NICE Buys Causata to Extend Its Customer Experience Management Position

So, there I was around 7:30 Eastern time this morning, sending out reminder notices to vendors I need to interview for an upcoming report on Customer Data Platforms. I received an immediate response from the Kevin Nix of Causata, offering to talk that very morning. This seemed a bit odd – Causata is based in San Francisco, so it was 4:30 a.m. local time and most people need more notice to schedule a call. But I had Things To Do, so I didn't give it much thought. Then, at the end of another call, a participant casually mentioned that Causata had just been purchased by Israel-based NICE Systems.  At first I was struck by the coincidence, and then realized what had happened: Nix was up because he had been talking to the folks in Israel, and he replied because he wanted to discuss his acquisition, not my report. [Insert image of deflating self-importance].

Sure enough, when I did dial in, I was treated to a prepared briefing on why NICE had made the deal.

There’s really nothing wrong with that. NICE is little-known in marketing circles, although I had bumped into them previously when they bought decision management vendor eGlue in 2010. But NICE is a major player in contact center systems, with nearly $1 billion revenue and $2.5 billion stock market capitalization. So I was pleased to connect with them directly and learn a bit more.

The briefing itself was interesting too. It turns out that while NICE still sells primarily to contact center managers, it is working hard to expand to clients in marketing, sales, compliance (it bought Actimize in 2007) and other areas related to customer experience. Its interest in Causata related to all  that, and in particular to that fact that Causata can capture Web interactions in real time and present them with related recommendations to contact center agents and other systems. This pumped me back up a bit, since it can be read as validation of the Customer Data Platform concept that I’ve been developing, which is about exactly this need to make customer data easily available across platforms. In fact, Causata was the original example I used to introduce the idea.

But enough about me, at least for the moment. The idea of NICE expanding to become an all-channel, all-department customer experience vendor immediately raises the question of how they’ll compete with all those other omni-everythings approaching from digital marketing (Adobe), B2B CRM (Salesforce.com), and general enterprise systems (Oracle, SAP, IBM). The contact center world has actually been a font of decision management systems, most notably Chordiant (now part of Pegasystems) and Infor Epiphany. So it’s certainly possible that they will be another source of competitors converging on the market for integrated customer experience management solutions. Like the CRM and Web content management vendors, the contact center firms start from a strong customer and financial base, making them formidable contenderss in what will surely be a long battle for high stakes.

I haven’t formed a solid opinion yet on how NICE in particular or contact center vendors in general are likely to fare in this new arena. But they are definitely something to factor into future assessments.

I've Discovered a New Class of System: the Customer Data Platform. Causata Is An Example.

It has taken me a while to connect the dots, but I’m now pretty sure I see a new type of software emerging. These systems that gather customer data from multiple sources, combine information related to the same individuals, perform predictive analytics on the resulting database, and use the results to guide marketing treatments across multiple channels. This differs quite radically from standard marketing automation systems, which use databases built elsewhere, rarely include integrated predictive modeling, and are focused primarily on moving customers through multi-step campaigns. In fact, the new systems complement rather than compete with marketing automation, which they treat as just one of several execution platforms. The new systems can also feed sales, customer service, online advertising, point of sale, and any other customer-facing systems.

Given how much vendors and analysts love to create new categories, I’m genuinely perplexed that no one has yet named this one. I’ll step in myself, and hereby christen the concept as “Customer Data Platform”.  Aside from having a relatively available three letter abbreviation (see Acronym Finder for other uses of CDP), the merits of this name include:

- “Customer” shows the scope extends to all customer-related functions, not just marketing;
- “Data” shows the primary focus is on data, not execution; and
- “Platform” shows it does more than data management while supporting other systems

But, you may ask, is this really new? Certainly systems for Customer Data Integration (CDI) have been around for decades: these include specialized products like Harte-Hanks Trillium and SAS DataFlux, CDI features within general data management suites like Informatica and Pentaho, and integration within cloud-based business intelligence products like GoodData and Birst. Many of those products have limited capabilities for working with newer data sources like Web sites and social networks, but the real distinction between them and CDPs is that the older systems are mainly designed to assemble data.  Some also provide analytics, but they don't extend to real-time decisions based on predictive models.

Similarly, there have long been specialized systems for real-time interaction management (such as Infor Interaction Advisor and Oracle Real Time Decisions) and for predictive modeling (SAS, IBM SPSS, KXEN). Some interaction managers do create predictive models, and the really big vendors (IBM, SAS, Oracle) have all three key components (CDI, real-time decisions, and predictive models) somewhere in their stables. But systems that closely couple just those features with the goal of feeding data as well as recommendations to execution systems? Those are something new.

By now, you’re probably wondering if I’ll ever get around to actually naming the vendors I have in mind. I’ve recently written about some of them, including Reachforce/SetLogik and Lattice Engines.  I also include RedPoint in the mix, because it has all the key capabilities (database development, predictive models, and real time decisions) even though it also offers conventional campaign management. Others I haven’t yet written about include Mintigo and Gainsight. Of course, each has a different mix of features and its own market position.  Indeed, several have specifically told me they do not compete with the others. Fair enough, but I still see enough similarity to group them together.

All this is a very long-winded introduction to Causata, yet another member of this new class. By now, you can probably guess Causata’s main functions: assemble customer data from multiple sources, consolidate it by customer, place it in an analytics-friendly format, run predictive models against it, and respond in real time to recommendation requests from other systems including Web sites, email, banner ads, and call centers. And you’d be right.

But that’s not the end of the story. With any product, it’s the details that matter. Causata is particularly strong in the data management department, accepting both batch and real-time data feeds and storing data as different types of events (email sent, Web site visit, call center interaction, etc.), each having predefined attributes. The system also has a particularly sophisticated “identity association” service, which looks for simultaneous events involving different identifiers as a way to link them, and can chain identifiers that were linked at different times. When I spoke with Causata about two months ago, the association rules were pretty much the same for all clients, but they promised users would get more control in the future. Users could already choose which types of associations to use in specific queries.

Causata stores the assembled data in HBase, a Hadoop-based database management system that is particularly well suited to large data volumes, many different data types, and ad hoc queries. In addition to the raw data, the system can store derived values such as aggregations (e.g., number of Web page view in past 24 hours) and model scores. Users can run SQL queries to extract data for analysis and predictive modeling in third-party software including QlikView, Tableau, SAS, and R. Prebuilt QlikView reports show the predictive power of different variables for user-specified events. The lack of native analysis and modeling tools creates some friction for users, but also lets them stick with familiar products. So the pros and cons probably cancel each other out.

The system’s decision tools are straightforward. For each situation, users define a “decision engine” that can select among multiple options, such as campaigns, products, or marketing content. These options can have qualification rules. To make a decision, the system can test the options in sequence and pick the first one for which a customer is qualified, or pick the option with the highest predictive model score. Users can also specify a percentage of customers to receive a random option, to gather data for future decisions. An engine can return multiple decisions for situations that require more than one option, such as a Web page with several offers. Causata has some machine learning algorithms to help with the decision process. It plans to expand these to automatically select the best option in a given situation.

Decision engines are called by external systems through a Web services API that can respond in under 50 milliseconds. This is fast enough to manage Web banner ads – something not all interaction managers can achieve. Model scores and other data are updated in real time during an interaction.

Causata can be deployed on-premise by a client or as a cloud-based service. The vendor says a typical implementation starts with three or four data sources and is deployed in about 30 days – very fast for this type of system. In February, Causata introduced prebuilt applications for cross-sell, acquisition, and return programs in financial services, communications, and digital media. These will further speed deployment.

Pricing is based on the number of data sources and touchpoints, with additional charges based on data storage. Cost begins around $150,000 per year.