Friday, October 28, 2016

Singing the Customer Data Platform Blues: Who's to Blame for Disjointed Customer Data?

I’m in the midst of collating data from 150 published surveys about marketing technology, a project that is fascinating and stupefying at the same time. A theme related to marketing data seems to be emerging that I didn’t expect, and many marketers won’t necessarily be happy to hear.

Many surveys present a familiar tune: most marketers want unified customer data but few have it. This excerpt from an especially fine study by Econsultancy make the case clearly, although plenty of other studies show something similar.

So far so good. The gap is music to my ears, since helping marketers fill it is what keeps consultants like me in the business. But it inevitably raises the question of why the gap exists.

The conventional answer is it’s a technology problem. Indeed, an Experian survey makes exactly this point: the top challenges are all technology related.

And, comfortingly, marketers can sing their same old song of blaming IT for failing to deliver what they need.  For example, even though 61% of companies in this Forbes Insights survey had a central database of some sort, only 14% had fully unified, accessible data.

But something sounds a little funny. After all, doesn’t marketing now control its own fate? In this Ascend2 report, 61% of the marketing departments said they were primarily responsible for marketing data and nearly all the others said they shared responsibility.

Now we hear that quavering note of uncertainty: maybe it’s marketing’s own fault? That’s something I didn’t expect. And, just maybe, the data is saying that. For example, a study from Black Ink ROI found that the top barrier to success was better analytics (which implicitly requires better data) and explicitly listed data access as the third-ranked barrier.

But – and here’s the grand finale – the same study found that data integration software ranked sixth on the marketers’ shopping lists. In other words, even though marketers knew they needed better data, they weren’t planning to spend money to make it happen. That’s a very sour chord indeed.

But the song isn't over.  If we listen closely, we can barely make out one final theme: marketers won’t invest in data management technology because they don’t have the skills to use it. At least, that’s what this survey from suggests.

In its own way, that’s an upbeat ending: expertise can be acquired, either through training or hiring outside experts (or possibly even mending some fences with the IT team). Better tools, like Customer Data Platforms, can help by reducing the expertise needed. So while marketers aren't parading towards a complete customer view with a triumphal Sousa march, there’s no need for a funeral dirge quite yet.

Wednesday, October 26, 2016

Survey on Customer Data Management - Please Help!

I'm working with MarTech Advisor on a survey to understand the state of customer data management.  If you have five minutes or so, could you please fill it out?  Link is here.  And if possible, pass on to people in other companies who could also help.  You'll get a copy of the final report and my gratitude.  Just for reading this far, here's a kitten:

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.