Openprise Gives Marketers Easy(ish) Tool to Manage Their Data

When I first described Customer Data Platforms two and half years ago,  all the vendors offered an application such as predictive analytics or campaign management in addition to the "pure" CDP function of building the customer database.  Since then, some "pure" CDPs have emerged, notably among vendors with roots in Web page tag management – Tealium, Signal, and Ensighten (which just raised $53 million). Other data collection specialists include Segment.com, Aginity, Umbel, Lytics, NGData, and Woopra, although some of these do supplement database building with predictive model scores, segmentation, and/or event-based triggers.

Openprise falls roughly into this second category. It’s primarily used to set up data processing flows for data cleaning, matching, and lead routing. But it can also apply segment tags and send out alerts when specified conditions are met. What it doesn’t do is maintain a permanent customer database accessible to other systems for campaigns and execution. This means Openprise doesn’t meet the technical definition of a CDP. But Openprise could post data to such a database.  And since the essence of the CDP concept is letting marketers build the customer database for themselves, Openprise arguably provides the most important part of a CDP solution.

Current clients use Openprise in more modest ways, however.  Most are marketing and sales operations staff supporting Salesforce.com and Marketo who use Openprise to supplement the limited data management capabilities native to those systems. Openprise also integrates today with Google Apps and the Amazon Redshift database. Integrations with Oracle Eloqua, HubSpot and Salesforce Pardot are planned by end of this year. The Marketo integration reads only the lead object, although the activities object is being added.  The Salesforce integration reads leads, contacts, opportunities, campaigns and accounts and will add custom objects.

Openprise works by connecting data sources, which are typically lists but sometimes API feeds, to “pipelines” that contain a sequence of if/then rules. Each rule checks whether a record meets a set of conditions (the “if”) and executes specified actions on those that qualify (the “then”). The interface lets users set up the flows, rules, and actions without writing programming code or scripts, usually by completing templates made up of forms with drop-down lists of possible answers. For example, a complex condition such as “sum exceeds threshold” would have form with blanks where the user specifies the variable to sum, variable to group by, the comparison operator, threshold value, and time period. This still takes some highly structured thinking – it’s far from writing an English language sentence – but is well within the capabilities of anyone likely to be in charge of operating a marketing automation or CRM system.

Of course, the value of such a system depends on the actual actions it makes available. The two basic actions in Openprise are sending alerts and setting attribute values. Alerts can be based on complex rules and delivered via email or text message. Attribute values can be used to set segment tags, assign lead owners for routing, and cleanse data. Cleansing features include normalization to apply rules, standardize formats, and match against reference tables.  The system can also fill in missing values based on relationships such as inferring city and state from Zip code. Matching can apply fuzzy methods, use rules to handle near-matches, and set priorities when several possible matches are available. Parsing can scan a text block for keywords and extract them.

Openprise already has special features to standardize job titles and roles and is working on company name clean up. It plans to add connectors for Dun and Bradsteet, Zoominfo and Data.com to verify and enhance customer information.

Updated records can be returned to the original source or sent to a different destination.  The Amazon Redshift connector means Openprise could feed a data warehouse or CDP available to other analytic and execution systems. Users can assign access rights to different data sets and to different elements within a set. They can then have the system send file extracts of the appropriate data to different recipients, a feature often used to share data with channel partners. Most pipelines execute as batch processes, either on demand or on a user-specified schedule. Some can run in real time through API calls.

The system also provides some data analysis capabilities, including time series, ranking, pie charts, word frequency, calendars, time of day, and trend reports. These are used mostly to help assess data quality and to profile new inputs.

Openprise says new customers usually get about two hours of training, during which they map a couple of data sources and build a sample pipeline.  The vendor also provides training videos and “cookbooks” that show how to set up common processes such as lead cleansing and merging two lists.

Pricing of Openprise is based on data volume processed, not number of records. Users can run 50 MB per month without charge. Running 100 MB per month costs $100 and running 1 GB per month costs $1,000. There also a free trial.

Openprise was released in late September and had accrued more than 30 users by mid-October. It is available on Marketo LaunchPoint and will eventually be added to Salesforce AppExchange.

Why Time Is the Real Barrier to Marketing Technology Adoption and What To Do About It

I split my time this week between two conferences, Sailthru LIFT and Marketing Profs B2B Forum.  Both were well attended, well produced, and well worth while.  My personal highlights were:

- Sailthru introducing its next round of predictive modeling and personalization features and working to help users adopt them. As you probably don’t know, Sailthru automatically creates several scores on each customer record for things such as likelihood to purchase in the next week and likelihood to opt out of email. The company is making those available to guide list selection and content personalization for both email and Web pages.  One big focus at the conference was getting more clients to use them.

- Yours Truly presenting to the Sailthru attendees about building better data.  The thrust was that marketers know they need better data but still don’t give it priority. I tried to get them so excited with use cases – a.k.a. “business porn” – that they’d decide it was more important than other projects. If they wanted it badly enough, the theory went, they’d find the time and budget for the necessary technology and training. I probably shouldn’t admit this, but I was so determined to keep their attention that I resorted to a bar chart built entirely of kittens.  To download the deck, kittens and all, click here.

- Various experts at Marketing Profs talking (mostly over drinks) about the growth of Account Based Marketing. The consensus was that ABM is still in the early stages where people don’t agree on what’s included or how to evaluate results. Specific questions included whether ABM should deliver actual prospect names (at the risk of being measured solely on cost per lead); what measurements really do make sense (and whether marketers will pay for measurement separately from the ABM system); and how to extend ABM beyond display ad targeting. Or at least I think that’s what we discussed; the room was loud and drinks were free.

- Me (again) advising Marketing Profs attendees on avoiding common mistakes when selecting a marketing automation vendor.   My message here, repeated so many times it may have been annoying, was that users MUST MUST MUST define specific requirements and explore vendor features in detail to pick the right system. One epiphany was finding that nearly everyone in the room already had a marketing automation product in place – something that would not have been true two or three years ago.  These are knowledgeable buyers, which changes things completely.  (Click here for those slides which had no kittens but do include a nice unicorn.)

You may have noticed a common theme in these moments: trying to help marketers do things that are clearly in their interest but they're somehow avoiding. Making fuller use of predictive models, building a complete customer view, focusing on target accounts, and using relevant system selection criteria are all things marketers know they should do. Yet nearly all industry discussion is focused on proving their value once again, or – usually the next step – in explaining how to do it.

What's the real obstacle?  Surveys often show that budget, strategy, or technology are the barriers. (See ChiefMartec Scott Brinker's recent post for more on this topic.)   But when you ask marketers face to face about the obstacles, the reason that comes up is consistently lack of time. (My theory on the difference is that people pressed for time don’t answer surveys.) And time, as I hinted above, is really a matter of priority: they are spending their time on other things that seem more important.

So the way to get marketers to do new things is to convince them they are worth the time.  That is, you must convince them the new things are more important than their current priorities.  Alternately, you can make the new thing so easy that it doesn’t need any time at all. The ABM vendors I discussed this with – all highly successful marketers – were doing both of these already, although they were polite enough not to roll their eye and say “duh” when I brought it up.

How do you convince marketers (or any other buyers) that something they already know is important is more important than whatever they’re doing now? I’d argue this isn’t likely to be a rational choice: MAYBE you can find some fabulously compelling proof of value, but the marketers will probably have seen those arguments already and not been convinced. More likely, you'll need to rely on emotion.  This means getting marketers excited about doing something (that’s where the “business porn” comes in) or scared about the consequences of not doing it (see the CEB Challenger Sales model,  for example). In short, it’s about appealing to basic instincts – what Seth Godin calls the lizard brain – which will ultimately dictate to the rational mind.

What about the other path I mentioned around the time barrier, showing that the new idea takes so little time that it doesn’t require giving up any current priorities? That’s a more rational argument, since you have to convince the buyer that it’s true.  But everything new will take up at least some time and money, so there’s still some need to get the buyer excited enough to make the extra effort. This brings us back to the lizard.

I’m not saying all marketing should be emotional.  Powerful as they are, emotions can only tip the balance if the rational choice is close. And I’m talking about the specific situation of getting people to adopt something new, which is quite different from, say, selling an existing solution against a similar competitor. But I spend a lot of time talking with vendors who are selling new types of solutions and talking with marketers who would benefit from those solutions. Both the vendors and I often forget that time, not budget, skills or value, is the real barrier to adoption and that emotions are the key to unlocking more time. So emotions must be a big part of our marketing if we, and the marketers we're trying to serve, are ultimately going to succeed.

Teradata Adds a Data Management Platform To Its Marketing Cloud...Who Will Be Next?

Teradata on Tuesday announced it is adding a data management platform (DMP) to its marketing cloud through the acquisition of Netherlands-based FLXone.  This is interesting on several levels, including:

- It makes Teradata the third of the big marketing cloud vendors to add a DMP, joining Oracle DMP (BlueKai) and Adobe Audience Manager. I already expected the other cloud vendors to do this eventually; now I expect that will happen even sooner. I’m looking at you, Salesforce.com.

- Unlike Oracle and Adobe, Teradata has stated (in a briefing about the announcement) that it intends to use the DMP as the primary data store for all components of its suite. I see this as a huge difference from the other vendors, who maintain separate databases for each of their suite components and integrate them largely by swapping audience files with a few data elements on specified customers. (In fact, Adobe just last week briefed analysts on a new batch integration that pushes Campaign data into Audience Manager to build display advertising lookalike audiences. The process takes 24 hours.)

Of course, we’ll see what Teradata actually delivers in this regard.  It's also important to recognize that performance needs will almost surely require intermediate layers between the DMP's primary data store and the actual execution systems. This means the distinction between a single database and multiple databases isn’t as clear as I may be seeming to suggest. But I still think it’s an important difference in mindset.  In case it isn’t obvious, I think real integration does ultimately require running all systems on the same primary database.

- It is still more evidence of the merger between ad tech and martech. I know I wrote last week that this is old news, but there’s still plenty of work to be done to make it a reality. One consequence of "madtech" is complete solutions are even larger than before, making them even harder for non-giant firms to produce. That’s the primary lesson I took away from last week’s news that StrongView had been merged into Selligent: although StrongView’s vision of omni-channel “contextual marketing” made tons of sense, they didn’t have the resources to make it happen. (See J-P De Clerck's excellent piece for in-depth analysis of the StrongView/Selligent deal.)  I’m not sure the combined Selligent/StrongView is big enough either, or that Sellingent owner HGGC will make the other investments needed to fill all the gaps.

To be clear: I'm not saying small martech/adtech/madtech firms can't do well.  I think they can plug into a larger architecture that sits on top of a customer data platform and perhaps a shared decision platform. But I very much doubt that a mid-size software firm can build or buy a complete solution of its own.  If you're wondering just who I have in mind...well, Mom always told me that if I couldn’t say something nice, I shouldn’t say anything at all.  So I won’t name names.

EverString Takes Another $65 Million and (More Important) Launches Predictive Ad Targeting Solution

EverString announced a $65 million funding round and new ad targeting product on Tuesday. (It also released a new survey on predictive marketing which is probably interesting, but I just can't face after last weekend’s data binge.)

The new funding is certainly impressive, although the record for a B2B predictive marketing vendor is apparently InsideSales’ $100 million Series C in April 2014.  It confirms that EverString has become a leader in the field despite its relatively late entry.

But the new product is what’s really intriguing. Integration between marketing and advertising technologies has now gone from astute prediction to overused cliché, so nobody gets credit for creating another example. But the new EverString product isn’t the usual sharing of a prospect list with an ad platform, as in display retargeting, Facebook Custom Audiences, or LinkedIn Lead Accelerator. Rather, it finds prospects who are not yet on the marketer’s own list by scanning ad exchanges for promising individuals. More precisely, it puts a tag on the client's Web site to capture visitor behavior, combines this with the client's CRM data and EverString's own data, and then builds a predictive model to find prospects who are similar to the most engaged current customers.  This is a form of lookalike modeling -- something that was separately mentioned to me twice this week (both times by big marketing cloud vendors), earning it the coveted Use Case of the Week Award.

Once the prospects are ranked, EverString lets users define the number of new prospects they want and set up real time bidding campaigns with the usual bells and whistles including total and daily budgets and frequency caps per individual.  EverString doesn’t identify the prospects by name, but it does figure out their employer and track their behaviors over time. If this all rings a bell, you’re on the right track: yes, EverString has created its very own combined Data Management Platform / Demand Side Platform and is using it build and target audience profiles.

In some ways, this isn’t such a huge leap: EverString and several other predictive marketing vendors have long assembled large databases of company and/or individual profiles. These were typically sourced from public information such as Web sites, job postings, and social media. Some vendors also added intent data based on visits to a network of publisher Web sites, but those networks capture a small share of total Web activity. Building a true DMP/DSP with access to the full range of ad exchange traffic is a major step beyond previous efforts. It puts EverString in competition with new sets of players, including the big marketing clouds, several of which have their own DMPs; the big data compilers; and ad targeting giants such LinkedIn, Google, and Facebook. Of course, the most direct competitors would be account based marketing vendors including Demandbase, Terminus, Azalead, Engagio, and Vendemore. While we’re at it, we could throw in the mix other DMP/DSPs such as RocketFuel, Turn, and IgnitionOne.

At this point, your inner business strategist may be wondering if EverString has bitten off more than it can chew or committed the cardinal sin of losing focus. That may turn out to be the case, but the company does have an internal logic guiding its decisions. Specifically, it sees itself as leveraging its core competency in B2B prospect modeling, by using the same models for multiple tasks including lead scoring, new prospect identification, and, now, ad targeting. Moreover, it sees these applications reinforcing each other by sharing the data they create: for example, the ad targeting becomes more effective when it can use information that lead scoring has gathered about who ultimately becomes a customer.

From a more mundane perspective, limiting its focus to B2B prospect management lets EverString concentrate its own marketing and sales efforts on a specific set of buyers, even as it slowly expands the range of problems it can help those buyers to solve. So there is considerably more going on here than a hammer looking for something new to nail.

Speaking of unrelated topics*, the EverString funding follows quickly on the heels of another large investment  – $58 million – in automated testing and personalization vendor Optimizely, which itself followed Oracle’s acquisition of Optimizely competitor Maxymiser. I’ve never thought of predictive modeling and testing as having much to do with each other, although both do use advanced analytics. But now that they’re both in the news at the same time, I’m wondering if there might be some deeper connection. After all, both are concerned with predicting behavior and, ultimately, with choosing the right treatment for each individual. This suggests that cross-pollination could result in a useful hybrid – perhaps testing techniques could help evolve campaign structures that use predictive modeling to select messages at each step. It’s a half-baked notion but does address automated campaign design, which I see as the next grand challenge for the combined martech/adtech (=madtech) industry. On a less exalted level, I suspect that automated testing and predictive modeling can be combined to give better results in their current applications than either by itself. So I’ll be keeping an eye out for that type of integration. Let me know if you spot any.

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*lamest transition ever

Marketers Are Struggling to Keep Up With Customer Expectations: Here's Proof

How pitiful is this: My wife left me alone all last weekend and the most mischief I could get into was looking for research about cross-channel customer views. The only defense I can make is I did promise a client a paper on the topic, which I finished Sunday night. But then I decided it was way too wonky and wrote a new, data-free version that people might actually read.

But you, Dear Reader, get the benefit of my crazy little binge. Here’s a fact-filled blog post that uses some my carefully assembled information. (Yes, there was actually much more. I’m so ashamed.) 

Customer Expectations are Rising

Let's start with a truth universally acknowledged – that customers have rising expectations for personalized treatment. Unlike Jane Austen, I have facts for my assertion: e-tailing group's 7th Annual Consumer Personalization Survey found that 52% of consumers believe most online retailers can recognize them as the same person across devices and personalize their shopping experience accordingly. An even higher proportion (60%) want their past behaviors used to expedite the shopping experience, and one-third (37%) are frustrated when companies don’t take that data into account.

Switching to customer service, Microsoft’s 2015 Global State of Multichannel Customer Service Report  found that 68% of U.S. consumers had stopped doing business with a brand due to a poor customer service experience and 56% have higher expectations for customer service than a year ago. So, yes, customer expectations are rising and failing to meet them has a price.

Marketers Know They Need Data

Marketers certainly see this as well. In a Harris Poll conducted for Lithium Technologies, 82% of 300 executives agreed that customer expectations have gotten higher in the past three years.  Focusing more specifically on data, Experian's 2015 Data Quality Benchmark Report, which had 1200 respondents, found that 99% agreed some type of customer data is essential for marketing success. Marketers are backing those opinions with money: when Winterberry Group asked a select set of senior marketers what was driving their investments in data-driven marketing and advertising, the most commonly cited reason was the need to deliver more relevant communications and be more customer-centric. .

Few Have the Data They Need

But marketers also recognize that they have a long way to go. In Experian’s 2015 Digital Marketer study, 89% of marketers reported at least one challenge with creating a complete customer view.

Econsultancy’s 2015 The Multichannel Reality study for Adobe found that just 29% had succeeded in creating such a view, 15% could access the complete view in their campaign manager, 14% integrate all campaigns across all channels, and 8% were able to adapt the customer experience based on context in real time.  In other words, the complete view is just the beginning.  In other words, marketers are nowhere near as good at personalizing experiences as consumers think.

Real-Time Isn't a Luxury

Given the challenges in building any complete view, is real-time experience coordination too much to ask? Customers don’t think so; in fact, as we've already seen, they assume it’s already happening. Marketers, of course, are more aware of the challenges, but they too see it as the goal. In a survey of their own clients, marketing data analysis and campaign software vendor Apteco Ltd found that 12% of respondents were already using real-time data, 31% were sure they needed it and 37% felt it might be useful. Just 17% felt daily updates were adequate.

Real-Time Must Also Be Cross-Channel

It’s important to not to confuse real-time personalization with tracking customers across channels or even identifying customers at all.  In a survey by personalization vendor Evergage, respondents who were already doing real-time personalization were most often basing it on immediately observable, potentially anonymous data including type of content viewed, location, time on site, and navigation behavior.  Yet the marketers in that same study gave the highest importance ratings to identity-based information including customer value, buying/shopping patterns, and buyer persona. It’s clear that marketers recognize the need for a complete customer view even if they haven't built one.

Summary

What are we to make of all this, other than the fact that I need to get out more?  I'd summarize this in three points:

- customer expectations are truly rising and you'll be penalized if you don't meet them
- marketers know that meeting expectations requires a complete customer view but few have built one
- the complete view has to be part of a real-time integrated, real-time to deliver the necessary results

None of this should be news to anyone. But perhaps this data will help build your business case for investments to solve the problem.  If so, my lost weekend will not have been in vain.
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How Many Ads Do You See Each Day? Fewer Than It Seems (I Think)

My cliché detector starts chirping as soon as anyone says today’s marketers face more competition than ever before. Sepia-toned glasses notwithstanding, marketers have had competitors at least since the railroads (or maybe canals) made it practical for customers to shop outside the local village – and for even longer if you lived in a city. So competition has been tough for as long as anyone now living can remember.

I thought I’d found a legitimate exception when writing a recent paper for QuickPivot (available here) about the continued value of direct mail marketing. My core argument was that direct mail is needed to cut through the clutter created by the increase in advertising messages. Surely it’s self-evident that people get more advertising today than ever before, right?

Well, I certainly thought so when I wrote the paper. But I recently searched for some validation of the claim about more clutter.  Turns out that shockingly little research has been published on the question of how many ads people actually receive. The most authoritative-looking study I found was from Media Dynamics in 2014, which found almost no change since 1945 in ads people were exposed to, despite a near-doubling in the time spent with media (defined as TV, radio, Internet, newspapers and magazines).

Really? Just 362 ads per day? That certainly seemed low to me, even after recognizing that the study reports on paid advertising, as opposed to the brand logos you see on everything from football stadiums to a biker’s tattoo. The 362 is nowhere near the widely cited figure of 5,000 per day, although the origins of that are mysterious at best.

I looked further but couldn’t find any better data. Now I was really frustrated and pondered what it would take to make a crude estimate of my own as a simple sanity check. This ultimately led to the bright idea of using total ad spending and cost per thousand impressions to calculate total ad impressions per year. Once you have that number, it can easily be converted to ad impressions per person per day. What I like about this method is avoids any need to estimate how many ads an individual sees per minute of media time or how many ads are theoretically available to be viewed. On the other hand, it assumes that advertisers get exactly the impressions they pay for, neither more nor less.  This is certainly not true in an absolute sense, but I'm willing to trust that the correlation between actual and purchased impressions is close enough to give an answer that's approximately correct.

The table below shows my results. Figures for advertising are easy: eMarketer publishes them all the time.  CPMs are harder to find.  I ended up using figures from sources including INFOACRS (which itself quotes eMarketer, although from 2008) and MonetizePros. There’s a bit of body English in there as well.  I do think the results are either reasonable or low – another widely quoted authority, Augustine Fou, shows generally higher CPM figures than the ones I used, which would result in estimating even fewer impressions from the same spending.

Bottom line, my numbers show 264 impressions per person per day.  That's a little lower than Media Dynamics, but in the same ballpark. Interesting.

I will admit that I’m inordinately pleased with this methodology, which I suspect is roughly correct despite many flaws in the details. One virtue is it sheds some light on the original question of whether people are seeing more ads: since we know that total media spending is rising slightly and the mix is shifting towards the low-CPM digital channel, the number of impressions is almost certainly on the rise. You'd have to adjust any trend calculations for changes in population and in channel CPMs to know for sure. 

On the other hand, the shifts are fairly gradual so it's probably wrong to claim that we're facing a sudden flood of increased advertising.  I don't have the necessary data readily available to do more detailed calculations, but if anyone out there does have the data and the time to do the math, I’d love to see your results.

(And if you're wondering: there will be about 80 billion pieces of advertising mail delivered this year, which comes to about 0.7 per person per day.)

Data Plus MarTech: HubSpot and Demandbase Join the Race

There were two industry announcements this week that were unexpectedly related. The first was HubSpot’s announcement yesterday that its CRM offerings would now include access to a 19 million account prospecting database. The second was Demandbase’s acquisition of data-as-a-service vendor WhoToo, which offers its own set of 250 million profiles relating to 70 million business professionals.

The WhoToo acquisition marks a big step in the continued evolution of Demandbase, since it's a change from targeting companies to targeting individuals (although DemandBase still won’t sell you their names). More precisely, WhoToo aggregates audience data from multiple sources and makes it available for selections based on company and individual attributes. The company does know the identity of some individuals and will use these to target email and Web advertising to names you provide. It will also let you market to audiences in those channels without providing their names. This is a nice extension of Demandbase’s existing account-based marketing capabilities. What makes WhoToo really special is it has the technology to access its data with the split-second speed needed to purchase display and mobile ads in real time.

The addition of individual-level targeting puts Demandbase on a more even plane with LinkedIn, which of course already sells advertising to its own huge database of more than 350 million profiles. The WhoToo deal won’t fully close that gap, but it does help to keep Demandbase competitive. (I’m sure Demandbase would argue it has its own advantages over LinkedIn.)

In this context, the HubSpot announcement is interesting mostly because it too recognizes the importance of giving marketers audience lists without acquiring the names for themselves. You could argue this makes HubSpot a player in the super-hot Account Based Marketing category, although they didn't use the term.  If they are, it's ABM-lite, in the sense that HubSpot will give CRM users basic profile information, usually including a phone number, but doesn't offer contact names or email addresses. It also pulls recent news stories.  This is pretty consistent with HubSpot's historic aversion to unsolicited outbound contacts.  The company does approach the line by giving enterprise users an option to find other people in their company who have a contact at target accounts and ask for a warm introduction.

On the other hand, HubSpot also announced integration with LinkedIn for paid ad campaigns and said a Google AdWords integration is in beta, which are definitely in outbound territory. Naturally, HubSpot says its LinkedIn and Google campaigns will be giving potential buyers information they want, so they are not at all like that bad old interruptive advertising that HubSpot has always opposed. No, not one bit.

Anyway, the point here is that both HubSpot and Demandbase are adding data to their marketing technology, something we’ve seen in other deals like Oracle buying Datalogix. There are still plenty of stand-alone data vendors, especially when it comes to B2B prospecting lists. And there are plenty of vendors who combine prospect data with predictive – including LinkedIn itself since its recent FlipTop acquisition. But I think we can add “data plus tech” to the tote board of martech horse races.

LinkedIn Buys Fliptop: Why Account Based Marketing and Predictive Analytics Are a Natural Fit

Predictive analytics vendor Fliptop today announced its acquisition by B2B social network LinkedIn.  It's an interesting piece of news but I'm personally disappointed at the timing because I have been planning all week to write a post about the relationship between predictive analytics and account-based marketing (ABM).  I would have looked so much more prescient had they announced the acquisition after I had published this post!

The original inspiration for the planned post was a set of three back-to-back conversations I had last Friday with one ABM vendor and two predictive analytics companies (none of which were Fliptop or LinkedIn).  The juxtaposition highlighted just how much predictive and ABM complement each other.  In fact, the relationship is so obvious that it almost seems unnecessary to lay it out: predictive vendors help marketers find accounts to target; ABM helps marketers reach target accounts.  You can safely assume that both sets of vendors have noticed the relationship and that many are working to combine the two techniques.  The Fliptop/LinkedIn deal is just more evidence of the connection.

To move past the very obvious, ABM vendors – whose basic business is selling ads targeted to specific companies – could also use predictive analytics to refine their ad targeting.  This could mean selecting the best people to reach within targeted accounts or selecting the most effective ad placements to reach those accounts.  This requires integration of predictive analytics within the ABM product, not just using predictive before ABM begins.  I expect LinkedIn will use Fliptop's capabilities in these ways among others.

But, getting back to last week's conversations, what really struck me was a less obvious connection of ABM and predictive to content.  Two of the vendors described using their systems to select which content to send to specific accounts or individuals.  These selections are based on previous behavior, something that certainly makes sense.  But I don't generally recall hearing ABM or predictive vendors discussing as one of their applications.  It's an important idea because it promises to improve results by delivering more relevant content for the same price.  The same data gives marketers insights into broader trends in the types of content that buyers find interesting.

Content analysis requires the ABM or predictive system to be aware of the topics of the content being consumed.  This is only possible if someone specifically goes to the trouble of tagging the content and capturing the tags.  So content analysis is not quite a natural byproduct of the ABM or predictive analytics: it takes some intentional effort.  A corollary is that not all ABM and predictive systems can deliver this benefit.  So it's something to specifically ask prospective vendors about if you think you'll want it.

To put things in a still broader perspective, targeting content with ABM and predictive systems is part of a broader trend of using advanced technology to help marketers create, manage, and optimize content.  This is something that vendors like Captora, Persado, and Olapic do in terms of content creation, and Jivox, OneSpot, Triblio, and BloomReach do in terms of personalized content creation.  I've been looking at a lot of those systems recently although I haven't written much about them here.  New targeting technologies create unprecedented demands for more content, which only new content technologies can meet.  So you can expect to hear more about technology-based content creation, whether I write about it or not.

Landscape of MarTech Vendor Directories

I'm making a presentation on marketing technology selection at B2BLeadsCon in New York next week, and had thought to start with the usual Oh-My-God-There-Are-So-Many-Vendors slide to get everybody's attention.  This would ordinarily be Scott Brinker's popular Chief MarTech Landscape but I've recently seen so many variations on the theme that I put together a composite slide instead.  This includes Scott's slide plus versions from Luma Partners, Gartner, MarTech Advisor, Terminus/FlipMyFunnel, and Growthverse.

I considered labeling this a "landscape of landscapes" but quickly realized that (a) it's not all that witty and (b) six vendors isn't enough.  But on further reflection, I recognized that these landscapes are really a type of directory that helps marketers find available products.  This led me to consider other types of online directories, of which there are many.  So I did end up producing a landscape that still isn't as crowded as Scott's but does show the number of information sources available.

As you see, this contains four sets of products: the original six landscapes, divided between the static images and the two interactive options (both very cool).  In addition, there are two directories with analyst ratings, from Gleanster and TopAlternatives.  But the biggest category is the community review sites, of which the best known among marketers are probably G2 Crowd, TrustRadius, and Software Advice.  Because the purpose here is to list tools that help marketers find systems to purchase, I didn't extend the landscape to business directories like Crunchbase, VentureBeat's VB Profiles and Owler.

I did look at every vendor shown in the graphic and can affirm that each includes at least some marketing systems.  There are some interesting differences in approach but, like any good landscape creator, I'll simply give you a set of logos and let you research from there. Again following the tradition of landscape publishers, I make no claims about the completeness of my list or the quality of any of the companies listed.  But I will make your life a bit easier by listing all the links below.  Enjoy!

AlternativeTo
AppAppeal
BestVendor
Chief MarTech
Cloudswave
Credii
DiscoverCloud
GetApp
G2 Crowd
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New Paper -- Which Social Media Marketing Methods Are Best For You?

I’ve long been intrigued by the notion that complex business decisions can be reduced to relatively simple rules of thumb.   Perhaps I shouldn’t admit this, at least when it comes to decisions I might be paid to make as a consultant. But I think I may safely say that simple rules can often provide a default decision that still needs confirmation by an expert. This gives people a clearer picture of the factors that go into making a choice without anyone getting hurt.

The good people at CommuniGator, a UK-based marketing automation system, were kind enough to sponsor a paper along those lines.  It's a do-it-yourself guide to picking social media marketing methods. You can download the actual paper here.  In this post, I’ll just outline the general approach – something I think can be applied to other situations as well.

The approach is built around three factors: the marketing methods available; the marketer's goals; and the marketer’s "situation", an umbrella term that includes resources, technology, and industry. It’s balancing  goals against the situation that's tricky: if resources were not a problem, you’d just use whatever methods fit your goals, and if goals didn’t matter, you’d just pick whatever methods fit your situation. No doubt plenty of companies have made exactly these errors. One virtue of a systematic approach is that it forces marketers to consider both dimensions.

For the social media marketing paper, we defined eight goals: attract attention, build brand reputation, generate qualified traffic, generate qualified leads, nurture relationships with leads, retain and grow existing customers, provide customer support, and gather market intelligence. You’ll notice these map nicely to the usual customer lifecycle stages.

We considered seven social media methods: monitoring social behavior, generating publicity, pushing content, making content sharable, promoting content on social bookmarking sites like StumbleUpon and Reddit, working with influencers, and managing reviews on sites like Yelp! and TrustRadius.

Finally, we have the situation. Resources include ability to generate large volumes of content, ability to generate high quality content, existing social media traffic, available support staff, and budget. Technology includes available execution tools and measurement tools. Industry considers whether customers are highly engaged in the products, how easily they can find product information, and whether the product is a locally-created service such as a restaurant or home repairs.

The actual mechanics of relating these factors are something you’ll best understand by downloading the paper. Suffice it to say that we assigned points to show which goals were more or less suited to each method, which let us identified the most appropriate methods for the marketer's goals.  We then used another set of points to find which resources, technologies, and industry characteristics are most important for each method’s success. This let us judge which of the previously selected methods were most likely to be executed successfully.  And that's what we wanted to know.

I felt the results were pretty reasonable, but then I’m biased. Please take a look and let me know what you think.

Intent Data Basics: Where It Comes From, What It's Good For, What To Test

Intent data is a marketer’s dream come true: rather than advertising to mass audiences in the hope of getting a handful of active buyers to identify themselves, just buy a list of those buyers and talk to them directly. It lops a whole layer off the top of the funnel and finally lets you discard the wasted half of your advertising.

But intent data is a complicated topic. It comes from different places, has different degrees of accuracy and coverage, and can be used in many different ways. Here’s a little primer on the topic.

What is intent data? It’s data that tells you an addressable individual is interested in your product. Ideally, that individual will be identifiable, meaning you have an email address, postal address, device ID, mobile app registration, phone number, or other piece of information that tells you who they are and lets you communicate with them directly. But sometimes you may only have an anonymous cookie or segment identifier that lets you reach them through only one channel and not by name.

Where does it come from? Lots of places. Behavior your company captures by itself is first-party intent data.  This is probably the most reliable but only applies to people you already know. Behavior captured by others is third-party intent data.  It's the most interesting because it provides new names or new information about names already in your database. Search queries are an obvious indicator of intent, but search engine vendors don’t sell lists based on query terms because they’d rather sell you ads based on those terms. So most third-party intent data is based on visits to Web pages whose content attracts prospective buyers of specific products. A smaller portion comes from other behaviors such as downloads, email clicks, or social media posts.

How is it sold? There are two primary formats. The first is ads served to people who have shown intent; this is generally called retargeting. The people are identified by cookies or device IDs and can be shown ads on any Web site that is part of the retargeter’s network. The original action could happen on your own Web site – the classic example is an abandoned shopping cart – or on some other site. Intent-based ads on social networks work roughly the same way, although the users are known individuals because they had to sign in. Retargeting is arguably a form of advertising and therefore not intent “data” at all. But I’m including it here because so many retargeting vendors describe their products in terms of intent. The second format is lists of email addresses. These are clearly data.  The addresses are usually gathered through user registration with the Web site.  Alternatively, the email can be derived from the cookie or device through matching services like Acxiom LiveRamp.

How reliable is it? Good question. Hopefully everyone reading knows that data isn’t perfect. But intent data is especially dicey because it comes from many different sources, some of which may be stronger indicators of intent than others. Users generally can’t see the original source. In addition, the data is aggregated by assigning the original Web content to standardized categories.  These may not precisely match your own products, or they may be grouped together so that some highly relevant intent is mixed with a lot of less relevant intent.  These problems are especially acute for B2B marketers, whose products may have a very narrow focus.  There’s also an issue of freshness: intent can change rapidly, either because someone already made their purchase or because their interests have shifted. So behavior that isn’t gathered and processed quickly may be obsolete by the time it reaches the marketer.

How complete is it? It’s worth distinguishing completeness from reliability because completeness is a big problem on its own. Intent vendors won’t necessarily capture every person in market for a particular product. In fact, depending on the situation, they may capture just a small fraction. Some people may not visit any site in the aggregator’s network; some may not visit often enough to register the required level of interest; some may decline to provide their identity or delete their cookies. In some businesses, reaching only a small fraction of interested buyers is still very useful; in others – especially where there are relatively few buyers to begin with – the marketer may be forced to run her own outreach programs to capture as many as possible.  In that case, the intent vendor's list would probably not include enough additional new names to be worth buying. 

How do you use it? More ways than you might think. It’s tempting just treat intent-based lists as sales leads.  But often the quality isn't high enough for that.  So the intent lists are often considered prospects to be touched through email, targeted advertising, and other low-cost media. Similarly, retargeting ads can be used to make hard sales offers or to more gently present brand messages and name-capturing content. Other applications include using presence on an intent list as a data point in a lead score, reaching out to dormant leads or current customers who suddenly register on intent lists, and tailoring messages based on the which topics the intent vendor finds an individual is consuming.

How do you test it? On the simplest level, you just apply the intent data to whatever type of program you’re testing (sales qualification, prospecting, lead scoring, reactivation, personalization, etc.) and read the results. Where things get a little tricky is figuring out which of the names would have registered as leads through some other channel, since they should be excluded from the analysis.  Similarly, you need to carefully test how new programs like lead scoring, reactivation, or topic selection programs would have performed without the intent data – it may be the good idea was the program itself. As a general rule of thumb, expect your own data to gain power as you build a longer history, so intent data is most likely to prove valuable on names early in the buying process.

Who sells it? Consumer marketers have a wide variety of intent sources, including Nielsen eXelate, Oracle Datalogix, and Neustar for lists and AdRoll, Retargeter, Fetchback, Chango and Magnetic for retargeting. B2B marketers can work with Bombora, The Big Willow, TechTarget, IDG, and Demandbase.

VEST Report: Competition in B2B Marketing Automation Isn't About Features

Yesterday I released the mid-year edition of the VEST Report on B2B marketing automation vendors, thereby meeting my self-imposed deadline of July 31. Look here for more information or to make a purchase.

Updating the report gives a nice overview of recent industry developments. Here are some observations:

- market positions are pretty stable. The only new vendor to make a splash recently has been SharpSpring, which went from zero to 500 agency clients in the past year. This puts them among the top 3 leaders in the small business sector. Otherwise, the top players have remained the same: Infusionsoft, HubSpot, Act-On, Salesforce Pardot, Marketo, Oracle Eloqua, and Adobe. Maybe RedPoint has crept up to a leader position, but they don’t share enough business information for me to know. Open source vendor Mautic has some interesting potential but it’s too soon to see any actual impact.

- products are pretty stable, too. The VEST entries showed very little change in the features reported by the various vendors since the last report. This isn't bad: it's simply that the standard features are now widely understood and vendors have had time to add them. The only major changes captured in the new report are the custom table abilities added by Marketo and Ontraport.

- the real action is outside the products. Probably the most interesting trend is integration of marketing automation with retargeting and display ad vendors, which has been announced in various forms by Marketo, Oracle, and Adobe. That, of course, relates to the convergence of martech and ad tech into “madtech” that I've written about before. The other big trends are systems for marketing agencies (either focused products like SharpSpring or added features and partner programs by the major vendors) and education programs for users (something that major vendors have long done but that others like Autopilot* and Mautic are also expanding). Both agencies and education are ways to support industry growth by overcoming the lack of marketers who can effectively use marketing automation systems.

- the really real action is elsewhere. Lest you think I’m just plain cranky, be assured that I see lots of exciting things happening in predictive analytics, data aggregation and enrichment, automated intelligence, and other areas. Even B2C marketing automation is showing some interesting new life. But even though B2B marketing automation revenues are still growing nicely**, the industry itself is looking pretty stable these days.

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*Not in the VEST by their choice.
** I'm estimating 40% in 2015, although it’s now harder to know because so much revenue is hidden inside companies like Oracle, Salesforce, and IBM that don’t report it separately.

Design Your Best Marketing Technology Stack and Plan the Transition: Sneak Peek at FlipMyFunnel Conference

Picture posted by Terminus

I can’t decide which is more exciting about next month’s FlipMyFunnel conference in Atlanta (register here and use the code DR50 for a 50% discount): the opportunity to interact with a great collection of speakers and attendees or seeing what the conference organizers at Terminus do with the notion of MarTech Stack Jenga. Based on one cryptic Twitter picture, they’re up to something big.

My own contribution will be a presentation on designing your marketing stack. This is something I’ve done for years as a consultant but it’s now an especially hot topic. Here are some of the key points I’ll be making:

- the stack is based on your business and marketing strategies. I’ve described the importance of strategy before but have now refined my explanation to show how marketing programs, business requirements, and functional requirements connect over-all marketing strategy with martech. The picture below also highlights the importance of planning for future business, marketing, and martech developments.

And I’ve provided a sample template for organizing your requirements by system.

- a winning stack is efficient as well as functional. I'll present a checklist for evaluating your stack design along those dimensions.

- how you draw the stack makes a difference. I’ll argue that a diagram which shows relationships between systems is more helpful than one that simply lists the different components. In the example below, the flow highlights the isolation of sales and service from the rest of the stack – a critical weakness that isn’t apparent when you look at the systems only.

- transition planning must be systematic as well. Companies struggle with transition planning even more than they struggle with stack design.  The goal is to sequence the stack changes so that each new system adds the greatest value with the least disruption. This requires understanding which system changes support each improvement.  This lets you figure out which improvements would be supported by changing any one system, what would then be possible after changing a second system, what is possible after changing a third system, and so on.  The worksheet lets you explore different sequences so you can pick the best one.

This will be easier to understand in person than in writing. Don't take my word for it: join us in Atlanta and see for yourself.

Predictive Analytics: Should Automated Content Selection Work by Segment or Individual?

Two vendors made the same point with me this week, which is reason enough for a blog post in mid-July. The point was the difference between basing content selection on individuals and on segments. I have never considered the distinction to be especially important, since segment membership is determined by individual behaviors and individual-level decisions are guided by behavior patterns of groups. But the two vendors in question (Evergage and Jetlore) and another I spoke with earlier (Sailthru) were downright religious about the superiority of their approach (individual-level selections in every case). So I thought it worth some discussion.

First, let’s clarify the topic. The distinction these vendors were making is between selecting content separately for each individual and selecting the same content for all members of a segment. Of course, customers are assigned to segments based on their individual behavior and other attributes, but once someone is in a segment, the segment-based system ignores individual differences. Among segment-based systems, collaborative filtering uses product selections almost exclusively: this is the classic “customers who looked at this product also considered these products” approach, which doesn’t take into account other aspects of the customer’s history. Other methods build segments based on customer life stage, demographics, and similar broad attributes. It’s possible to build segments based on very detailed behavioral differences, but that’s likely to create too many segments to be practical.

At an operational level, the individual-level systems use automated analytics to rank all possible content choices for each individual using that individual’s data. Segment-based systems either use rules to select content for each segment or use automated analytics to rank content choices for the segment as a whole. The individual-level approach makes the most sense when there are many content choices to consider, as with retail merchandise or entertainment (books, music, movies, etc.). Those are cases where getting precisely the right content in front of the customer is much more effective than offering everyone the most commonly-selected items. Retail and entertainment marketers also usually have detailed history which supports accurate predictions of what the customer will want. Segment-based systems work best when only a few choices are available.  This means a separate segment can be created for each item or, more realistically, segments come first and items are created to serve them.

So does the entire debate really come down to using individual-level systems when there are lots of choices and segment-based systems when there are only a few? Not really: collaborative filtering can also handle massive numbers of options with great accuracy. The difference is that collaborative filtering doesn’t really consider much beyond a particular product choice, while a sophisticated individual-level system will consider other factors including the current context and the customer’s history. Done correctly, this should yield more appropriate selections. On the other hand, individual-level approaches require more data and more complex analytics, so there will be cases where a segment-based method is ultimately more appropriate.

Moreover, the two approaches are as much complementary as competitive. A segment can indicate customer state, such as just-acquired, satisfied, or churn-risk, which constrains the contents considered for offer by the individual-level system.* Or a segment-level system could chose the type of message to send but let the individual-level system pick the specific contents. Dynamic content within email campaigns often works exactly this way.

In fact, I’d argue that state-based segmentation is essential for individual-level optimization because states provide a framework to organize the masses of detailed customer data. Without tagging the customer’s current state during each event, it would be very difficult for even the most sophisticated analytical system to see the larger arc of the customer life cycle or to understand the relationship between specific offers and long-term outcomes.

All this has practical implications for marketers considering these systems.

- for individual-level systems, make sure they can look beyond predicting the highest immediate response rate to measuring impact on long-term objectives such as conversion or lifetime value.

- for segment-level systems, make sure they can take into account the customer’s past behaviors and attributes, not just the products they have recently purchased or considered

- for all types of systems, assess how they track and guide the customer through the stages of her long-term journey

You'll want to consider other differences between content selection systems, such as number of items they can manage, how quickly they return selections, how they incorporate items without a sales history, and what data they consider in their analysis. Just remember that making selections isn’t an end in itself: you want to make choices that will create the greatest long-term value. To do that, it’s not a choice between individual and segment level analysis. You need both.

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* See my June 25 post for a more detailed discussion of state-based campaigns.

Does Future Marketing Technology Require Perfect Data?

I mentioned in my last post that I’ve started to think in terms of three realities: today (the next two years), tomorrow (two to five years out), and later (after five years). Like the famous New Yorker magazine cover that showed a detailed knowledge of Manhattan and increasingly vague view of more distant regions, our picture of the immediate future is much more nuanced than what happens farther out. One result is an apparent assumption that future technology will work much better than today’s technology – not because anyone really thinks that future technology will be perfect, but because we can’t see where its imperfections will appear.

I’ve been thinking about this because so my own predictions are premised on increasingly detailed knowledge about customers and prospects. Both the “madtech” vision of broad access to third-party data and the “robotech” vision of delegating decisions to machines assume that effectively complete data will be available about each customer. But a quick look at today’s data shows that is far from true. Here are some factoids I’ve been gathering to illustrate the point:


- 37% of mobile ad locations are accurate to within 100 meters (Thinknear)

- 30-55% match rates for B2C individual-level onboarding (LiveRamp)

- 16-29% match rates for B2B individual-level data enrichment: (Raab Associates client tests)

- 14% match rates and low predictive value for B2B account-level intent data: (Infer)

And this doesn’t even begin to address predictive modeling, where even a 10x lift vs average still implies many errors at the individual level.

Contemplating these results does give me pause. At some point, poor data means that theoretically possible approaches are not practical because of low coverage or insufficient performance. Those constraints won’t magically vanish in the future, even though they’re not visible at this distance.

Being a technology optimist, I assume that data will get better over time. But I can’t cite much evidence to support my optimism.  If anything, the number of new data sources is outstripping improvements in existing sources. The true core challenge is identity resolution, which means associating data from different sources with the right individual profile. Cross-device matching is the current focus of this discussion but covers just part of the problem.

It’s a safe bet that perfect data won’t be available in two years or five years or probably ever. But the real question is whether enough good data will be available to support the futures I’ve been forecasting.

I think a realistic view is that some data will be more available than other data, and, as a result, some portions of the visions will happen while others do not. Customer data is likely to be richer than prospect data, since customers will grant permission to link with external data sources (or take actions that make linking easier even without their permission). Sharing among complementary companies – for example, airlines and hotels – will be easier to negotiate than sharing with anyone through public exchanges. Data about objects, such as cars or groceries or homes, should be less sensitive than data about individuals (even though there’s obviously a close relationship between objects and their owners). Data about public behaviors, such as travel and store visits, is less sensitive than data about private matters such as health care.  (See this recent Altimeter Group report for more information on consumer attitudes to privacy.)

In short, the future will remain unevenly distributed, as William Gibson observed. Marketers and the technologists who support them need both the ideal vision of how things would work in a world of perfect data (which isn’t the same as a perfect world!) and the realistic understanding of what’s likely to be practical within their planning horizon. They can then aggressively pursue opportunities revealed by the vision without chasing chimeras that will never appear. This pursuit is essential: tomorrow always comes, but the future won’t happen by itself.

Marketing Beyond MadTech: What Happens When The Robots Take Over?

I’ve recently found myself bouncing between three worlds:

- today’s world, where I spend my time reviewing software and helping marketers choose martech products. Since most of that discussion is currently phrased in terms of building a marketing stack, let’s call it the world of “stacktech”.

- tomorrow’s world, about two to five years in the future.  This is dominated by the merger between martech and adtech, a.k.a. “madtech”. The trends shaping this world are well known and many people agree on the broad outlines of what it will look like. I spend my time filling in the details since details will determine which tools and skills marketers need for success.

- the future world, out five years and beyond.* There’s much less agreement on how this will look and it’s arguably too far away for most marketers to worry about. But I do have a vision which I think may be useful to vendors and managers making long term plans. Since the dominant feature of this world will be an expanded role for machines, I’ll call it “robotech”.

Each of these worlds is very different from the others. Today’s marketing stack is still largely about tools to manage direct interactions between customers and the company. It works with the company’s own data and primarily through company-owned media like email and Web sites. Customer activities with anyone other than the company are largely invisible to the company.

By contrast, advertising and social messages in the "madtech" world are tightly integrated with company-owned channels and all customer behaviors are visible (for a price). The technical symbol of this transition is the change I wrote about last week from linear, company-driven campaign flows to customer-triggered experience plays.

The "robotech" world brings yet another radical shift.  In this future, humans have delegated increasing numbers of day-to-day decisions to their machines. My recent speeches have illustrated this with a vignette about a person in headed home in her self-driving car: she works quietly in her virtual office while her devices debate whether to stop for fuel, buy her coffee, avoid donuts, and get milk for breakfast. Only once the machines have reached a consensus do they inform her of the decision.

The example is trivial but the implications are profound. When machines buy on behalf of their owners, then marketers will sell to the machines. Since the machines will decide on the basis of algorithms, marketing becomes a matter of understanding and appealing to those algorithms. We already do this today in specialized areas like search engine optimization (“selling” to Google for a higher ranking) and programmatic media buying (providing more data about impressions so they earn higher bids). This sort of marketing is fundamentally different from both stacktech and madtech. My rough calculations show that nearly half of all consumer expenditures could eventually shift to machine control.

Humans still play an important role in the robotech world. It’s not just that they’re paying the bills for purchases by autonomous agents – a relationship familiar to any parent of a teenager. It’s also that humans are choosing the agents themselves. This is essentially a subscription: people will pay for a service that manages individual purchases. Since the details of each agent’s algorithm will be too hard to evaluate directly, the subscriptions will ultimately be purchased on the basis of trust.  This is a classic goal for traditioinal brand marketing but quite a change from the madtech focus on optimizing shorter-term metrics such as response or immediate revenue.

I don’t think the rebirth of brand marketing will mean a return to the simple-minded glories of the Mad Men era – we’ll still have all that data and all those channels to work with. But it might just possibly mean a less frantic urge to respond to every twist in the customer journey, replaced by broader, more stable messages aimed at building brand trust and a long-term relationship. In a world where customers increasingly filter out marketing messages and rely on machines to manage many steps in their customer journey, marketing approaches that deliver a few general messages may ultimately be the best use marketers can make of the limited customer attention they have available.

In sum, the transition madtech to robotech will be just as wrenching as the transition from stacktech to madtech.  Marketers should recognize that both are coming, even if it's too soon to prepare for the robotech world.  The time for that will come very quickly and it's always good to have at least thought about it in advance.

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* Serious planners think much further out, in terms of decades. But I don’t think anything usefully concrete can be predicted that far in advance.