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CRM + Predictive Analytics: Why It All Adds Up

May 2, 2011 No Comments

SOURCE: Destination CRM

Though predictive analytics (PA) tools have been around for decades—with a strong uptake historically in telecommunications and banking—demand has risen dramatically in the past couple of years. In fact, any CRM vendor focusing on a business-to-customer-facing client base must incorporate some PA into its offerings in order to build staying power.

The biggest factor driving demand for PA in recent years is return on investment (ROI). Since the onset of the recession—which may or may not be over, depending on your industry and location—many businesses have squeezed more value out of every dollar spent. With layoffs, furloughs, and closings, every aspect of an enterprise’s budget has come under scrutiny, and CRM is no exception. Consequently, in those kinds of evaluations, PA’s promises of targeted and optimized customer outreach have been attractive.

Traditionally much of the ROI derived from PA involves “maximizing the lifetime value of a customer,” which in many cases refers to customer retention. That means intervening with a next-best offer when a customer appears likely to turn away from a provider or making the right offer once the customer has announced his intention to break ties. In some cases, it’s vital to determine when a customer seems likely to leave before he says anything.

“If people actually tell you they’re going to leave, it’s much more difficult to retain them,” says Rob Walker, vice president of decision management and analytics for Pegasystems, a business process management (BPM) and CRM solutions provider.

CALCULATING LIKELY DEFECTIONS

To make that determination, PA solutions leverage weighted algorithms and models, sometimes in the hundreds, simultaneously. For example, a telecom company would employ PA to figure out when customers are likely to leave, incorporating a number of factors.

Among them are trigger dates, such as the expiration of a contract, as well as call logs and wireless browsing history. PA would notice a customer who has called the service line of a competitor or looked on its Web site. Also important is usage: If a customer already has signed up with a competitor, maybe he’s just using the time left on his previous carrier’s phone. Factors such as those get processed through a PA tool to assess how likely a customer is to leave. If the returned value indicates that flight is likely, an intervention can be made.

Exactly what should be done is another determination made through PA. The expected future value of a customer’s business is weighed against how much a given offer for retention would cost, and that is stacked against a customer’s likelihood to accept it. Of course, other considerations are analyzed. A PA model may take Bureau of Labor and Statistics information, for instance. If a customer lives in an area of high unemployment and falls within certain demographic lines, he might not be able to afford his current plan. If he’s been laid off, a cheaper plan should be offered. A plethora of determinations like these must be made.

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APPLICATION INTEGRATION, SOCIAL BUSINESS

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