It’s a slight stretch to call Provenir a CDP, because it doesn’t manage a permanent customer database. Rather, like most interaction managers, it calls data from external sources during each decision. But Provenir does have some customer matching capabilities and stores at least some information internally. Moreover, it completely meets the other three CDP criteria: predictive modeling, real-time decisions/recommendations executed through external systems, and a non-technical user interface. It’s also sold as the “glue” connecting data sources, modeling, and execution systems, which is exactly the role played by a CDP. So, what the heck…welcome to the club!
Provenir is organized around process flows, which cover a particular task such as reacting to a Web site visit. Users define each process by building a flow chart, or, as the cool kids call them today, a graph.** These, um, graphs***, can contain branches, loops, and other advanced structures. The nodes can also contain other graphs that define a subprocess in more detail. Nodes can perform a wide range of operations including data gathering, calculations, updates, decisions, and messages to external systems. Although setting these up is inevitably rigorous, Provenir makes it as painless as possible by providing help such as letting users draw lines to map fields from one system to another; building rules through score cards, tables and decision trees; and warning if a flow is incomplete.
Provenir relies on external systems to assemble, integrate, and store customer data. Users can build matching processes with system graphs, although the vendor recommends connecting to other products to load reference data or do advanced "fuzzy" matching. Provenir can monitor source systems for selected events and issue queries to assemble data as needed. The social listening features can monitor Twitter for keywords and Tweets by specified individuals. These can trigger process flows that can retweet a message, send a direct Twitter message to the poster, or respond through another channel. The system can also monitor and post messages on Facebook. Other channels will be added over time.
Predictive modeling in Provenir is also done in external systems. The system can import PMML code or call models in SAS, R, or even Excel. Data mapping functions can automatically extract the list of required variables from PMML, do basic transformations and calculations when loading model inputs, and manage parameters, constants, and local variables.
Decisioning is Provenir’s greatest strength. The process flow…I mean graph…is inherently very flexible, and the ability to define rules as tables, trees, score cards, and other formats adds even more power. Users can set up champion/challenger tests as splits within a process flow; results are stored in a database for analysis and reporting. Users can also build simulated data sets, containing specified distributions of particular variables, and use these to forecast results of their flow designs. Such simulation is one mark of a mature decision system.
Provenir has some built-in messaging capabilities, but most decisions are executed externally. The system has been connected with email, Web content management, call centers, campaign management, text messaging, and other execution platforms.
Pricing for Provenir’s social listening product is based on the size of the customer database. Starting price can be as a low as several thousand dollars per month. The system is usually sold on a Software-as-a-Service (SaaS) basis, but on-premise licenses are also available.
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* For extra credit, compare and contrast Provenir’s primary Web site with the site for their listening division.
** Defined in Wikipedia as “mathematical structures used to model pairwise relations between objects”.
*** Would it be even cooler to call them grafs or, better still, grafz?
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