Thursday, October 20, 2016 Offers A Customer Data Platform for B2B Marketers

The need for a Customer Data Platform – a marketer-controlled, unified, persistent, accessible customer database – applies equally to business and consumer marketing. Indeed, many of the firms I originally identified as CDPs were lead scoring and customer success management vendors who serve primarily B2B clients. But as the category has evolved, I’ve narrowed my filter to only consider CDPs as companies that focus primarily on building the unified data.  This excludes the predictive modeling vendors and customer success managers, as well as the big marketing clouds that list a CDP as one of many components. Once you apply that filter, nearly all the remaining firms sell largely to B2C enterprise clients. is an exception. Its clients are mostly small, B2B companies – exactly the firms that were first to adopt software-as-a-service (SaaS) technologies including marketing automation and CRM. This is no accident: SaaS solves one problem by making it easy to acquire new systems, but that creates another problem because those systems are often isolated from each other. Hull addresses that problem by unifying their data, or, more precisely, by synchronizing it.

How it works is this: Hull has connectors for major customer-facing SaaS systems, such as Salesforce, Optimizely, HubSpot, Mailchimp, Facebook custom audiences, Slack, and Zendesk. Users connect with those systems and specify data elements or lists to synchronize. When data changes in one of customer-facing products, the change is sent to Hull which in turn sends it to other products that are tracking that data.

But, unlike data exchanges such as Zapier or Segment, Hull also keeps its own copy of the data. That’s the “persistent” bit of the CDP definition. It gives Hull a place to store data from enhancement vendors including Datanyze and Clearbit, from external processes called through Javascript, and from user-defined custom variables and summary properties, such as days since last visit. Those can be used along with other data to create triggers and define segments within Hull.  The segments can then be sent to other systems and updated as they change.

In other words, even though the external systems are not directly reading the data stored within Hull, they can still all work with consistent versions of the data.* Think of it as the martech equivalent of Einstein’s’ “spooky action at a distance”  if that clarifies things for you.

To extend its reach even further, can also integrate with Zapier and Segment allowing it to exchange data with the hundreds of systems those products support.

Three important things have to happen inside of to provide a unified customer view. First, it has to map data from different sources to a common data model – so that things like customer name or product ID are recognized as referring to the same entities even if they come from different places. simplifies this as much as possible by limiting its internal data model to two entities, customers and events.  Input data, no matter how complicated, is converted to these entities by splitting each record into components that are tagged with their original meaning and relationships. The splitting and tagging are automatic, which is very important for making the system easy to deploy and maintain.  Users still need to manually tell the system which elements from different systems should map to the same element in the shared data.

The second important thing is translating stored data into the structure needed by the receiving system. This is the reverse of the data loading process, since complex records must be assembled from the simplified internal model. What’s tricky is that the output format is almost always different from the input format, so the pieces have to be reassembled in a different format.  While we’re making questionably helpful analogies, think of this as the Jive Lady translating for the sick passenger in the movie Airplane.

The third key thing is that data relating to the same customer needs to be linked. Hull will do “deterministic” matching to stitch together identities where overlapping information is available – such as, connecting an account ID to a device when someone uses that device to log into their account. Like many other CDPs, Hull doesn’t attempt “probabilistic” matching, which looks for patterns in behavior or data to associate identifiers that are likely to belong to the same person. It does use IP address to associate visitors with businesses, even if the individual is anonymous.

All told, this adds up to a respectable set of CDP features. But Hull co-founder Romain Dardour says few clients actually come to the company looking for a unified, persistent customer database. Rather, they are trying to create specific processes, such as using Slack to send notifications of support tickets from Zendesk. Hull has built a collection of these processes, which it calls recipes. Customers can use an existing recipe or design their own. Dardour said that once clients deploy a few recipes they usually recognize the broader possibilities of the system and migrate towards thinking of it as a true CDP, even if they still don’t use the term.

This is consistent with what I’ve seen elsewhere.  Big enterprises can afford to purchase a unified customer database by itself, but smaller firms often want their CDP to include a specific money-making application. That’s why my original B2B CDPs usually included applications like lead scoring and customer success, while the B2C enterprise CDPs often did not.

The other big divide between Hull and enterprise CDPs is cost. Most enterprise CDPs start somewhere between $100,000 and $250,000 per year and can easily reach seven figures. Hull starts as low as $500 per month, with a current average of about $1,000 and the largest clients topping out around $10,000. Price is based primarily on the number of system connections, with some adjustments for number of contact records, guaranteed response time, data retention period, and special features. Hull has over 1,000 clients, mostly in the U.S. but with world-wide presence. It was founded in 2013.

*You could argue that because the external systems are not reading’s data directly, it doesn’t truly qualify as a CDP. I’d say it’s not worth the quibble – although if really massive amounts of data were involved, it might be significant. Remember that is dealing with smaller businesses, where replicating all the relevant data is not a huge burden.

Friday, October 14, 2016

Datorama Applies Machine Intelligence to Speed Marketing Analytics

As I mentioned a couple of posts back, I’ve been surveying the borders of Customer Data Platform-land recently, trying to figure out which vendors fit within the category and which do not. Naturally, there are cases where the answer isn’t clear. Datorama is one of them.

At first glance, you’d think Datorama is definitely not a CDP: it positions itself as a “marketing analytics platform” and makes clear that its primary clients are agencies, publishers, and corporate marketers who want to measure advertising performance. But the company also calls itself a “marketing integration engine” that works with “all of your data”, which certainly goes beyond just advertising. Dig a bit deeper and the confusion just grows: the company works mostly with aggregated performance data, but also works with some individual-level data.  It doesn’t currently do identity resolution to build unified customer profiles, but is moving in that direction. And it integrates with advertising and Web analytics data on one hand and social listening, marketing automation, and CRM on the other. So while Datorama wasn’t built to be a CDP – because unified customer profiles are the core CDP feature – it may be evolving towards one.

This isn't to say that Datorama lacks focus. The system was introduced in 2012 and now has over 2,000 clients, including brands, agencies, and publishers. It grew by solving a very specific problem: the challenges that advertisers and publishers face in combining information about ad placements and results. Its solution was to automate every step of the marketing measurement process as much as it could, using machine intelligence to identify information within new data sources, map those to a standard data model, present the results in dashboards, and uncover opportunities for improvement. In other words, Datorama gives marketers one system for everything from data ingestion to consolidation to delivery to analytics.  This lets them manage a process that would otherwise require many different products and lots of technical support. That approach – putting marketers in control by giving them a system pre-tailored to their needs – is very much the CDP strategy.

Paradoxically, the main result of Datorama’s specialization is flexibility. The system’s developers set of goal of handling any data source, which led to a system that can ingest nearly any database type, API feed or file format, including JSON and XML; automatically identify the contents of each field; and map the fields to the standard data model. Datorama keeps track of what it learns about common source systems, like Facebook, Adobe Analytics, or AppNexus, making it better at mapping those sources for future implementations. It can also clean, transform, classify, and reformat the inputs to make them more usable, applying advanced features like rules, formulas, and sentiment analysis. At the other end of the process, machine learning builds predictive models to do things like estimate lifetime value and forecast campaign results. The results can be displayed in Datorama’s own interface, read by business intelligence products like Tableau, or exported to other systems like marketing automation.

Datorama’s extensive use of machine learning lets it speed up the marketing analytics process while reducing the cost. But this is still not a push-button solution. The vendor says a typical proof of concept usually takes about one month, and it takes another one to two months more to convert the proof of concept into a production deployment. That’s faster than your father’s data warehouse but not like adding an app to your iPhone. Pricing is also non-trivial: a small company will pay in the five figures for a year’s service and a large company's bill could reach into seven figures. Fees are based on data volume and number of users. Datorama can also provide services to help users get set up or to run the system for them if they prefer.

Tuesday, October 04, 2016

News from Krux, Demandbase, Radius: Customer Data Takes Center Stage

If Dreamforce seems a little less crowded than you expected this week, perhaps it's because I didn’t attend. But I’m still tracking the news from Salesforce and other vendors from my cave in Philadelphia. Three announcements caught my eye, all highlighting the increasing attention being paid to customer data.

Salesforce itself had the biggest news yesterday, with its agreement to purchase Krux, a data management platform that has expanded well beyond the core DMP function of assembling audiences from cookie pools. Krux now has an “intelligent marketing hub” that can also load a company’s own data from CRM, Websites, mobile apps, and offline sources, and unify customer data to build complete cross-channel profiles. Krux also allows third party data owners to sell their data through the Krux platform and offers self-service data science for exploration and predictive models. The purchase makes great strategic sense for Salesforce, providing it with a DMP to match existing components in the Oracle and Adobe marketing clouds. But beyond the standard DMP function of generating advertising audiences, Krux gives Salesforce a solid customer data foundation to support all kinds of marketing management.  In particular, it goes beyond the functions in Salesforce ExactTarget, which was previously the designated core marketing database for Salesforce Marketing Cloud. To be clear, there’s no campaign management or journey orchestration within Krux; those functions would be performed by other systems that simply draw on Krux data. Which is exactly as it should be, if marketers are to maintain maximum flexibility in their tools.

Demandbase had its own announcement yesterday: something it calls “DemandGraph,” which is basically a combination of Demandbase’s existing business database with data gathering and analytical functions the Spiderbook system that Demandbase bought in May 2016. DemandGraph isn’t exactly a product but rather a resource that Demandbase will use to power other products. It lets Demandbase more easily build detailed profiles of people and companies, including history, interests, and relationships. It can then use the information to predict future purchases and guide marketing and sales messages. There’s also a liberal sprinkling of artificial intelligence throughout DemandGraph, used mostly in Spiderbook’s processing of unstructured Web data but also in some of the predictive functions. If I’m sounding vague here it’s because, frankly, so was Demandbase. But it’s still clear that DemandGraph represents a major improvement in the power and scope of data available to business marketers.

Predictive marketing vendor Radius made its announcement last week of the Radius Customer Exchange.  This uses the Radius Business Graph database (notice a naming trend here?) to help clients identify shared customers without exposing their entire files to each other. Like Spiderbook, Radius gathers much of its data by scanning the public Web; however, Radius Business Graph also incorporates data provided Radius clients. The client data provides continuous, additional inputs that Radius says makes its data and matching much more accurate than conventional business data sources. Similarly, while there’s nothing new about using third parties to find shared customers, the Radius Customer Exchange enables sharing in near real time, gives precise revocable control over what is shared, and incorporates other information such as marketing touches and predictive models. These are subtle but significant improvements that make data-driven marketing more effective than ever. The announcement also supports a slight shift in Radius’ position from “predictive modeling” (a category that has lost some of its luster in the past year) to “business data provider”, a category that seems especially enticing after Microsoft paid $26.2 billion for LinkedIn.

Do these announcements reflect a change in industry focus from marketing applications to marketing data? I’m probably too data-centric to be an objective judge, but a case could be made. If so, I’d argue it’s a natural development as marketers look beyond the endless supply of sparkly new Martech applications to the underlying foundations needed to support them. In the long run, a solid foundation makes it easier to dance creatively along the surface: so I’d rate a new data-driven attitude as a Good Thing.

Friday, September 30, 2016

Reltio Makes Enterprise Data Usable, and Then Uses It

I’ve spent a lot of time recently talking to Customer Data Platform vendors, or companies that looked like they might be. One that sits right on the border is Reltio, which fits the CDP criteria* but goes beyond customer data to all types of enterprise information. That puts it more in the realm of Master Data Management, except that MDM is highly technical while Reltio is designed to be used by marketers and other business people. You might call it “self-service MDM” but that’s an oxymoron right up there with “do-it-yourself brain surgery”.

Or not. Reltio avoids the traditional complexity of MDM in part by using the Cassandra data store, which is highly scalable and can more easily add new data types and attributes than standard relational databases. Reltio works with a simple data model – or graph schema if you prefer – that captures relationships among basic objects including people, organizations, products, and places. It can work with data from multiple sources, relying on partner vendors such as SnapLogic and MuleSoft for data acquisition and Tamr, Alteryx, and Trifacta for data preparation. It has its own matching algorithms to associate related data from different sources. As for the do-it-yourself bit: well, there’s certainly some technical expertise needed to set things up, but Reltio's services team generally does the hard parts for its clients. The point is that Reltio reduces the work involved – while adding a new source to a conventional data warehouse can easily take weeks or months, Reltio says it can add a new source to an existing installation in one day.

The result is a customer profile that contains pretty much any data the company can acquire. This is where the real fun begins, because that profile is now available for analysis and applications. These can also be done in Reltio itself, using built-in machine learning and data presentation tools to provide deep views into customers and accounts, including recommendations for products and messages. A simple app might take one or two months to build; a complicated app might take three or four months. The data is also available to external systems via real-time API calls.

Reltio is a cloud service, meaning the system doesn’t run on the client’s own computers. Pricing depends on the number of users and profiles managed but not the number of sources or data volume. The company was founded in 2011 and released its product several years later. Its clients are primarily large enterprises in retail, media, and life sciences.

* marketer-controlled; multi-source unified persistent data; accessible to external systems

Monday, September 19, 2016

History of Marketing Technology and What's Special about Journey Orchestration

I delivered my presentation on the history of marketing technology last week at the Optimove CONNECT conference in Tel Aviv. Sadly, the audience didn’t seem to share my fascination with arcana (did you know that the Chinese invented paper in 100 CE? that Return on Investment analysis originated at DuPont in 1912?) So, chastened a bit, I’ll share with you a much-condensed version of my timeline, leaving out juicy details like brothel advertising at Pompeii.

The timeline* traces three categories: marketing channels; tools used by marketers to manage those channels; and data available to marketers.  The yellow areas represent the volume of technology available during each period. Again skipping over my beloved details, there are two main points:
  • although the number of marketing channels increased dramatically during the industrial age (adding mass print, direct mail, radio, television, and telemarketing), there was almost no growth in marketing technology or data until computers were applied to list management in the 1970’s. The real explosions in martech and data happen after the Internet appears in the 1990’s.

  • the core martech technology, campaign management, begins in the 1980’s: that is, it predates the Internet. In fact, campaign management was originally designed to manage direct mail lists (and – aracana alert! – itself mimicked practices developed for mechanical list technologies such as punch cards and metal address plates). Although marketers have long talked about being customer- rather than campaign-centric, it’s not until the current crop of Journey Orchestration Engines (JOEs) that we see a thorough replacement of campaign-based methods.

It’s not surprising the transition took so long. As I described in my earlier post on the adoption of electric power by factories (more aracana!), the shift to new technology happens in stages as individual components of a process are changed, which then opens a path to changing other components, until finally all the old components are gone and new components are deployed in a configuration optimized for the new capabilities. In the transition from campaign management to journey orchestration, marketers had to develop tools to track individuals over time, to personalize messages to those individuals, identify and optimize individual journeys, act on complete data in real time, and to incorporate masses of unstructured data. Each of those transitions involved a technology change: from lists to databases, from static messages to dynamic content, from segment-level descriptive analytics to individual-level predictions, from batch updates to real time processes, and from relational databases to “big data” stores.

It’s really difficult to retrofit old systems with new technologies, which is one reason vendors like Oracle and IBM keep buying new companies to supplement current products. It’s also why the newest systems tend to be the most advanced.** Thus, the Journey Orchestration Engines I’ve written about previously (Thunderhead ONE , Pointillist, Usermind, Hive9 ) all use NoSQL data stores, build detailed individual-level customer histories, and track individuals as they move from state to state within a journey flow.

During my Tel Aviv visit last week, I also checked in with Pontis (just purchased by Amdocs), who showed me their own new tool which does an exceptionally fine job at ingesting all kinds of data, building a unified customer history, and coordinating treatments across all channels, all in real time. In true JOE fashion, the system selects the best treatment in each situation rather than pushing customers down predefined campaign sequences. Pontis also promised their February release would use machine learning to pick optimal messages and channels during each treatment. Separately, Optimove itself announced its own “Optibot” automation scheme, which also finds the best treatments for individuals as they move from state to state. So you can add Optimove to your cup of JOEs (sorry) as well.

I’m reluctant to proclaim JOEs as the final stage in customer management evolution only because it’s too soon to know if more change is on the way. As Pontis and Optimove both illustrate, the next step may be using automation to select customer treatments and ultimately to generate the framework that organizes those treatments. When that happens, we will have erased the last vestiges of the list- and campaign-based approaches that date back to the mail order pioneers of the 19th century and to the ancient Sumerians (first customer list, c. 3,000 BCE) before that.

*Dates represent commercialization, not the first appearance of the underlying technology. For example, we all know that Gutenberg’s press with moveable type was introduced around 1450, but newspapers with advertising didn’t show up until after 1600.

** This isn’t quite as tautological as it sounds. In some industries, deep-pocketed old vendors with big research budgets are the technical leaders. 

Thursday, September 15, 2016

How Quickly Is the MarTech Industry Growing?

Everyone in marketing knows there’s a lot of new marketing technology, but how quickly is martech really growing? Many people cite changes in Scott Brinker’s iconic marketing technology landscape, which has roughly doubled in size every year since Brinker first published it in 2011. Brinker himself is always careful to stress that his listings are not comprehensive, and anyone familiar with the industry will quickly realize much of the growth in his vendor count reflects greater thoroughness and broader scope rather than appearance of new vendors. But no matter how many caveats are made, the ubiquity of Brinker’s chart leaves a strong impression of tremendously quick expansion.

Fortunately, other data is available. Venture Scanner recently published the the number of companies founded by year for 1,295 martech firms in its database. This shows growth of around 12% per year from 2000 through 2012. (Figures for 2013 and later are almost surely understated because many firms started during those years have not yet been included in the data.)

A similar analysis from CabinetM, which has a database of 3,708 companies, showed a slightly higher rate of 14.5% per year for the same period.* Both sets of data show a noticeable acceleration after 2006: to about 16.5% for Venture Scanner and just under 16% for CabinetM.

These figures are still far from perfect. Many firms are obviously missing from the Venture Scanner data. CabinetM has apparently missed many as well: Brinker reported that comparison between CabinetM’s list and his own found that each had about 1,900 vendors the other did not. All lists will miss companies that are no longer in business, so there were probably more start-ups in each year than shown.

But even allowing for such issues, it’s probably reasonable to say that the number of vendors in the industry has been growing at something from 15% to 20% per year. That’s a healthy rate but nothing close to an annual doubling.

Note also that we’re talking here about the number of companies, not revenue.  I suspect revenue is growing more quickly than the number of vendors but can't give a meaningful estimate of how much.

Are particular segments within the industry growing faster than others? CabinetM provided me with a breakdown of starts by year by category.** To my surprise, growth has been spread fairly evenly across the different types of systems. Adtech grew a bit faster than the other categories in 2006 to 2010 and content marketing has grown faster than the average since 2006. But the share of marketing automation and operations have been surprisingly consistent throughout the period covered. So while the number of marketing automation vendors has indeed grown quickly, other categories seem to growing at about the same pace.

So what, if anything, does this tell us about the future?  It's certainly possible some of the drop-off in new vendors since 2013 reflects an actual slowdown in addition to the lag time before new vendors appear in databases. Funding data from Venture Scanner suggests that 2015 may have been a peak year for investments, although 2016 data is obviously incomplete.
Another set of funding data, from PitchBook, suggests 2014 was a peak but shows much less year-on-year variation than Venture Scanner. The inconsistency between the two sets of data makes it hard to accept either source as definitive.

So, what does this all mean?  First of all, that people should calm down a bit: the number of martech vendors hasn't been doubling every year.  Second, that industry growth may indeed be slowing, although it's too soon to say for sure.  Third, whatever the exact figures, there are plenty of martech vendors out there and they're not going away any time soon.  So marketers need to focus on a systematic approach to martech acquisition, balancing new opportunities against training and integration costs.
* Here's the actual CabinetM data.  I'm mostly showing this to clarify that my "growth rate" is comparing the number of new companies vs. total industry size, and not the number of new companies this year vs new companies last year.

**CabinetM actually tracks 30 categories.  I combined them into the seven groups used here.

Wednesday, September 07, 2016

Will Marketing Technologists Kill Martech?

I’ll be giving a speech next week on the evolution of marketing technology, which doesn't follow the path you might think. The new channels that appear on a typical “history of marketing timeline”, such as radio in the 1920’s and TV in the 1950’s, didn’t really trigger any particular changes in the technology used by marketers: planning was still done on paper spreadsheets and copy was typed manually up to the 1970’s. Similarly, marketers up that time worked with the same data – audience counts and customer lists – they had since Ben Franklin and before.

It was only in the 1960’s, when mailing lists were computerized, that new technologies begin to make more data available and marketers get new tools to work with it. Those evolved slowly – personalized printing and modern campaign managers appeared in the 1980’s. The big changes started in the 1990’s when email and Web marketing provided a flood of data about customer behaviors and vendors responded with a flood of new systems to work with it. But it wasn't until the late 2000’s that the number of vendors truly exploded.

I can’t prove this, but I think what triggered martech hypergrowth was Software-as-a-Service (SaaS). This made it easy for marketers to purchase systems without involving the corporate IT department, allowing users to buy tools that solved specific problems whether or not the tools fit into the corporate grand scheme of things. Major SaaS vendors, most notably, made their systems into platforms that provided a foundation for other systems. This freed developers to create specialized features without building a complete infrastructure. Building apps on platforms also sharply reduced integration costs, which had placed a severe limit on how many systems any marketing department could afford. Easier development, easier deployment, and easier acquisition created perfect environment for martech proliferation.

But every action has a reaction. The growth of martech led to the hiring of marketing technologists, as marketing departments realized they needed someone to manage their burgeoning technology investments. That might seem like a good thing for the martech industry, but it introduced a layer of supervision that restrained the free-wheeling purchases that marketers had been  making on their own. After all, the job of a martech manager is to rationalize and coordinate martech investments, which ultimately means saying “no.”

The quest for rationalization leads to long-term planning, vision development, architecture design, corporate standards, and project prioritization: all the excellent practices that made corporate IT departments so unresponsive to marketers in the first place. The scrappy rebels in martech departments hear the call of order-obsessed dark side and find it increasingly hard to resist.

And it only gets worse (from the martech vendor point of view). As marketing technologists discover just how many systems are already in place, they inevitably ask how they can make things simpler. The equally inevitable answer is to buy fewer systems by finding systems that do more things. This leads to integrated suites – marketing clouds, anyone? – that may not have the best features for any particular function but offer a broad range of capabilities. When the purchase is made by individual marketers focused on their own needs, the best features will win and small, innovative martech vendors can flourish. But when purchases are managed by the central martech department, integration and breadth will weigh more heavily in the decision.  This gives bigger, most established firms the advantage.

In short, martech today is at a crossroads. Martech managers can follow the natural logic of their positions, which leads to greater centralization, large multi-function systems, and increasingly frustrated marketers. Or they can retain their agility and support new, innovative martech vendors, recognizing that near-term efficiency will suffer. Put so starkly, it’s obvious that agility is the better choice, and there is plenty of discussion in the industry of how to maintain it. But the dark side is powerful, relentless, and seductively rational. Martech managers – and the marketers they ultimately serve – must tread carefully to stay on the right path.