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.

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

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 – arcana 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 arcana!), 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.




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*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. 

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.
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* 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.

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 Salesforce.com, 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.