Why MarTech Fails: A Data-Driven Answer

Do you suffer from Martech Fatigue Syndrome (MFS)?  Symptoms include obsessive concern with the number of martech vendors, anxiety at the prospect of evaluating new systems, and fear of missing out on an important new capability.  Severe cases have reported hallucinations of vendor logos covering vast surfaces and nightmares of being buried under a collapsed martech stack.  MFS is rarely life-threatening but can disrupt the quality of your every day marketing.  If you or someone you know shows symptoms of MFS, please call Scott Brinker immediately.

So far as I know, Martech Fatigue Syndrome is not yet a real thing.  But I've definitely sensed a certain weariness in recent discussions of marketing technology.  The initial excitement about new opportunities has become exhaustion as marketers realize they need to keep making investments even though they're not using their existing systems to the fullest.  See, for example, this Kitewheel study, which found 72% of agencies use less than 40% of their tools every week.

So what’s the problem? Have marketers simply purchased the wrong technologies – after all, they’re new at the system buying business and martech is filled with bright and shiny distractions. Or are they buying the right systems only to find that other roadblocks get in the way of success?

Many people have asked similar questions. So many, in fact, that I've found a half-dozen surveys in the past two months touched on the topic.  You can see the questions and their answers at the bottom of this post.

But each survey asks different questions and gets slightly different answers.  To look for over-all patterns, I’ve combined the answers on the following table, putting similar items on the same row and keeping related ones nearby.  I’ve grouped the answers into general topic areas: organization, management support, marketing strategy, data management, delivery systems, and external factors. High-rated issues within each survey are shaded orange and low-rated issues are shaded green.  In other words, orange cells are the biggest problems, green cells are the smallest, and white cells are in between.

The reason for all that careful arrangement is to see any clusters in the answers. Sure enough, some do emerge: the biggest problems are concentrated in organizational issues (lots of orange).  The one exception that people think their own skills are perfectly adequate. Of course.

The management support area is mostly neutral except for a slash of orange for Return on Investment.  That make sense: measuring ROI is always a challenge for marketers.  To be clear, the answers are referring to the ROI of marketing programs in general, not martech investments in particular.  If anything, the surprise is that related items like management support and budget are less of a roadblock.

Marketing strategy isn’t a major problem in most cases, with just one survey as an exception. As with skills, this basically means that marketers are confident they know how to do their jobs.

The next two items, data management and delivery systems, are where technology comes in. There’s more green here than orange, confirming our hunch that access to adequate technology isn’t marketers’ main problem.

The final group, external factors, is no problem at all.

As a final bit of analysis, I've normalized all the answers to create a combined ranking for each category, splitting out ROI and internal skills since they are so different from everything else.  Apologies in advance to any real statistician who is horrified at the procedural flaws in this approach.   The rankings do seem to come out about right, and putting it all into one graph meets the goldfish attention span test.

Bottom line: measuring ROI and organizational roadblocks are the biggest reasons marketers fall to get value from technology. Finding good technology and knowing what to do with it are less of a concern.  These answers aren't unexpected but now you have a data-driven answer next time anybody asks.



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Survey Details:

Closing the Gap Between People’s Expectations and Retail Realities Retail Touchpoints for Magnetic
Barriers to cross-channel experience:
55%	not enough data for full profile
53%	internal organizational silos
50%	don’t know what types of messages resonate best
50%	struggle to get right message to the right person
49%	delivery systems are not integrated
49%	struggle to integrate first and third party data
46%	don’t know how to use our data to create a better experience
The Outlook for Data 2017: A Snapshot Into the Evolving Role of Audience Insight Winterberry Group for IAB Data Center of Excellence
Obstacles to value from data-driven marketing:
	year ago
45%	26%	difficulty proving ROI
45%	35%	lack of internal experience
39%	45%	insufficient technology
36%	25%	siloed organization
25%	33%	inadequate first party data
23%	12%	tests have failed
20%	22%	lack of leadership support
19%	15%	competitive pressures
16%	8%	lack of guidance from agency/service partners
12%	17%	inadequate third party data
9%	7%	little demand from customers
Forbes Insights Research Report on the Future of Marketing Forbes Insights for Aprimo
Agile marketing challenges
47%	bureaucracy
37%	proving ROI
32%	employees not empowered
27%	can’t connect agile marketing to business outcomes
27%	hierarchical organization
26%	budget
23%	lack of management vision
17%	lack right technology
14%	lack IT talent
14%	lack marketing talent
12%	difficulty choosing right third party
Marketer’s Confidence Index January 2017 Kantar Vermeer for American Marketing Association
Not confident the organization’s marketing team
	year ago
41%	33%	has right operating model (people/structure/processes/tools)
38%	27%	understands ROI of efforts
32%	28%	has clear strategy
30%	17%	is investing in right customers
29%	24%	is doing the right things
28%	24%	has clear brand position
25%	19%	has right capabilities
2017 Personalization Development Study Monetate
Obstacles to 1-to1 personalization
91%	organizational constraints block personal accountability
79%	automating decisions at scale
68%	assemble real time customer view with full context
65%	understand buyer behavior in context
62%	creating compelling offers and content
55%	integrating third party data
54%	data quality
48%	understanding who to personalize when in which channel
38%	sustainable data architecture
Adobe Digital Intelligence Briefing, 2017 Digital Trends Econsultancy for Adobe
Most difficult customer experience components to master
40%	strategy
39%	journey design
38%	process
37%	data
35%	technology
34%	culture
32%	collaboration
31%	skills

Zaius Offers Mid-Market Customer Data Platform Plus Analytics and Campaigns

It wasn’t until the end of a long demonstration that I finally understood what Zaius is. Which is pretty ironic, since they’re an almost perfect example of a Customer Data Platform – that is, a system that assembles customer data from multiple systems and makes it available for marketing and analytics. If anyone should recognize a CDP when they see one, it’s me. Come to think of it, if anyone is going to call something a CDP even when it isn't, that’s probably me, too.

So what fooled me about Zaius? It’s probably that most of their clients are mid-sized ecommerce companies, and the systems I’ve recently seen for ecommerce marketers have focused on personalized messaging and optimization. Zaius seemed to fall into those categories since much of our discussion focused on building marketing campaigns and doing attribution. I probably wasn’t helped by Zaius’ Web site, which calls it a “B2C CRM” and then lists single customer view, real-time marketing automation, and cross-channel attribution as its main features.  Single customer view is clearly CDP territory, but the marketing automation and attribution are not. In fact, CRM and marketing automation are feeder systems to CDPs, so you could argue it’s logically impossible for the same system to be both.

None of which really matters, I guess.  Let’s forget about labels and look at what Zaius does.

Turns out, the primary thing that Zaius does is to build that unified customer database. It has connectors to gather data from Shopify and Magento ecommerce systems; Salesforce ExactTarget, Oracle Responsys, IBM Silverpop, MailChimp, and SendGrid email services;  and the Segment, Tealium and Google tag managers.*  More prebuilt connectors are on the way. In the meantime, Zaius can capture data from Web sites through Javascript tags, from mobile apps through a System Development Kit, and from pretty much anything through APIs and batch uploads. The system loads data into a structured schema, which must be updated to accommodate new fields or objects.   Non-technical users can add custom fields on their own, but Zaius staff must add a new object. The system will reject records that have unexpected or invalid data and notify users of the problem. Zaius doesn’t automatically apply address standardization or other data transformations, although the vendor can create custom adapters to do some of that.

Once data is loaded, Zaius does deterministic identity resolution, which means it will chain together data using any identifier known to be associated with an individual. (For example, if a phone number and email address have been associated with the same person, any new record with either that phone number or email address will be linked to that person). It builds profiles of anonymous identifiers, such as cookies, and will link them to known individuals if they are later associated with a personal identifier. The system will merge identities if it discovers a connection, but it doesn’t do probabilistic matching across devices, fuzzy matching of similar postal addresses, or householding.

The data loading process also includes sessionization, which associates events that occurred around the same time. For example, multiple Web page views during a single visit would be a session. Zaius assigns events to sessions after they are linked to unified identities, so one session can include interactions across several channels. This might help users find customers who called on the phone after having trouble placing a Web order.

Zaius gives users tools to analyze the data it has captured, to create and export segments, and to run outbound marketing campaigns. Analytics include dashboards, attribution reports, and funnel analyses that track customers through a purchase process. Because Zaius is unifying data from multiple sources, its analyses can span events that happened in different systems. This means a funnel report could include an outbound email, a Web visit from a link in the email, and an ecommerce purchase during that visit. Neither the email or ecommerce system alone could track this entire path.

The system can report on customers at different stages in the life cycle, giving a useful overview of the user's business. It also lets users see tactical metrics, such as number of new customers acquired in the past month, and then drill down to see which campaigns produced those customers. Users who want to explore still further can look as deep as the specific events within an individual customer’s history. Security features can limit users to specified subsets of data, such as particular Web sites or product groups.

Segmentation in Zaius can draw on any data in the system. The system provides a form-based segment builder that can create complex expressions. These can be saved and used within other segment definitions. Users can export segments to other systems, including a two-way audience synchronization with Facebook and Google. In addition, a real-time API lets external systems query Zaius directly to find individual customer profiles. Segment exports and API access are what qualify Zaius as a CPD.

Segments can in turn be used in marketing campaigns.  These are built with templates that let users specify the channel (email, push, or SMS) and delivery type (once, recurring, continuous, or event triggered). Users can create email messages using a drag-and-drop interface that supports advanced personalization, such as selecting the top products in a customer’s most commonly purchased category. Personalization variables can be built with a scripting language or by inserting pre-built objects. Testing features let users define a test duration, evaluation criterion, and content versions. Users can set aside a portion of the audience to automatically receive the winning version when the test is complete. Zaius lacks more advanced optimization such as multi-variate tests that automatically create different combinations of features, finding segments within the audience that respond best to different versions, or predictive modeling.  Zaius sends email and SMS lists to external vendors for delivery. It uses Amazon SNS to send push messages. The vendor plans to add direct mail and browser push channels in the future.

Zaius was launched in 2014, with an original focus on providing a unified customer view and analytics. Its initial clients were large enterprises but most sales are now to mid-market firms with at least 100,000 contacts. Pricing is based on volume and starts at $1,000 per month.


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*Segment and Tealium are CDPs themselves, but let’s not confuse things even more.

LeadGenius Adds a Dash of Artificial Intelligence to Account Based Marketing

You may have noticed that I’m writing a little less about artificial intelligence than I had been. It may be that Skynet has imprisoned the real David Raab to block him from issuing dire warnings about its imminent threat to humanity and replaced him with a less alarmist simulation. You can’t actually prove that isn’t happening. But the David Raab, or Raab-bot, writing this will tell you it’s because he’s concluded that AI is destined to become so pervasive that it doesn’t make sense to treat it as a distinct topic. It will simply be embedded in everything and so should be evaluated as part of whatever it belongs to.

LeadGenius is a good example. The company is in the business of assembling B2B marketing lists – an industry dating back centuries to city directories and beyond. But LeadGenius was founded in 2011 to commercialize university research into combining AI with human inputs. It has since expanded from list gathering to all stages in the Account Based Marketing process, sprinkling in dashes of artificial intelligence at every step along the way.

Let’s look at those stages, using the four step structure of the Raab Guide to ABM Vendors.

1. Identify target accounts. This includes assembling data on potential accounts and selecting the right targets. Like many data gatherers, LeadGenius uses a combination of Web and other sources to build company and contact lists. Nearly every vendor who does this applies some form of natural language processing to extract information from unstructured sources. LeadGenius does this too. But it goes further by using artificial intelligence to identify records with questionably accurate information. It then sends these to humans for direct verification by telephone. The company guarantees 99% accuracy in its data, which is significantly better than most competitors can offer. AI's contribution here is to let LeadGenius call only the companies that need human contact, reducing over-all effort substantially. 

To find the right targets, LeadGenius loads a client's current CRM lists. It analyzes these for accuracy and completeness, providing users with reports that highlight problem areas. There’s probably some AI at work in that analysis. LeadGenius then identifies major file segments within the customer base and finds similar companies in the broader universe, estimating potential buyers and revenue by segment. Somewhat surprisingly , LeadGenius doesn’t create predictive lead scores, having found its more useful to prioritize prospects based on company attributes like size and industry. LeadGenius does use artificial intelligence, or at least its country cousin “fuzzy logic”, to map business titles into buyer roles, taking into account how different terms are used at different size companies to describe the same role.

2. Plan interactions. LeadGenius has a basic email campaign capability, including segment definition, email templates with personalization variables, and email sequences. There don’t seem to be any particular AI features here, although we’ll see in a moment that email does play a key role in LeadGenius’ AI utilization.

3. Execute interactions. LeadGenius sends emails through corporate or individual salespeople’s email accounts. It captures replies and uses AI-based natural language processing to classify them, distinguishing answers that indicate interest from out-of-office messages and clear rejections. Hot leads are pushed back to salespeople’s inboxes.  All response classifications are added to the database where they can be used in future selections. So AI does indirectly drive interaction flows. Response data can also be posted to Salesforce.com or Marketos, with additional integrations planned for the near future. Messages through other channels would have to be executed through marketing automation or CRM.

4. Analyze results. LeadGenius has the usual email and campaign reporting, enhanced with the AI-based response classifications.

As promised, you see a bit of AI magic at each of these process stages (assuming you count the AI-based email response as enabling the interaction planning). Certainly there’s room for more features and more AI use. But it’s already enough to illustrate how AI will add power throughout the ABM cycle.

LeadGenius is used primarily by large enterprises selling to small businesses. Those are the firms that can most benefit from its comprehensive data, market analyses, and prospect lists. Pricing is tailored to each client. The company has more than 150 B2B clients.

Quaero AdVantage CDP Bridges Identified and Anonymous Data

It’s a common pattern: several vendors proudly roll out new products they developed in secret, only to find they’re all very similar. The amazing coincidence isn’t really so amazing: everyone sees the same problems and has the same technologies available to solve them. So they come up with similar solutions.

Simultaneous rollout.  I've had this picture in my head for years.  Apologies to Dr. Seuss.

We’ve seen some of that in the Customer Data Platform industry, but there’s a twist. Many CDPs evolved from older systems and inherited some of their ancestors’ characteristics. One of those lineages goes back to marketing databases from simpler days, when postal mail and email were the main channels. The big challenges for those systems were loading complex data structures (addresses, transactions, message history, etc.), cleaning that data, and identifying records that belonged to the same individual. In that world, there was no such thing as an anonymous customer and most data was neatly structured. As I say, a simpler time.

Quaero’s AdVantage is a good example of a system with deep roots in the old methods – but updated to handle modern challenges. Quaero itself was founded back in 1999 as a marketing services provider (meaning they built custom marketing databases and attached tools like the Unica campaign manager). It was purchased in 2008 by CSG International, a telecom customer communications specialist, and repurchased by the original management in 2014. By then, the managers had already started work on a next-generation platform designed to handle both traditional and online data, using relational databases for one and a NoSQL system (in this case, Hadoop) for the other. The company has recently introduced this to the market as AdVantage.

The split architecture of AdVantage is actually pretty common among CDPs, since anonymous and identified customer data are often kept separate for privacy reasons. It’s also common to hold all the raw data in a NoSQL data lake and extract it to a relational database where it's refined and restructured for analysis. AdVantage does that too. It’s a bit less common for vendors to be so open about these details; Quaero management's transparency is probably another result of their maturity.


What’s truly unusual is the sophistication of AdVantage’s data processing itself. After nearly two decades of wrestling with customer identities, Quaero has mastered tricks that many newer vendors have yet to see.* More concretely, the system provides over 1,000 prebuilt “workflows” that perform tasks within data staging, loading, cleaning, transformation, aggregation, scoring, and measurement. These can be configured to specific situations, giving users a great deal of power without writing actual queries or scripts. Workflows can also be strung together to create larger flows, which AdVantage visualizes nicely.  This lets users trace exactly how the system got to its results. Configuring the workflows is still definitely technical work, which is either done by IT staff or the Quaero services team. But AdVantage makes it more efficient than hand coding and vastly more accessible to anyone other than the original coder.

Another important feature is that AdVantage flows work with metadata, meaning they are not mapped directly to the underlying data stores. This means an implementation can move to different platforms without losing most of the work. That makes it easier to adopt new technologies and to convert to more powerful platforms if a system outgrows its original installation.

AdVantage’s features for working with identified customers are especially mature, handling different kinds of “fuzzy” name and address matching as well as creating a “golden record” of best values from all sources. It also has strong features for unifying anonymous inputs, which it supplements with device matching services such as Tapad and Oracle Crosswise. AdVantage creates separate customer IDs for identified and anonymous profiles, and then, when possible, links them with a master ID

Once the data is assembled, AdVantage makes it available to marketing users and applications such as business intelligence tools and campaign managers. AdVantage provides its own interactive reports and segmentation interface. But most users will attach their own tools such as Tableau or Looker. AdVantage also connects to execution tools such as email engines. These tools can directly access both the relational and NoSQL data stores. Quaero has built standard connectors to common products, both to load data and access it. It builds new connectors as clients need them.

Existing AdVantage installations are hosted on Amazon Web Services, although a client could also run it on-premise if desired. Pricing is based on factors including number of sources, data volume, users, and applications. An average installation runs $15,000 to $25,000 per month although some are lower and higher. Quaero provides services with the product to help clients get set up properly and make changes over time.


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*Some others have, especially those with a similar background.