KXEN Packages Automated Predictive Models within Salesforce Apps


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As I mentioned in my Marketing Automation Beer Goggles post, KXEN introduced a free lead scoring app for Salesforce.com users at Dreamforce. KXEN has since given me a closer look at the lead scoring product, underlying technology, and future plans.

First for details on Predictive Lead Scoring itself. As originally reported, it’s free and requires no configuration to set up. The trade-off for this simplicity is users have no control over which variables are included or what the models predict. The variables will include all standard and custom fields on the lead object, which isn’t too bad except that there might be useful data on related objects such as activity details. At best, marketers could summarize such data and add the summary to the lead object, but that requires human intervention. Predictions are limited to conversions from lead to contact. This isn’t always what you want, but it does stay within the lead object’s contents.

Some of these limits might be relaxed in future versions of the app. However, KXEN is wary of making deployment more difficult or letting users make poor decisions such as removing variables they should keep. The system does provide reports showing the contributions made to the scoring formula by different variables and by values within those variables. Recognizing that this is already more than many users will care to review, KXEN plans to add simpler reports over time.

Although the lead scoring app attracted more interest than KXEN expected, it was really developed to illustrate the power of KXEN’s new “Cloud Prediction” model-building engine. This uses the same automated modeling methods as KXEN’s established on-premise product. What’s new is a REST API that lets external applications send inputs over the Internet, wait while the engine builds a new model, and then receive the completed model formula. Scoring happens within the external application itself – Salesforce.com in this case – allowing the system to update scores as Salesforce data changes without running on KXEN’s own servers. Similarly, the app relies only on data stored within Salesforce.com’s own database, so KXEN doesn’t have to keep a copy.

The limits of the lead scoring app are a design choice: the cloud prediction API allows as much end-user control as KXEN’s on-premise system. But KXEN isn’t planning to expose the full API any time soon. Instead, it’s pursuing the app-based model as a way to expand use of its technology beyond its current base of relatively skilled users.

The next Salesforce.com app KXEN will release – pretty much any day – will tackle prediction of the “next best activity” for a given customer. This is substantially more complicated than the lead scoring app, since it creates a separate predictive model for each activity and then chooses the activity with the highest probability of response in each situation. This one won’t be free: list price is $50 per user per month.

The next best activity app also requires more user effort to set up, since users must define the activities to model and specify eligibility rules for each activity. The system recommends activities randomly at first, to build some experience with different situations. After the initial models are built it will still make occasional random selections to keep the models current. Unlike lead scoring, the next best activity app reads from several Salesforce.com data objects in addition to the lead object. Eventually, users will get control over which data elements to include.

The next best action app also relies on data stored within Salesforce.com. It won’t store a full history of offers made and rejected, because this would take more data than Salesforce.com can economically hold. That means the system won’t know when agents decide not to make the recommended offer. This can be a problem because the models based on a user-selected subset of cases. It’s a common issue with recommendation systems.

Whether these and similar issues cause serious problems for KXEN’s cloud modeling apps remains to be seen. Some amount of skilled human intervention may be essential to apply modeling effectively. But it’s worth exploring what’s really needed: the ever-growing volumes of data and decisions make low-cost, automated predictions increasingly important for marketing success.

Republished with author's permission from original post.


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