. They have their sights set on providing a communications layer to support all customer-facing operations, including sales and service. Still a suite, but a different kind. </p><p>
The drive to construct comprehensive marketing suites is interesting because it conflicts with the current notion that marketers don’t want big, integrated products but instead want to create their own collections of components, building some parts with the latest self-service tools and connecting the rest through microservices, open APIs, and other technical wizardry. The pure vision is a distributed, non-hierarchical architecture, modeled roughly on the Internet itself, where any system can connect with any other system. The more practical vision is a platform-centric world where any system can plug into a central platform that provides basic services. In both visions, companies construct their own, highly customized collections of systems that are perfectly tailored to their needs. </p><p>
Simply put, the vendors assembling these suites are betting that vision is wrong. They believe – based no doubt on what buyers are telling them – that companies still want to buy an integrated product that meets their needs without any assembly required. The purely practical reason is that companies don’t assemble systems for their own sake; they assemble them as tools to do what’s really important, which is to make money (usually by delivering goods and services to customers). Sure, you can <a href="https://www.slashgear.com/chevy-made-a-life-size-silverado-pickup-with-lego-bricks-19562818/" target="_blank">build a pickup truck from Lego blocks</a> and you might even do that for fun. But if you actually need to haul something, you’ll go to a dealer and buy one. </p><p>
In other words, we still live in a world ruled by Raab's Law, which is “Suites win”. (More formally: In the long run, suites always win the competition between suites and best-of-breed systems.) Platforms don’t change this as much as you’d think, because customers always want the platforms themselves to add more features and make them tightly integrated. It's true that buyers want third-party applications that can extend platform capabilities. It's also in the platform vendors’ interest to be open to those applications since they add value to the platform at little cost to the platform owner. But there’s a time and effort cost to the user of selecting, connecting, and learning to use each new application, regardless of whether the app is “free” or how easily it connects. Users are very aware of these costs, which is why they want the core platform to offer as many features as possible. Put another way: the value of applications is they enable users to add features a platform lacks; but the more features a platform provides internally, the more value it provides from the start. This pushes platform vendors to add features that save users from the need to install apps. Of course, the art of platform management is knowing which features are popular and standardized enough to be incorporated. </p><p>
As new features are added, platforms increasingly resemble integrated suites. The significant difference from suites is that platforms offer users the option to replace the platform’s default components with external alternatives. But if the platform builders do their jobs correctly, users will find less need to do that over time. </p><p>
This is what makes campaign CDPs so attractive to companies attempting to construct a new marketing suite. The marketing features of the CDPs provide a core of functionality that marketers are looking for. In addition, and crucially, the core CDP features make it easier for the suite vendor to integrate components of its own system and also enable the vendor to offer platform-style flexibility by connecting with external systems. </p><p>
What, then, do these acquisitions tell us about the future of the CDP industry? The first thing to realize is that most CDPs already fall into the campaign and delivery categories (70% of the industry, measured on company count or employment, <a href="https://lp.cdpinstitute.venntive.com/DL2176-CDPI-Industry-Update-July-2020" target="_blank">according to our statistics</a>). Most of these firms actually started as marketing automation, personalization, or delivery systems and added CDP capabilities later. Some already provide an integrated marketing suite; the others can expand in that direction on their own or through combinations with other products. </p><p></p><p>It will be increasingly difficult for this type of CDP to survive without a broad set of marketing functions. Competitive pressures will force them to improve those features while treating core CDP capabilities of building and sharing unified profiles as just one talking point. We've already seen limit their investment in CDP features and instead partner with other data-oriented CDPs to meet those needs. (We also expect these firms to increasingly specialize by industry and company size. This makes it easier for them to build connectors to operational systems such as point of sale in retail or reservations in travel, as well as building industry-specific features, hiring industry-expert staff, and fine-tuning delivery and pricing models to meet target price-points.) </p><p>
The other 30% of the CDP industry are vendors specializing in data management and analytics. We uncreatively call these "data" and "analytics" CDPs. Many started life as tag managers, data collection, or predictive modeling systems; others were built as CDPs from the begining. As the Twilio/Segment deal illustrated, data CDPs may also be acquisition targets, especially for companies that are aiming to build a corporate-level backbone rather than a marketing suite. Firms that aren't acquired will be able to remain independent by offering best-of-breed customer data unification services to companies that need and can pay for a best-of-breed solution. These will likely be large enterprises. This type of CDP will increasingly be purchased by IT and data departments, rather than marketing, and will come to look more like IT tools than end-user applications. As such, they’ll find themselves increasingly competing with general purpose data management tools from other software providers and from data management and analytics tools built into the big cloud platforms (Google Cloud, AWS, Azure). So far, the specialized features of the most sophisticated data CDPs are more advanced than what’s available elsewhere. Some of these vendors will continue to innovate and ultimately emerge as strong leaders in this segment. Others will probably withdraw into niches or sell themselves to other companies that want to jumpstart their own CDP offerings. </p><p>
One happy byproduct of these developments may be a final end to the theological debate over the proper definition of “Customer Data Platform”. As the campaign and delivery CDPs position themselves as marketing suites or platforms, they’re likely to move away from CDP as their primary label. But they’ll still need the world to know that they offer the core CDP capabilities of unified profile creation and sharing. With any luck at all, they’ll handle this by labeling those features as "CDP" when they describe their system capabilities. This should eventually lead to a more consistent use of the CDP term throughout the market and, thus, less confusion over what it means. The data and decision CDPs already define CDP in terms of the same core capabilities. Some of those firms are today pulling away from verbal confusion by labeling themselves as “infrastructure” or “pipeline” customer data platforms. If the narrower definition of CDP reasserts itself, they may come back to adopting the CDP label itself. </p>
<br />David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-57640839713061812262021-01-03T11:30:00.002-05:002021-01-03T11:30:43.811-05:00Software Has Stopped Eating the World<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiF4oAX5h-T6ljPw_kiFeFmKFObv-X6tZh5HY5xZkyQk5Ms2DCcVEfHU1GrTNdt_nI_GW58VXanevnfZiejsxtTWX1YDCv0w1hD06PiQSQZMXPc3pbYiqAmU2XUAq46dOPxHKth/s1540/World+eating+software+2.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="880" data-original-width="1540" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiF4oAX5h-T6ljPw_kiFeFmKFObv-X6tZh5HY5xZkyQk5Ms2DCcVEfHU1GrTNdt_nI_GW58VXanevnfZiejsxtTWX1YDCv0w1hD06PiQSQZMXPc3pbYiqAmU2XUAq46dOPxHKth/s320/World+eating+software+2.png" width="320" /></a></div><p>This August will see the tenth anniversary of Marc Andreessen’s famous claim that <a href="https://www.wsj.com/articles/SB10001424053111903480904576512250915629460" target="_blank">software is eating the world</a>. He may have been right at the time but things have now changed: the world is biting back.
</p><p>I’m not referring to COVID-19, although it’s fitting that it took an all-too-physical virus to prove that a digital bubble of alternate facts could not permanently displace reality. Nor am I juxtaposing the <a href="https://www.cbsnews.com/news/the-threats-arising-from-the-massive-solarwinds-hack/" target="_blank">SolarWinds hack</a> with the unexpectedly secure U.S. election, which showed a simple paper trail succeed while the world’s most elite computer security experts failed.
</p><p>Rather, I’m looking at the most interesting frontiers of tech innovation: self-driving vehicles, green energy, and biosciences top my list. What they have in common is interaction with the physical world. By contrast, recent years haven’t seen radical change in software development. There have certainly been improvements in software, but they’re more about architectures (cloud, micro-services) and self-service interfaces than fundamentally new applications. And while most physical-world innovations are powered by software, the importance of those innovations is that they are changing physical experiences, not that they are replacing them with software-based virtual equivalents. </p><p>
</p><p>Even the most important software development of all – artificial intelligence – measures much of its progress by its ability to handle physical-world tasks such as image recognition, autonomous vehicle navigation, and recognizing human emotion. Let’s face it: it’s one thing for a computer to beat you at Go, but quite another for it to <a href="https://www.youtube.com/watch?v=fn3KWM1kuAw&feature=youtu.be" target="_blank">beat your dance moves</a>. Really, what special talent is left for humans to claim as their own?
</p><p>The shift is well under way in the world of marketing. One of the more surprising developments of the pandemic year was the boom in digital out-of-home advertising, which includes outdoor billboards and indoor signage. The growth seemed odd, given how much time people were forced to spend at home. But the industry marched ahead, spurred in good part by increased ability to track devices as they move through the physical world. It’s a safe bet that out-of-home ads will grow even faster once people can move about more freely.
</p><p>Indeed, the industries hit hardest by the pandemic – travel and events – also show that virtual experiences are not enough. Whatever their complaints before the pandemic, almost everyone who formerly traveled for business or attended business events is now eager to return to seeing people and places in person. The amount of travel will surely be reduced but it’s now clear that some physical interaction is irreplaceable.
</p><p>In a similarly ironic way, the pandemic-driven boost to ecommerce has been accompanied by a parallel lesson in the importance of physical delivery. Almost overnight, fulfillment has gone from a boring cost center to a realm of intensive competition, innovation, and even a bit of heroism. Software plays a critical role but it’s a supporting actor in a drama where the excitement is in the streets.
</p><p>Still closer to home for marketers, we’ve seen a new appreciation for the importance of customer experience, specifically extending past advertising to include product, delivery, service and support. If the obsession of the past decade has been targeted advertising, the obsession of the next decade will be superior service. This ties into other trends that were already under way, including the importance of trust (earned by delivering on promises through fulfillment, not making promises in advertising) and the shift from prospecting with third party data to supporting customers with first party data. Even at the cutting edge, advertising innovation has now shifted to augmented reality, which integrates real-world experiences with advertising, and away from virtual reality, which replaces the real world entirely.</p><p>This shift has substantial implications for martech.
</p><p>- The endless proliferation of martech tools may well continue, especially if the definition of “tools” stretches to include self-built applications. But the importance of tools that only interact with other software will diminish. What will grow will be tools that interact with the real world, and it’s likely those tools will be harder to find and (at least initially) take more skills to use. It’s the difference between building a flight simulator game and an actual aircraft. The stakes are higher when real-world objects are involved and there’s an irreducible level of complexity needed to make things work right.
</p><p>- As with all technology shifts, the leaders in the old world – the big software companies and audience aggregators like Facebook and Google – won’t necessarily lead in the new world. Reawakened anti-trust enforcement comes at exactly the worst moment for big tech companies needing to pivot. So we can expect more change in the industry landscape than we’ve seen in the past decade.
</p><p>- New skills will be needed, both to manage martech and to do the marketing itself. The new martech skills will involve learning about new technologies and tighter integration with non-marketing systems, although fundamentals of system selection and management will be largely the same. The marketing skill shift may be more profound, as marketers must master entirely new modes of interaction. But, again, the marketer’s fundamental tasks – to understand customer motivations and build programs that satisfy them – will remain what they always were.
</p><p>It’s been said that people <a href="https://quoteinvestigator.com/2019/01/03/estimate/" target="_blank">overestimate short-term change and underestimate long-term change</a>. The shift from software to physical innovation won’t happen overnight and will never be total. But the pendulum has reversed direction and the world is now starting to eat software. Keep an eye out for that future.
</p>David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-67993699496682387442020-12-13T20:50:00.003-05:002020-12-13T20:57:42.387-05:00MarTech Plot Lines for 2021<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj6VPh9TV6gn8JE3eJfQq0W1mS7cRTClL2lFtD02gSxtgPyMkExyDACfVzvNK3G5UQPLZKXHk5eBTnNQHiaMpEP4xMnw0ORdFrrhyphenhyphenWDmXxlDMozyYZ9k6Tuo7k5_UNrjDgsyfcg/s600/storyteller.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="390" data-original-width="600" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj6VPh9TV6gn8JE3eJfQq0W1mS7cRTClL2lFtD02gSxtgPyMkExyDACfVzvNK3G5UQPLZKXHk5eBTnNQHiaMpEP4xMnw0ORdFrrhyphenhyphenWDmXxlDMozyYZ9k6Tuo7k5_UNrjDgsyfcg/s320/storyteller.png" width="320" /></a></div><br />“Apophenia” – seeing patterns where none exist – is both occupational hazard and job requirement for an industry analyst. The<a href="https://www.cdpinstitute.org" target="_blank"> CDP Institute Daily Newsletter</a> provides a steady supply of grist for my pattern detection mill. But the selection of items for that newsletter isn’t random. I have a list of long-running stories that I follow, and keep an eye out for items that illuminate them. I’ll share some of those below.<p></p><p>Feel free to play along at home and let me know what stories you see developing. Deep State conspiracy theories are out of bounds but you’re welcome to speculate on the actual author(s) of the works attributed to “<a href="https://chiefmartec.com" target="_blank">Scott Brinker</a>”. </p><p><b>Media</b></p><p>
Everyone knows the pandemic accelerated the shift towards online media that was already under way. A few points that haven’t been made quite so often include:</p><p>
- connected TVs and other devices allow <a href="https://www.tvisioninsights.com/resources/tvisions-new-ctv-ad-platform-enables-first-in-industry-tag-free-ad-measurement-across-all-streaming-apps" target="_blank">individual-level targeting</a> without use of third-party cookies. As <a href="https://info.pixalate.com/connected-tv-ctv-ott-ad-supply-trends-report-q3-2020">online advertising is increasingly delivered</a> through those channels,
the death of cookies becomes less important. Nearly all device-level targeting can also <a href="https://www.groundtruth.com/article/groundtruth-expands-location-targeting-beyond-mobile-with-connected-tv-and-desktop-to-optimize-online-advertising-strategies/" target="_blank">include location data</a>, adding a dimension that cookies often lack.<br /></p><p>
- walled gardens (Facebook, Google, Amazon) face increasing competition from walled flower pots – that is, businesses with less data but a similar approach. Retailers like Walmart, Kroger, Target, and CVS have all started <a href="https://www.adweek.com/brand-marketing/cvs-rolls-out-new-in-house-digital-advertising-network " target="_blank">their own ad networks</a>, drawing on their own customer data. Traditional <a href="https://meredith.mediaroom.com/2020-08-10-Meredith-Corporation-Launches-Meredith-Data-Studio">publishers like Meredith</a> have collected their formerly-scattered customer data to enable cross-channel, individual-level targeting. <a href="https://www.businesswire.com/news/home/20201022005407/en/Neustar-Launches-Second-Party-Data-Marketplace-Powered-by-Fabrick%E2%84%A2-to-Improve-Targeting-and-Measurement-Across-Addressable-Media-Channels">Compilers like Neustar</a> and Merkle are also entering the business.
None of these has the data depth or scale of Facebook, Google, or Amazon but their audiences are big enough to be interesting. The various <a href="https://www.adweek.com/programmatic/the-trade-desk-building-version-2-0-unified-id/" target="_blank">“universal ID” efforts</a> being pursued by the ad industry will enable the different flow pots to cross-pollinate, <a href="https://meredith.mediaroom.com/2020-08-03-Meredith-Corporation-Teams-Up-With-Kroger-To-Help-Advertisers-Bridge-The-Gap-Between-Inspiration-And-Purchase" target="_blank">creating larger audiences</a> that I’ll call walled flower beds unless someone stops me. <br /></p><p>
- <a href="https://www.prnewswire.com/news-releases/clinch-unveils-new-shoppable-video-solutions-enabling-real-time-e-commerce-video-across-all-channels-from-online-video-to-ott-301183639.html" target="_blank">shoppable video</a> is growing rapidly. Amazon seems unstoppable but it faces increasing competition from social networks, streaming TV, and every other digital channel that can let viewers make purchases related to what they’re watching. The numbers are still relatively small but the potential is huge. And note that this is a way to sell based purely on context, so targeting doesn’t have to be based on individual identities. That will become more important as privacy regulations become more effective at shutting off the flow of third-party personal data.</p><p>
- digital out-of-home ads will combine with augmented and virtual reality to create a fundamentally new medium. The <a href="https://investors.quotient.com/press-releases/press-release-details/2020/Quotient-Unveils-New-In-Store-Digital-Out-Of-Home-Inventory-Powered-by-Deterministic-Shopper-Purchase-Data/default.aspx" target="_blank">growth of digital out of home advertising</a> is worth watching just because DOOH is such a great acronym . But it’s also a huge story that doesn’t currently get much attention and will explode once people can travel more freely post-pandemic. Augmented and virtual reality are making great technical strides (how about an <a href="https://venturebeat.com/2020/12/08/mojo-vision-teams-up-with-optics-leader-menicon-to-develop-ar-contact-lenses/" target="_blank">AR contact lens</a>?) but so far seem like very niche marketing tools. However, the two technologies perfectly complement each other, and will be supercharged by more accessible location data. Watch this space. </p><p>
<b>Marketing Technology</b></p><p>
- data will become more accessible. That marketers want to be “data-driven” is old news. What’s changing is that years of struggle are finally yielding progress toward making data more available and providing the tools to use it. As with digital advertising, the pandemic has accelerated an existing trend, <a href="https://ahoy.twilio.com/covid-19-digital-engagement-report" target="_blank">achieving in months</a> digital transformations that would otherwise have taken years. Although internal data is the focus of most integration efforts, access to external data is also growing, privacy rules notwithstanding. Intent data has been a particular focus with recent announcements from <a href="https://investor.techtarget.com/file/Index?KeyFile=406297309" target="_blank">TechTarget</a>, <a href="https://www.zoominfo.com/newsroom/press-releases/zoominfo-privacy-clusters-future-proof-intent-data" target="_blank">ZoomInfo</a>, <a href="https://swzd.com/blogs/spiceworks-ziff-davis-scales-actionable-b2b-intent-data-with-aberdeen-acquisition/" target="_blank">Spiceworks Ziff Davis</a>, and <a href="https://zetaglobal.com/blog/introducing-the-customer-data-management-solution-marketers-need-zeta-cdp/" target="_blank">Zeta Global</a>. <br /></p><p>
- artificial intelligence will become (even more) ubiquitous. It seems just yesterday that we were impressed to hear that a company’s product was “AI-powered”. Today, that’s as exciting as being told their offices have “electric lights”. But AI continues to grow stronger even if it doesn’t get as much attention (which the truly paranoid will suspect is because the AIs prefer it that way). Marketers increasingly <a href="https://www.episerver.com/reports/b2b-digital-experiences-report" target="_blank">worry that AI will ultimately replace them</a>, even if it makes more productive before that happens. The headline story is that AI is taking on more “creative” tasks such as content creation and campaign design, which were once thought beyond its capabilities. But the real reason for its growth may be that interactions are shifting to digital channels where success will be based more on relentless analytics than an occasional flash of uniquely human insight.
</p><p>
- blockchain will quiet down. I’ll list blockchain only to point out that’s been an underachiever in the hype-generation department. Back in 2018 we saw it at least as often as AI. Now it comes up just rarely. There are many clear applications in logistics and some promising proposals related to privacy. But there’s less wild-eyed talk about blockchain changing the world. Do keep an ear open, though: I suspect more is happening behind the scenes than we know.</p><p>
- no-code will <a href="https://www.prnewswire.com/news-releases/unqork-announces-207-million-in-series-c-funding-raising-company-valuation-to-2-billion-301146685.html" target="_blank">continue to grow</a>. If anything has replaced AI as the buzzword of the year, it’s “no code” and related concepts like “self-service” and “citizen [whatever]”. It’s easy to make fun of these (“citizen brain surgeon”, anyone?) but there’s no doubt that many workers become more productive when they can automate processes without relying on IT professionals. The downside is the same loss of quality control and integration posed other types of shadow IT – although no-code systems are more often governed than true shadow IT projects. In addition, no-code’s more sophisticated cousin, low-code, is <a href="https://www.outsystems.com/1/state-app-development-trends" target="_blank">widely used by IT professionals</a>. It’s possible to see no-code systems as an alternative to AI: both improve productivity, one by letting workers do more and other by replacing them altogether. But a more realistic view is to recognize AI as a key enabling technology inside many no-code systems. As the internal AIs get smarter, no-code will take on increasingly complex tasks, making it more helpful (and more threatening) to increasingly skilled workers.</p><p><b>
Marketing</b></p><p>
The pandemic has changed how marketers (and everyone else) do their work. With vaccines now reaching the public, it’s important to realize that conditions will change again fairly soon. But that doesn’t mean things will go back to how they were.</p><p>
- events have changed forever. Yes, <a href="https://www.integrate.com/resource/the-state-of-events-ebook/" target="_blank">in-person events will return</a> and many of us will welcome them with new appreciation for what we’ve missed. But tremendous <a href="https://venturebeat.com/2020/12/02/bizzabo-raises-138-million-as-events-industry-goes-hybrid " target="_blank">innovation has occurred in on-line events</a> and more will surely appear in coming months. It’s obvious that there will be a permanent shift towards more digital events, with in-person events reserved for situations where they offer a unique advantage. We can also expect in-person events to incorporate innovations developed for digital events – such as enhanced networking techniques and interactive presentations. I don’t think the significance of this has been fully recognized. Bear in mind that live events are often the most important new business source for B2B marketers, so major changes in how they work will ramify throughout the marketing and sales process.</p><p>
- remote work is here to stay. Like events, marketers’ worksites will drift away from the current nearly-all-digital mode to a mix of online and office-based activities. Also like events, innovations developed for remote work, such as improved collaboration tools, will be deployed in both situations. The key difference is that attendance of most events is optional, so attendees can walk away from dysfunctional changes. Workers have less choice about their environments, so harmful innovations such as employee surveillance and off-hours interruptions are harder for them to reject. Whether these stressors outweigh <a href="https://mms.businesswire.com/media/20201007005330/en/828185/1/4275411cReworking_Work_Atlassian_and_PaperGiant_en.pdf" target="_blank">the benefits of remote work</a> will depend on how well companies manage them, so we can expect a period of experimentation and turmoil as businesses learn what works best. With luck, this will mean new attention to workplace policies and management practices, something many firms have handled poorly in the past. Companies that excel at managing remote workers will have a new competitive advantage, especially since remote work lets the best workers choose from a wider variety of employers. </p><p>
- privacy pressures will rise. The European Union’s General Data Protection Regulation (GDPR) wasn’t the first serious privacy rule or the only reason that privacy gained more attention. But its enforcement date of May 25, 2018 does mark the start of an escalating set of changes that impact <a href="https://s3.amazonaws.com/media.mediapost.com/uploads/IABBrandDisruption2020.pdf" target="_blank">what data is available to marketers</a> and how consumers view use of their personal information. These changes will continue and companies will find it increasingly important to manage consumer data in ways that comply with ever-more-demanding regulations and give consumers confidence that their data is being handled appropriately. (A closely related subplot is <a href="https://enterprise.verizon.com/resources/reports/dbir/" target="_blank">continued security breaches</a> as companies fail to secure their data despite best efforts. Another is the continued misbehavior of Facebook and other social media firms and increasing resistance by regulators and consumers. That one is worth a channel of its own.) Marketers will need to take a more active role in privacy discussions, which have been dominated by legal, security, and IT staffs in businesses, and by consumer advocates, academics, and regulators in the political world. Earning a seat at that crowded table won’t be easy but making their voice heard is essential if marketers want the rules to reflect their needs. </p><p>
- trust is under fire. This is a broad trend spanning continents and stretching back for years (see Martin Gurri’s uncannily prescient <i><a href="https://www.goodreads.com/en/book/show/22451908-the-revolt-of-the-public-and-the-crisis-of-authority" target="_blank">The Revolt of the Public</a></i>, published in 2014), Socially, the trend presents itself as a loss of trust in <a href="https://www.edelman.com/trustbarometer" target="_blank">institutions</a>, the benefits of <a href="https://www.groupm.com/new-groupm-research-examines-consumer-trust-digital-marketing/" target="_blank">technology</a>, and credentialed experts in general. In marketing, it shows up as companies voicing disappointment with <a href="https://www.cmswire.com/digital-marketing/why-some-marketers-are-leery-of-analytics" target="_blank">data-driven analytics</a> and <a href="https://www.gartner.com/en/newsroom/press-releases/2019-12-02-gartner-predicts-80--of-marketers-will-abandon-person" target="_blank">personalization</a>, as <a href="https://www.idexpertscorp.com/ponemon-privacy-security-2020" target="_blank">consumers not trusting companies</a> to manage or protect their data, as workers' fear that <a href="https://www.rrd.com/pivot" target="_blank">AI systems will harm creativity</a> and codify unfair bias, as <a href="https://www.envistacorp.com/coverage/2020-personalization-special-report" target="_blank">widely-noted gaps</a> between what customers want and companies deliver, as “citizen developers” preferring to build their own systems, and as <a href="https://hingemarketing.com/library/article/inside-the-buyers-brain-third-edition-executive-summary" target="_blank">buyers preferring peers, Web searches, social media</a>, and pretty much any other information source to analysts reports. </p><p>Trust is the theme that connects all the stories I’ve listed above. Without trust, consumers won’t share their data, respond to marketing messages, or try new channels; governments will push for more stringent privacy and business regulations; workers will be less productive; and all industry progress will move more slowly. The trust crisis is too broad for marketers fix by themselves. But they need to account for it in everything they do, adjusting their plans to include trust-building measures that might not have been needed in a healthier past. The pandemic will end soon and technologies come and go. But trust will be a story to follow for a long, long time.<br /></p>
David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-50911793923643519462020-11-11T13:30:00.001-05:002020-11-11T13:30:31.152-05:00Trust-Hub Maps Company Data for Privacy and Other Uses<p>Marketers care about privacy management primarily as it relates to customer data, but privacy management overlaps with a broader category of governance, risk, and compliance (GRC) systems that cover many data types. Like privacy systems (and Customer Data Platforms), GRC systems require an inventory of existing customer data, including systems, data elements within each system, and uses for each element. These inventories form the foundation for functions including risk assessments, security, process documentation, responses to consumer data requests, and compliance monitoring. </p><p>
Having a single inventory would be ideal. But each application needs the inventory to be presented in its own way. One reason so many different systems gather their own data inventories is that each is limited to its own type of presentation. </p><p>
<a href="https://www.trust-hub.com/" target="_blank">Trust-Hub</a> Privacy Lens avoids this problem by creating a comprehensive data inventory and then enabling users to create whatever views they need. This requires gathering not just a list of data elements, but also documenting the users, systems, geographic locations, and business processes associated with each element. These attributes can then be filtered to create views tailored to a particular purpose. The system builds on this foundation by creating applications for related tasks such as risk analysis, privacy impact assessments, and security risk analysis. Users access the system through customizable dashboards that can highlight their particular concerns. </p><p>
Privacy Lens offers a range of methods to collect its inventory. It can import existing information, such as spreadsheets prepared for s compliance reporting or security audits. It can read metadata from common systems including <a href="https://www.Salesforce.com">Salesforce</a> and <a href="https://bigid.com/" target="_blank">BigID</a> or import metadata gathered by specialized discovery tools. When the data is not already assembled, Trust-Hub can scan existing data sources to create its own maps of data elements or let users enter information manually. In addition to data elements, the system can track business processes, user roles, individual users, resources, locations, external organizations, legal information, and evidence related to particular incidents. This information is all mapped against a master data model, helping users track what they’ve assembled and what’s still missing. The data is held in a graph database, Neo4J, a technology that is particularly good at tracking relationships among different elements. </p><p>Although some Privacy Lens users will focus on loading data into the system, most will be interested in using that data for specific purposes. Privacy Lens supports these with applications. Privacy managers, for example, can see an over-all privacy risk score, a list of open risks, a matrix that helps to prioritize risks by plotting them against frequency and impact, detailed reports on each risk, and additional risk scores for specific data types and processes. These risk scores are based on ten factors such as confidentiality, accuracy, volume, and regulation. The scores enable users to assess not just the risk of violating a privacy regulation, but risk of a security breach and the potential cost of such a breach. Trust-Hub argues that companies tend to focus on compliance risk even though the costs of litigation and reputation loss from a breach are vastly higher than any regulatory fines. </p><p>
Privacy officers can also use the system to conduct formal assessments, such as Privacy Impact Assessments, by answering questions in a system-provided template. The system keeps a copy of each assessment report along with a snapshot of the data model when the report is created, making it easy to identify subsequent changes and how they might change the assessment. Compliance and security officers can conduct other assessments within the system, such as tracing risks created when data is shared with external business partners. </p><p>
Risks uncovered during an assessment can be assigned a mitigation plan, with tasks assigned to individual users and reports tracking progress towards completion. Data in the model can also create other reports, such as Record of Processing Activity (ROPA), consent dates, and legal justifications. Personal data usage reports can take multiple perspectives, including which systems and processes use a particular data element, which elements are used by a particular system or process, and where a particular individual’s data is held. </p><p>
Trust-Hub has two additional products that exploit the Privacy Lens data map. Privacy Hub loads actual customer data from mapped systems, where it can be used to respond to data requests by consumers (Data Subject Access Requests, or DSARs) or answer questions from business partners without revealing personal information (for example, to verify that a particular individual is over 18). Privacy Engine loads masked versions of personal data and makes it available for analysis, so that users can run reports and create lists without being given access to private data. </p><p>
Trust-Hub was founded in 2016 and released its first product in 2018. The company now has more than one hundred clients, primarily large organizations selling directly to consumers, and service providers to those companies, such as consultants, system integrators, and law firms. Pricing is based on the number of users and starts around $25,000 per year. </p><p>
</p>David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-87848537353379350222020-10-11T13:53:00.009-04:002020-10-12T11:50:38.046-04:00Twilio Buys CDP Segment for $3.2 Billion<p>
</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi650t7ZNscYbKC7b-RXCTgf9_S1KCklEktRes3BweRTCqFsOkImNF0GsvTeUY4d63IXjw9M-b6hvfpJ9mDwFbYdc1P763o1Kxptw_4XPNr0ZJlJ4vAHteM5N_IxrhPaKuoFlPy/s499/Segment%252BTwilio.png" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" data-original-height="225" data-original-width="499" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi650t7ZNscYbKC7b-RXCTgf9_S1KCklEktRes3BweRTCqFsOkImNF0GsvTeUY4d63IXjw9M-b6hvfpJ9mDwFbYdc1P763o1Kxptw_4XPNr0ZJlJ4vAHteM5N_IxrhPaKuoFlPy/s320/Segment%252BTwilio.png" width="320" /></a></div>Friday afternoon brought an <a href="https://www.forbes.com/sites/alexkonrad/2020/10/09/twilio-to-acquire-cloud-startup-segment-for-3-billion/#4d53498d2020" target="_blank">unconfirmed Forbes report</a> that communications platform <a href="https://www.Twilio.com" target="_blank">Twilio</a> is buying CDP <a href="https://segment.com" target="_blank">Segment</a> for $3.2 billion. (The all-stock deal was <a href="https://www.twilio.com/press/releases/twilio-acquire-segment-market-leading-customer-data-platform" target="_blank">officially announced</a> on Monday.) It's Twilio’s third acquisition this year, following much smaller deals in January for <a href="https://contxto.com/en/brazil/silicon-valley-twilio-buys-brazilian-startup-teravoz/" target="_blank">telephony platform Teravoz</a> and in July for <a href="https://connect.electricimp.com/blog/electric-imp-is-now-part-of-twilio" target="_blank">IoT connector Electric Imp</a>. It comes two years after Twilio’s <a href="https://techcrunch.com/2019/02/01/twilio-closes-acquisition-of-email-specialist-sendgrid-in-all-stock-deal-now-worth-3b/ " target="_blank">$3 billion purchase of email platform SendGrid.</a> <br /><p></p><p>
The deal is intriguing from at least three perspectives:
</p><p><b>
Valuation: </b>the $3.2 billion price is impressive by any standard. Segment’s current revenue isn’t known, although <a href="https://getlatka.com/companies/segment" target="_blank">one published estimate</a> put it at $180 million for 2019. That sounds a bit high for a company with 450 employees at the time, but let's go with it and assume $200 million for 2020 revenue. This has Twilio is paying 16x revenue, which is less than the <a href="https://news.crunchbase.com/news/unpacking-the-6-5b-mulesoft-salesforce-deal/" target="_blank">20x that Salesforce paid for Mulesoft</a> ($6.5 billion on roughly $300 million) but in line with the <a href="https://tomtunguz.com/marketo-vista-ma/" target="_blank">15x that Adobe paid for Marketo</a> ($4.7 billion on $320 million) or <a href="https://www.fool.com/investing/2018/10/17/twilio-gobbles-up-sendgrid-in-its-biggest-ever-acq.aspx" target="_blank">14x that Twilio itself paid for SendGrid</a> ($2 billion on $140 million when the deal was announced; the $3 billion price reflects the subsequent rise in Twilio’s stock). Note that these prices are well above the <a href="https://www.saas-capital.com/blog-posts/private-saas-company-valuations-q1-2020-update/" target="_blank">run-of-the-mill SaaS valuations</a>, which are below 10x revenue.
</p><b>Twilio:</b> the SendGrid acquisition marked a major movement of Twilio beyond its base in telephone messaging to support a broader range of channels. If they’re to avoid the fragmentation that has plagued the larger marketing clouds, which also grew by acquisition, they need a CDP to unify their customer data. The big clouds (Oracle, Adobe, Salesforce, Microsoft, SAP) all chose to build their CDPs internally, but Twilio is much smaller and lacks the resources to do the same in a timely fashion. (Even the big clouds struggled, of course). On the other hand, Twilio’s surging stock price makes acquisition much easier. So buying a CDP they can deploy immediately gains them time and a mature product. It also offers entry to 20,000 accounts that might buy other Twilio products, especially given Segment’s position at the heart of their customer data infrastructure.
<p>
Of course, if Twilio really wants to compete with the marketing clouds, it will need to support other channels, most notably Web site management and ecommerce. Note that vendors beyond the clouds are pursuing the same strategy, including Acquia (which bought CDP AgilOne), IBM-spinoff Acoustic, MailChimp, and HubSpot. So the strategy isn’t unique, but it may be the only way for companies like Twilio to avoid being marginalized as apps that depend on major platforms controlled by other vendors. By definition, apps are easily replaced and are therefore easily commoditized. That’s a position to escape if you have the resources to expand beyond it.
</p><p><b>
CDP Industry:</b> Segment is/was the largest independent CDP vendor, although Tealium and Treasure Data are close. Other recent CDP acquisitions were mostly mid-tier vendors (AgilOne, Evergage, QuickPivot, Lattice Engines, SessionM). Of these deals, only AgilOne seemed central to the product strategy of the buyers. Segment’s decision to sell rather than try to grow on its own may signal a recognition that it will be increasingly difficult to survive as a general-purpose independent CDP. We’ve already seen much of the industry shift to more defensible niches, including integrated marketing applications and vertical industry specialization. There’s certainly still a case to be made for an independent CDP as a way to avoid lock-in by broad marketing clouds. But there’s no doubt that the marketing cloud vendors’ own CDPs will grab some chunk of the market, and more will be lost to CDPs embedded in other systems (email, ecommerce, reservations, etc.), offered by service vendors (Mastercard, Vericast, TransUnion, etc.) and home-built on cloud platforms like Amazon Web Services and Google Cloud.
</p><p>
Given these pressures, we’re likely to see additional purchases of CDPs by companies who are trying to build their own complete marketing platforms, including Shopify, MailChimp, HubSpot, and a number of private-equity backed roll-ups. Faced with a daunting competitive situation, many CDP vendors will be interested in selling, even at prices that might not be as high as they once hoped.
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Ironically, none of this bodes ill for the fundamental concept of the CDP itself. Companies will still need a central system to assemble and share unified customer profiles. It is indeed the platform on which the other platforms are built. Whether their CDP is stand-alone software or part of a larger solution doesn’t really matter from the user’s perspective: what matters is that clean, consistent, complete customer data is easily available to any system that needs it. Similarly, companies will still need the skills to build and manage CDPs. Marketing, data, and IT departments will wrestle with customer data long into the future, and the winners will be best positioned to achieve business success. </p><br />David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-92209800200834965642020-09-25T21:15:00.001-04:002020-09-25T21:15:13.629-04:00Software Review: Skypoint Cloud Combines CDP and Privacy ManagementThere are obvious similarities between Customer Data Platforms and privacy systems: both find customer data in all company systems; both assemble that data into unified profiles; and both govern access to those profiles. Indeed, some CDP vendors have expanded into privacy management by building consent modules to their systems or by integrating third-party consent managers.
<p>Still, the line between CDP and privacy managers is usually clear: CDPs store customer data imported from other systems while privacy managers read the data in place. There might be a small gray area where the privacy system imports a little information to do identity matching or to build a map of what each source system contains. But it’s pretty easy to distinguish systems that build huge, detailed customer data sets from those that don’t. </p><p>There’s an exception for every rule. <a href="https://skypointcloud.com" target="_blank">Skypoint Cloud</a> is a CDP that positions itself as a privacy system, including data mapping, consent management, and DSR (Data Subject Request) fulfillment. What makes it a CDP is that Skypoint ingests all customer data and builds its own profiles. Storing the data within the system actually makes fulfilling the privacy requirements easier, since Skypoint can provide customers with copies of their data by reading its own files and can ensure that data extracts contain only permitted information. Combining CDP and privacy in a single system also saves the duplicate effort of having two systems each map and read customer data in source systems.</p><p>
The conceptual advantages of having one system for both CDP and privacy are obvious. But whether you’d want to use a combined system depends on how good it is at the functions themselves. This is really just an example of the general “suite vs best-of-breed” debate that applies across all systems types. </p><p>You won’t be surprised that a young, small vendor like Skypoint lacks many refinements of more mature CDP systems. Most obviously, its scope is limited to ingesting data and assembling customer profiles, with just basic segmentation capabilities and no advanced analytics or personalization. That’s only a problem if you want your CDP to include those features; many companies would rather use other tools for them anyway. There’s that “suite vs best-of-breed” choice again.</p><p>
When it comes to assembling the unified database, Skypoint has a bit of a secret weapon: it relies heavily on Microsoft Azure Data Lake and Microsoft’s Common Data Model. Azure lets it scale effortlessly, avoiding one set of problems that often limit new products. Common Data Model lets Skypoint tap into an existing ecosystem of data connectors and applications, again saving Skypoint from developing those from scratch. Skypoint says they’re the only CDP vendor other than Microsoft itself to use the Common Data Model: so far as I know, that’s correct. (Microsoft, Adobe, SAP, and others are working on the Open Data Initiative that will map to the Common Data Model but we haven’t heard much about that recently.) </p><p>How it works is this: Skypoint can pull in any raw data, using its own Web tag or other sources, and store it in the data lake. Users set up a data flow to ingest each source, using either the existing or custom-built connectors. The 200+ existing connectors cover most of the usual suspects, include Web analytics, ecommerce, CRM, marketing automation, personalization, chat, Data Management Platforms, email, mobile apps, data stores, and the big cloud platforms.</p><p>
Each data flow maps the source data into data entities and relations, as defined in the Common Data Model or adjusted by the user. This is usually done before the data is loaded into the data lake but can also be done later to extract additional information from the raw input. Skypoint applies machine learning to identify likely PII within source data and lets users then flag PII entities in the data map. Users can also define SQL queries to create calculated values. </p><p>
Each flow has a privacy tab that lets the user specify which entities are returned by Data Subject Requests, whether data subjects can order the data erased, and which data processes use each entity. The data processes, which are defined separately, can include multiple entities with details about which entities are included and what consents are required. Users can set up different data processes for customers who are subject to different privacy regulations due to location or other reasons.</p><p>
Once the data is available to the system, Skypoint can link records related to the same person using either rule-based (deterministic) matches or machine learning. It’s up to the client define her own matching rules. The system maintains its own persistent ID for each individual. Matches can be either incremental – only matching new inputs to existing IDs – or can rebuild the entire matching universe from scratch. Skypoint also supports real-time identity resolution through API calls from a Web tag.</p><p>
After the matching is complete, the system merges its data into unified customer profiles. Skypoint provides a basic audience builder that lets users define selection conditions. This also leverages Skypoint's privacy features by first having users define the purpose of the audience and then making available only data entities that are permitted for that purpose. Users can also apply consent flags as variables within selection rules. Audiences can be connected with actions, which export data to other systems manually or through connectors.</p><p>
Users can supplement the audience builder by creating their own apps with Microsoft Azure tools or let external systems access the data directly by connecting through the Common Data Model.</p><p>
Back to privacy. Skypoint creates an online Privacy Center that lets customers consent to different uses of their data, make data access requests, and review company policy statements. It creates an internal queue of access requests and tracks their progress towards fulfillment. Users can specify information to be used in the privacy center, such as the privacy contact email and URLs of the policy statements. They can also create personalized email templates for privacy-related messages such as responses to access requests or requests to verify a requestor’s email address. </p><p>
This is a nicely organized set of features that includes what most companies will need to meet privacy regulations. But the real value here is the integration with data management: gathering data for subject access requests is largely automated when data is mapped into the system through the data flows, a major improvement over the manual data assembly required by most privacy solutions. Similarly, the connection between data flows, audiences, and data processing definitions makes it easier to ensure the company uses only properly consented information. There are certainly gaps – in particular, data processes must be manually defined by users, so an undocumented process would be missed by the system. But that’s a fairly common approach among privacy products. </p><p>
Pricing for Skypoint starts with a free version limited mostly to the privacy center, consent manager, and data access requests. Published pricing ranges past $2,000 per month for more than ten data integrations. The company was founded in 2019 and is just selling to its first clients.</p>
David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-67146613556441474152020-09-13T17:34:00.004-04:002020-09-13T17:37:55.921-04:00Software Review: Osano Manages Cookie Consent and Access Requests<p>The next stop on our privacy software tour is <a href="https://www.Osano.com" target="_blank">Osano</a>, which bills itself as “the only privacy platform you’ll ever need”. That's a bit of an overstatement: Osano is largely limited to data subject interactions, which is only one of the four primary privacy system functions I defined in <a href="https://customerexperiencematrix.blogspot.com/2020/08/software-review-bigid-for-privacy-data.html" target="_blank">my first post on this topic</a>. . (The other three are: discovering personal data in company systems, defining policies for data use, and enforcing those policies.) But Osano handles the interactions quite well and adds several other functions that are unique. So it’s certainly worth knowing.</p><p>
The two main types of data subject interactions are consent management and data subject access requests (DSARs). Osano offers structured, forms-based solutions to both of these, available in a Software-as-a-Service (Saas) model that lets users deploy them on Web sites with a single line of javascript or on Android and iOS mobile apps with an SDK.</p><p>
The consent management solution provides a prebuilt interface that automatically adapts its dialog to local laws, using the geolocation to determine the site visitor's location. There are versions for 40+ countries and 30+ languages, which Osano updates as local laws change. Because it is delivered as a SaaS platform, the changes made by Osano are automatically applied to its clients. This is a major time-saver for organizations that would otherwise need their own resources to monitor local laws and update their system to conform to changes.</p><p>
Details will vary, but Osano generally lets Web visitors consent to or reject different cookie uses including essential, analytics, marketing, and personalization. Where required by laws like the California Consumer Protection Act (CCPA), it will also collect permission for data sharing. Osano stores these consents in a blockchain, which prevents anyone from tampering with them and provides legally-acceptable proof that consent was obtained. Osano retains only a hashed version of the visitor’s personal identifiers, thus avoiding the risk of a PII leak while still enabling users to search for consent on a known individual.</p><p>
Osano’s use of blockchain to store consent records is unusual. Also unusual: Osano will search its client’s Website to check for first- and third-party cookies and scripts. The system will tentatively categorize these, let users confirm or change the classifications, and then let site visitors decide which cookies and scripts to allow or block. There’s an option to show visitors details about each cookie or script. </p><p>
Osano also provides customer-facing forms to accept Data Subject Access Requests. The system backs these with an inventory of customer data, built by users who manually define systems, data elements, and system owners. Put another way: there’s no automated data discovery. The DSAR form collects the user’s information and then sends an authentication email to confirm they are who they claim. Once the request is accepted, Osano sends notices to the owners of the related systems, specifying the data elements included and the action requested (review, change, delete, redact), and tracks the owners’ reports on completion of the required action. Osano doesn’t collect the data itself or make any changes in the source systems.</p><p>
The one place where Osano does connect directly with source systems is through an API that tracks sharing of personal data with outside entities. This requires system users to embed an API call within each application or workflow that shares such data: again, there’s no automated discovery of such flows. Osano receives notification of data sharing as its happens, encrypts the personal identifiers, and stores it in a blockchain alone with event details. Users can search the blockchain for the encrypted identifiers to build a history of when each customer’s data was shared. </p><p>
Perhaps the most unusual feature of Osano is the company’s database of privacy policies and related information for more than 11,000 companies. Osano gathers this data from public Web sites and has privacy attorneys review the contents and score each company on 163 data points. This lets Osano rate firms based on the quality of their privacy processes. It runs Web spiders continuously check for changes and will adjust privacy ratings when appropriate. Osano also keeps watch on other information, such as data breach reports and lawsuits, which might also affect ratings. This lets Osano alert its clients if they are sharing data with a risky partner.</p><p>
Osano is offered in a variety of configurations, ranging from free (cookie blocking only) to $199/month (cookie blocking and consent management for up to 50,000 monthly unique Web site visitors) to enterprise (all features, negotiated prices). The company was started in 2018 and says its free version is installed on more than 750,000 Web sites.</p>
David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-11895023209559964592020-09-06T16:22:00.004-04:002020-09-07T13:43:58.121-04:00When CDPs Fail: Insights from the CDP Institute Survey<p>We released a new member survey last week at the CDP Institute. You can (and should) <a href="https://lp.cdpinstitute.venntive.com/DL2207-CDPI-Member-Survey-2020" target="_blank">download the full report</a>, so I won’t go through all the details. You can also view a discussion of this on Scott Brinker's <a href="https://www.youtube.com/watch?v=72yc2i6VZSM" target="_blank">Chief Martech Show</a>. But here are three major findings. </p><p><b>Martech Best Practices Matter </b></p><p>We identified the top 20% of respondents as leaders, based on outcomes including over-all martech satisfaction, customer data unification, advanced privacy practices, and CDP deployment. We then compared martech practices of leaders vs. others. This is a slightly different approach from our previous surveys but the result was the same: the most successful companies deploy structured management methods, put a dedicated team within marketing inside of martech, and select their systems based on features and integration, not cost or familiarity. No surprise but still good to reaffirm. </p><p></p><div class="separator" style="clear: both; text-align: center;"><br /><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiG1l_RK6AtXcaFtlXMe-jOK4ZdadViUF3jnfZhuNhFP_9poEL2q50Bey8olO5wtO8C8p4NYd17AYKLkq1GLiSl9x-Yqw9BpIE6AMLpu7hcqJjWueSfzKfIUvN5zNHwkXeM0b6q/s974/survey+2020+blog+1.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="563" data-original-width="974" height="361" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiG1l_RK6AtXcaFtlXMe-jOK4ZdadViUF3jnfZhuNhFP_9poEL2q50Bey8olO5wtO8C8p4NYd17AYKLkq1GLiSl9x-Yqw9BpIE6AMLpu7hcqJjWueSfzKfIUvN5zNHwkXeM0b6q/w625-h361/survey+2020+blog+1.png" width="625" /></a></div><p></p><p></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEipFX9WlGIJNePtFXJuMPTaKIgJqHfdQbjFIZg2FEsjeGUHF6sWFPw92E5URYXMtDzxW5c2sJL2lxezXPSr8Jjnchta2gHqMgLNwILH4rDluwH8a72ixLzq97eaq5Z97QZlc4Qv/s911/Survey+2020+blog+2.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="498" data-original-width="911" height="343" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEipFX9WlGIJNePtFXJuMPTaKIgJqHfdQbjFIZg2FEsjeGUHF6sWFPw92E5URYXMtDzxW5c2sJL2lxezXPSr8Jjnchta2gHqMgLNwILH4rDluwH8a72ixLzq97eaq5Z97QZlc4Qv/w625-h343/Survey+2020+blog+2.png" width="625" /></a></div><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiHPk3eP_xl6Z5YUEcRryBIO1hXIPzQC9TXEp207yuNb-0yRJziQ1XT2G2PWgjfmUQpqJCJu4LYJL0EazrfLOrE6lKWedQoGIaEKV5MPUD2X6FQZyG1a0zpZnBnKaZjDbXRmA4y/s974/survey+2020+blog+3.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="485" data-original-width="974" height="311" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiHPk3eP_xl6Z5YUEcRryBIO1hXIPzQC9TXEp207yuNb-0yRJziQ1XT2G2PWgjfmUQpqJCJu4LYJL0EazrfLOrE6lKWedQoGIaEKV5MPUD2X6FQZyG1a0zpZnBnKaZjDbXRmA4y/w625-h311/survey+2020+blog+3.png" width="625" /></a></div><br /><br /><b>Martech Architectures are More Unified </b><p></p><p>For years, our own and other surveys showed a frustratingly static 15%-20% of companies reporting access to unified customer data. This report finally showed a substantial increase, to 26% or 52% depending on whether you think feeding data into a marketing automation or CRM system qualifies as true unification. (Lots of data in the survey suggests not, incidentally.)</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhaabOmSQkg2pRlLFPFRMoHibV_vLSZKxs-paZ4vagbvzOt6Ugkkn01OWPQf-lAb9ErIBOtM8je5gWzlIGxo3g8xuLVbEVJeaDDNeCzwvKcVKqcaIFGlhMHrs5kqKB14LZiPR4U/s713/survey+2020+blog+4.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="342" data-original-width="713" height="299" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhaabOmSQkg2pRlLFPFRMoHibV_vLSZKxs-paZ4vagbvzOt6Ugkkn01OWPQf-lAb9ErIBOtM8je5gWzlIGxo3g8xuLVbEVJeaDDNeCzwvKcVKqcaIFGlhMHrs5kqKB14LZiPR4U/w625-h299/survey+2020+blog+4.png" width="625" /></a></div><br /> <p></p><p></p><p><b><b>CDPs Are Maki</b>ng Good Progress </b></p><p>The survey showed a sharp growth in CDP deployment, up from 19% in 2017 to 29% in 2020. Bear in mind that we’re surveying members of the CDP Institute, so this is not a representative industry sample. But it’s progress nevertheless. </p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgntD9nLq0yqt8lpr6OO749Q-UjFXkG3LFWNn91AyPIKAKlpDDvtl4p4gF7oCjGcjYM5GKUHyNBqEdm0D6ZkaBYu8d142Pa2Kym2CXYcqJOHlEqLdKfbZi3dwtgEuzjjgDs72Vr/s559/survey+2020+blog+5.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="316" data-original-width="559" height="354" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgntD9nLq0yqt8lpr6OO749Q-UjFXkG3LFWNn91AyPIKAKlpDDvtl4p4gF7oCjGcjYM5GKUHyNBqEdm0D6ZkaBYu8d142Pa2Kym2CXYcqJOHlEqLdKfbZi3dwtgEuzjjgDs72Vr/w625-h354/survey+2020+blog+5.png" width="625" /></a></div><br />Where things got really interesting was a closer look at the relationship of customer data architectures to CDP status. You might think that pretty much everyone with a deployed CDP would have a unified customer database – after all, that’s the basic definition of a CDP and the numbers from the two questions are very close. But it turns out that just 43% of the respondents who said they had a deployed CDP also said they had a unified database (15% with the database alone and 28% with a database and shared orchestration engine). What’s going on here? <p></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhBHwsMl0idkbEsZ5DezV64urVeF2oDux5TATxUgVRLRkl8CKb7TO_z3_dcQ1QX0NuyDNt2kQb1cMAkFYh-cx9JKvv0pup0jLQt-tsDBGy2cWEJCYCaXz3r7b1ieYHzem_DdIw0/s647/survey+2020+blog+6.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="647" data-original-width="634" height="500" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhBHwsMl0idkbEsZ5DezV64urVeF2oDux5TATxUgVRLRkl8CKb7TO_z3_dcQ1QX0NuyDNt2kQb1cMAkFYh-cx9JKvv0pup0jLQt-tsDBGy2cWEJCYCaXz3r7b1ieYHzem_DdIw0/w491-h500/survey+2020+blog+6.png" width="491" /></a></div><br /> <p></p><p>The obvious answer is that people don’t understand what a CDP really is. Certainly we’ve heard that complaint many times. But these are CDP Institute members – a group that we know are generally smarter and better looking and, more to the point, should understand CDP accurately even if no one else does. Sure enough, when we look at the capabilities that people with a deployed CDP say they expect from a CDP, the rankings are virtually identical whether or not they report they have a unified database. </p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjhIEO9mbUUC0oqZgoTc8Cun0nrPDtwEFmrgnmqY5G_c72SZWbb7Flox9f83AcXOPXe-tjFPvwRWhQjrCeFaONejKSU_vqN4FG-4hIjb-0IPHfzVzCuXNmNCXxWFfHScQdCCCUt/s800/survey+2020+blog+7.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="450" data-original-width="800" height="351" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjhIEO9mbUUC0oqZgoTc8Cun0nrPDtwEFmrgnmqY5G_c72SZWbb7Flox9f83AcXOPXe-tjFPvwRWhQjrCeFaONejKSU_vqN4FG-4hIjb-0IPHfzVzCuXNmNCXxWFfHScQdCCCUt/w625-h351/survey+2020+blog+7.png" width="625" /></a></div>(Do you like this chart format? It’s designed to highlight the differences in answers between the two groups while still showing the relative popularity of each item. It took many hours to get it to this stage. To clarify, the first number on each bar shows the percentage for the group that selected the answer less often and the second number shows the group that selected it more often. So, on the first bar above, 73% of people with a unified customer database said they felt a CDP should collect data from all sources and 76% of those without a unified database said the same. The color of the values and at the tip of the bar shows which group chose the item more often: green means it was more common among people with a unified database and red means it was more common among people without a unified database. Apologies if you’re colorblind.) <p></p><p>Answers regarding CDP benefits were also pretty similar, although there begins to be an interesting divergence: respondents without a unified database were more likely to cite advanced applications including orchestration, message selection, and predictive models. Some CDPs offer those and some don’t, and it’s fair to think that people who prioritized them might consider themselves having a proper CDP deployment even if they haven’t unified all their data. </p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhBVyIY7uUKB4qjG8_Y-uqiXjieKx7uPXcV3BwS9n52zJqUa93qpqeKTHhKYf5p5ysoZ8sxnSSwd8J5y8dIFwkuwZoAh5rXEWgfwv3xdq5j6By-rmiPYAp6iNSYKw3wtVQJduYP/s976/survey+2020+blog+8.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="529" data-original-width="976" height="338" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhBVyIY7uUKB4qjG8_Y-uqiXjieKx7uPXcV3BwS9n52zJqUa93qpqeKTHhKYf5p5ysoZ8sxnSSwd8J5y8dIFwkuwZoAh5rXEWgfwv3xdq5j6By-rmiPYAp6iNSYKw3wtVQJduYP/w625-h338/survey+2020+blog+8.png" width="625" /></a></div><br />But the differences in the benefits are still pretty minor. Where things really get interesting is when we look at obstacles to customer data use (not to CDP in particular). Here, there’s a huge divergence: people without a unified database were almost twice as likely to cite challenges assembling unified data and using that data. <p></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjLTzYso_Q7oocdURXHFfeUceo4LYj1kBlYMGwOeqi93biof5uxDOLhyphenhyphen79r_Gvou2gK48OcQRPfHeM1PL5IMNbjeKsw4KKRboNs6HVEdMwiB1hjGAA0JK_Gnurt9BJVeT-sWWN-/s976/survey+2020+blog+9.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="528" data-original-width="976" height="338" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjLTzYso_Q7oocdURXHFfeUceo4LYj1kBlYMGwOeqi93biof5uxDOLhyphenhyphen79r_Gvou2gK48OcQRPfHeM1PL5IMNbjeKsw4KKRboNs6HVEdMwiB1hjGAA0JK_Gnurt9BJVeT-sWWN-/w625-h338/survey+2020+blog+9.png" width="625" /></a></div><br />Combining this with previous answers, I read the results this way:<b> people who say they have a deployed CDP but not a unified database know quite well that a CDP is supposed to create a unified database. They just haven’t been able to make that happen. </b><p></p><p>This of course raises the question of Why? We see from the obstacle chart that the people without unified data are substantially more likely to cite IT resources as an issue, with smaller differences in senior management support and data extraction. It’s intriguing that they are actually less likely to cite organizational issues, marketing staff time, or budget. </p><p>Going back to our martech practices, we also see that those without a unified database are more likely to employ “worst practices” of using outside consultants to compensate for internal weaknesses and letting each group within marketing select its own technology. They’re less likely to have a Center of Excellence, use agile techniques, or follow a long-term martech selection plan. (If the sequencing of this chart looks a bit odd, it's because they're arranged in order of total frequency, including respondents without a deployed CDP. That items at the bottom of the chart have relatively high values shows that deployed CDP owners selected those items substantially more often than people without a CDP.) <br /></p><p> <a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiIBP7Mge7gg3ujAi5_EJ0p9DDVYJah0KGvsMYhZgmeZ1Du81usTRfH4KJFsjmQdP5zPx72iN1XQPr9legDk73UjEZ7Cq3L6LkV_Gnf9aFRF-45tRQ0h6qissKr5tPDl8t77NVZ/s976/survey+2020+blog+10.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="528" data-original-width="976" height="338" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiIBP7Mge7gg3ujAi5_EJ0p9DDVYJah0KGvsMYhZgmeZ1Du81usTRfH4KJFsjmQdP5zPx72iN1XQPr9legDk73UjEZ7Cq3L6LkV_Gnf9aFRF-45tRQ0h6qissKr5tPDl8t77NVZ/w625-h338/survey+2020+blog+10.png" width="625" /></a><br /></p><p>So, whatever the problems with their IT staff, it seems at least some of their problems reflect martech management weaknesses as well. </p><p><b>But There's More...</b> <br /></p><p>The survey report includes two other analyses that touch on this same theme of management maturity as a driver of success. The first focuses on cross-channel orchestration as a marker of CDP understanding. It turns out that the closer people get to actually deploying a CDP, the less they see orchestration as a benefit. My interpretation is that orchestration is an appealing goal but, as people learn more about CDP, they realize a CDP alone can't deliver it. They then give higher priority to less demanding benefits. (To be clear: some CDPs do orchestration but there are other technical and organizational issues that must also be resolved.) </p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg4W1dy8zop-11NsMYd6zMsR04NbS1Z1EnIoiyHnKGkHXcMsSaatO_2lS0y0wCD6OwA10wT_1Oc0J-qAS5JFxxWIZNRHKzgbCL1N1Wzq4WtX5eTxtC41nMmQmdwhW2xw0znDblB/s1375/survey+2020+blog+11.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="791" data-original-width="1375" height="360" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg4W1dy8zop-11NsMYd6zMsR04NbS1Z1EnIoiyHnKGkHXcMsSaatO_2lS0y0wCD6OwA10wT_1Oc0J-qAS5JFxxWIZNRHKzgbCL1N1Wzq4WtX5eTxtC41nMmQmdwhW2xw0znDblB/w625-h360/survey+2020+blog+11.png" width="625" /></a></div><br />We see a similar evolution in understanding of obstacles to customer data use. These also change across the CDP journey: organizational issues including management support, budget, and cooperation are most prominent at the start of the process. Once companies start deployment, technical challenges rise to the top. Finally, after the CDP is deployed, the biggest problem is lack of marketing staff resources to take advantage of it. You may not be able to avoid this pattern, but it’s good to know what to expect. <p></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhe2WQz0BygG2rbB0o_IEN9LRwV4KqkumPdJyfJ6t6UHzjvUeiaY5O9PG1OBydYpMYUPLZuPOwBRj1L086adCx8ajL3lo-Rf4ClGtjK0R5AyLpgfLqZOjEOxgz3K3FzT-d0qkzR/s1318/survey+2020+blog+12.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="765" data-original-width="1318" height="364" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhe2WQz0BygG2rbB0o_IEN9LRwV4KqkumPdJyfJ6t6UHzjvUeiaY5O9PG1OBydYpMYUPLZuPOwBRj1L086adCx8ajL3lo-Rf4ClGtjK0R5AyLpgfLqZOjEOxgz3K3FzT-d0qkzR/w625-h364/survey+2020+blog+12.png" width="625" /></a></div><br />The other analysis looks at CDP results. In the current survey, 83% of respondents with a deployed CDP said it was delivering significant value while 17% said it was not. This figure has been stable: it was 16% in our 2017 survey and 18% in 2019. <p></p><p>I compared the satisfied vs dissatisfied CDP owners and found they generally agreed on capabilities and benefits, with orchestration again popping out as an exception: 65% of dissatisfied CDP owners cited it as a CDP benefit compared with just 45% of the satisfied owners. By contrast, satisfied owners were more likely to cite the less demanding goals of improved segmentation, predictive modeling, and data management efficiency. Similarly, the satisfied CDP users were less likely to cite coordinated customer treatments as a CDP capability and more likely to cite data collection. (Data collection still topped the list for both groups, at 77% for the satisfied owners and 65% for the others.) </p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiFRcugM8fbZcue17Kc3LrJ9YMTJ4wKT86dFYdAgLIc_ySgJQxaxaRVFM-ieMdrYhxn5YwEZMrY5Q6Nir3gWmTNnkK9nM3H2x_358X_wwEMRNYNOV9e3mKv64bdTVAoVJqvasps/s997/survey+2020+blog+13.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="551" data-original-width="997" height="345" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiFRcugM8fbZcue17Kc3LrJ9YMTJ4wKT86dFYdAgLIc_ySgJQxaxaRVFM-ieMdrYhxn5YwEZMrY5Q6Nir3gWmTNnkK9nM3H2x_358X_wwEMRNYNOV9e3mKv64bdTVAoVJqvasps/w625-h345/survey+2020+blog+13.png" width="625" /></a></div><p>When it came to obstacles, the dissatisfied owners were much more likely to cite IT and marketing staff limits and organizational cooperation. The divergence was even greater on measures of martech management, including selection, responsibility, and techniques. </p><p></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhqDsyW2yrjVKtXuGJjEq023rOFO__g1TC7MaxQvuO8Xa4r-PoA1NGVJPmGh4-5Mr8PENX5LJvuJ5TokBwbaU17-1vKHaW7bE6lqcwF5nHs5P5FyIOOf2NuvhE5_boM7p0-w9A2/s835/survey+2020+blog+14.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="746" data-original-width="835" height="560" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhqDsyW2yrjVKtXuGJjEq023rOFO__g1TC7MaxQvuO8Xa4r-PoA1NGVJPmGh4-5Mr8PENX5LJvuJ5TokBwbaU17-1vKHaW7bE6lqcwF5nHs5P5FyIOOf2NuvhE5_boM7p0-w9A2/w625-h560/survey+2020+blog+14.png" width="625" /></a></div><br />In short, the dissatisfied CDP owners were much less mature martech managers than their satisfied counterparts. As CDP adoption moves into the mainstream, it becomes even more important for managers to recognize that their success depends on more than the CDP technology itself. <p></p><p>There’s more in the report, including information on privacy compliance, and breakouts by region, company size, and company type. Again, you can download it <a href="https://lp.cdpinstitute.venntive.com/DL2207-CDPI-Member-Survey-2020" target="_blank">here</a> for free.
</p>David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0tag:blogger.com,1999:blog-34368959.post-54722013944112837942020-08-27T16:50:00.001-04:002020-08-27T16:50:56.514-04:00Software Review: BigID for Privacy Data Discovery<p>Until recently, most marketers were content to leave privacy compliance in the hands of data and legal teams. But laws like GDPR and CCPA now require increasingly prominent consent notifications and impose increasingly stringent limits on data use. This means marketers must become increasingly involved with the privacy systems to ensure a positive customer experience, gain access to the data they need, and ensure they use the data appropriately. </p>
<p>I feel your pain: it’s another chore for your already-full agenda. But no one else can represent marketers’ perspectives as companies decide how to implement expanded privacy programs. If you want to see what happens when marketers are not involved, just check out the customer-hostile consent notices and privacy policies on most Web sites.<br /></p><p>To ease the burden a bit, I’m going to start reviewing privacy systems in this blog. The first step is to define a framework of the functions required for a privacy solution. This gives a checklist of components so you know when you have a complete set. Of course, you’ll also need a more detailed checklist for each component so you can judge whether a particular system is adequate for the task. But let’s not get ahead of ourselves. </p>
<p>At the highest level, the components of a privacy solution are:</p><ul style="text-align: left;">
<li>Data discovery. This is searching company systems to build a catalog of sensitive data, including the type and location of each item. Discovery borders on data governance, quality, and identity resolution, although these are generally outside the scope of a privacy system. Identity resolution is on the border because responding to data subject requests (see next section) requires assembling all data belonging to the same person. Some privacy systems include identity resolution to make this possible, but others rely on external systems to provide a personal ID to use as a link.<br /> <br /></li>
<li>Data subject interactions. These are interactions between the system and the people whose data it holds (“data subjects”). The main interactions are to gather consent when the data is collected and to respond to subsequent “data subject access requests” (DSARs) to view, update, export, or delete their data. Consent collection and request processing are distinct processes. But they are certainly related and both require customer interactions. So it makes sense to consider them together. They are also where marketers are most likely to be directly involved in privacy programs.<br /><br /></li>
<li>Policy definition. This specifies how each data type can be used. There are often different rules based on location (usually where the data subject resides or is a citizen, but sometimes where the data is captured, where it’s stored, etc.), consent status, purpose, person or organization using the data, and other variables. Since regulations and company policies change frequently, this component includes processes to identify changes and either automatically adjust rules to reflect them or alert managers that adjustments may be needed.<br /><br /></li>
<li>Policy application. This monitors how data is actually used to ensure it complies with policies, send alerts if something is not compliant, and keep records of what’s done. Marketers may be heavily involved here but more as system users than system managers. Policy application is often limited to assessing data requests that are executed in other systems but it sometimes includes actions such as generating lists for marketing campaigns. It also includes security functions related specifically to data privacy, such as rules for masking of sensitive data or practices to prevent and react to data breaches. Again, security features may be limited to checking that rules are followed or include running the processes themselves. Security features in the privacy system are likely to work with corporate security systems in at least some areas, such as user access management. If general security systems are adequate, there may be no need for separate privacy security features. </li></ul><p>Bear in mind that one system need not provide all these functions. Companies may prefer to stitch together several “best of breed” components or to find a privacy solution within a larger system. They might even use different privacy
components from several larger systems, for example using a consent
manager built into a Customer Data Platform and a data access manager
built into a database’s core security functions. <br /></p>
<p>Whew.</p><p>Now that we have a framework, let's apply it to a specific product. We'll start with <a href="https://www.bigid.com" target="_blank">BigID</a>.<br /></p>
<p><b>Data Discovery</b></p><p style="text-align: left;">BigID is a specialist in data discovery. The system applies a particularly robust set of automated tools to examine and classify all types of data – structured, semi-structured, and unstructured; cloud and on-premise; in any language. For identified items, it builds a list showing the application, object name, data type, server, geographic location, and other details. </p><p style="text-align: left;">Of course, an item list is table stakes for data discovery. BigID goes beyond this to organize the items into clusters related to particular purposes, such as medical claims, invoices, and employee information. It also draws maps of relations across data sources, such as how the transaction ID in one table connects to the transaction ID in another table (even if the field names are not the same). Other features highlight data sources holding sensitive information, alert users if these are not properly secured from unauthorized access, and calculate privacy risk scores. </p>
<p style="text-align: left;">The relationship maps provide a foundation for identity resolution, since BigID can compare values across systems to find matches and use the results to stitch together related records. The system supports fuzzy as well as exact matches and can compare combinations of items (such as street, city, and zip) in one rule. But the matching is done by reading data from source systems for one person at a time, usually in response to an access request. This means that BigID could assemble a profile of an individual customer but won’t create the persistent profiles you’d see in a Customer Data Platform or other type of customer database. It also can’t pull the data together quickly enough to support real-time Web site personalization, although it might be fast enough for a call center. </p><p style="text-align: left;">In fact, BigID doesn’t store any data outside of the source systems except for metadata. So there's no reason to confuse it with a data lake, data warehouse, CRM, or CDP.<br /></p><p><b>Data Subject Interactions <br /></b></p>
<p>BigID doesn’t offer interfaces to capture consent but does provide applications that let data subjects view, edit, and delete their data and update preferences. When a data access request is submitted, the system creates a case that is sent to other systems or people to execute. BigID provides a workflow to track the status of these cases but won’t directly change data in source systems. </p>
<p><b>Policy Definition </b></p><p>BigID doesn’t have an integrated policy management system that lets users define and enforce data privacy rules. But it does have several components to support the process:</p><ul style="text-align: left;"><li>"Agreements" let users document the consent terms and conditions associated with specific items. This does not extend to checking the status of consent for a particular individual but does create a way to check whether a consent-gathering option is available for an item.<br /><br /></li>
<li>“Business flows” map the movement of data through business processes such as reviewing a resume or onboarding a new customer. Users can document flows manually or let the system discover them in the data it collects during its scan of company systems. Users specify which items are used within a flow and the legal justification for using sensitive items. The system will compare this with the list of consent agreements and alert users if an item is not properly authorized. BigID will also alert process owners if a scan uncovers a sensitive new data item in a source system. The owner can then indicate whether the business flow uses the new item and attach a justification. BigID also uses the business flows to create reports, required by some regulations, on how personal data is used and with whom it is shared. <br /><br /></li>
<li>“Policies” let users define queries to find data in specified situations, such as EU citizen data stored outside the EU. The system runs these automatically each time it scans the company systems. Query results can create an alert or task for someone to investigate. Policies are not connected to agreements or business flows, although this may change in the future. </li></ul><p><b>Policy Enforcement</b></p><p>BigID doesn’t directly control any data processing, so it can’t enforce privacy rules. But the alerts issued by the policy, agreement, and business flow components do help users to identify violations. Alerts can create tasks in workflow systems to ensure they are examined and resolved. The system also lets users define workflows to assess and manage a data breach should one occur. </p><p><b>Technology </b></p><p> As previously mentioned, BigID reads data from source systems without making its own copies or changes any data in those systems. Clients can run it in the cloud or on-premises. System functions are exposed via APIs which let the company, clients, or third parties build apps on top of the core product. In fact, the data subject access request and preference portal functions are among the applications that BigID created for itself. It recently launched an app marketplace to make its own and third party apps more easily available to its clients. </p><p><b>Business </b></p><p>BigID has raised $146 million in venture funding and reports nearly 200 employees. Pricing is based on the number of data sources: the company doesn’t release details but it’s not cheap. It also doesn’t release the number of clients but says the count is “substantial” and that most are large enterprises.
</p>David Raabhttp://www.blogger.com/profile/03489754392712536104noreply@blogger.com0