Predictive analytics can also reveal hidden truths about customers—truths that can influence business processes. Last year XO Communications, a telecommunications company based in Herndon, Virginia, struggled with why so many of its voice-over-IP subscribers were terminating their contracts within the first year, recalls Cris Payne, senior manager of customer intelligence.
Using analytics software from IBM’s SPSS business unit, the company made a surprising discovery: many of its customers were experiencing billing errors that were prompting them to abandon the carrier as soon as their contracts expired. The model showed that customers were more likely to jump ship if they had received a mistaken account suspension notice. After the error was cleared up, they’d angrily wait out the remainder of their contract and then fail to renew it. Despite complaints to XO call centers about billing errors, the company simply never would have made the connection without predictive analytics, says Payne.
To rectify the situation, XO Communications introduced a “first bill” review, in which a customer support representative goes over the details of its billing practices and procedures with each new customer. According to Payne, the result was a 60 percent reduction in “customer churn,” which more than paid for the $750,000 investment in the new software within the first year of deployment.
“If we didn’t have predictive analytics, we’d probably still be struggling to outsell our churn,” says Payne. “It really has plugged a hole and allowed us to grow.”
Stories like this may actually help explain why so few companies take advantage of predictive analytics, though. Eric King, president of the Modeling Agency, a predictive modeling consultancy, says that not all companies are interested in an honest assessment of how their normal business processes are working. He refers to it as the Jack Nicholson effect—You can’t handle the truth!—and warns that “if we can’t carry out the advice that a predictive model provides, the project will fizzle.”
Predictive models also don’t help much if companies don’t tweak them to reflect changes in market or customer base. Cabela’s recently altered one of its models, reclassifying its potential catalogue recipients as “households” rather than “individuals.” In the past, the company delivered catalogues to only those customers who made more than a minimum purchase each year—a strategy that often alienated other household members who were loyal customers but fell short of the threshold. By combining customers into household rankings, Bergstrom says, the new model has increased response rates by 20 percent.
All this provides good reason to carefully guard confidential customer data: the competitive edge it gives companies like XO and Cabela’s. “Ultimately every organization is going to have to make predictive analytics a part of their standard business intelligence practice,” says King. “It’s going to be too expensive not to.”