Pegasystems Buys Chordiant to Help Coordinate Customer Treatment Decisions

Summary: Pegasystems purchased Chordiant last week, adding a sophisticated cross-channel decision engine to its stable. It's been hard for independent decision engines to survive, even though it seems an independent product should make it easier for marketers to unify their customer treatments.

Business process technology vendor Pegasystems announced on Monday that it was purchasing Chordiant, which offers a central decision engine for customer interactions. Although the news is interesting in its own right, it also triggered a twinge of personal regret because I’ve been meaning to write about Chordiant for nearly a year. At that time, they had just added some slick simulation capabilities that estimated outcomes if a different set of rules had been applied to historical interactions.

This type of simulation allows business managers, rather than technicians, to directly assess the impact of alternative business rules. It's an important sign of maturity, showing that the vendor has shifted resources from primary system functions (making things work) to supporting functions (making things work better).

If you’re not familiar with the Chordiant decision engine, its primary function is to apply business rules that guide real-time customer treatments. It has been deployed primarily in call centers, although it is designed to work across multiple touchpoints. To accomplish this, the system must accept inputs from each touchpoint about a current interaction, apply rules to select an offer, and feed the selection back to the touchpoint. Tracking results also requires a second loop for the touchpoint to report whether the offer was actually delivered and whether it was accepted.

The business rules can use both data provided by the touchpoint and data from other systems such as transaction and marketing databases. The rules frequently include predictive models that can either be built within Chordiant or imported from other systems such as SAS or SPSS. Chordiant also supports self-adjusting models that monitor outcomes and modify future recommendations based on the results of different offers.

The appeal of a stand-alone decision engine like Chordiant is that companies can coordinate treatments without using a single vendor for all their touchpoint systems. This makes perfect sense, since in practice most firms do use different products for different touchpoints. In particular, Web interactions are often managed outside of the CRM system.

Yet it’s still been difficult for stand-alone decision engines to survive. Most firms use whatever interaction management features are built into the separate touchpoint engines and coordinate the rules administratively (if at all). Or they rely on interaction management features provided by their marketing automation system.

A few independent decision engine vendors remain, notably thinkAnalytics (another product I’ve been meaning to write about for months) and eGlue (which I wrote about here [update: a week after this post was written, eGlue was apparently purchased by interaction management vendor NICE Systems, although I've yet to see a formal announcement]). But it’s ultimately not surprising that Chordiant should end up as part of Pegasystems, with which Chordiant had already been integrated. The new relationship will let Pegasystems offer added value to its clients and better compete with CRM vendors.

As an aside, it's interesting to compare the position of decision management vendors with execution vendors like Conversen (which I wrote about last month) and ClickSquared (yet another vendor I hope to review shortly). Both sets of products unify a single function that is otherwise spread across multiple systems: offer selection for decision engines and message delivery for execution engines.

The challenges faced by independent decision engines may suggest that the execution engines will face similar problems. But the execution engines sit at the end of the messaging sequence, rather than in its middle: that is, they process outputs from marketing systems and send them elsewhere, rather than feeding them back into the same systems for delivery. This may make it easier for them to survive.