SAS® Analytics anchor Virgin Mobile USA low-churn, no-contract plans
SAS® data mining and forecasting reduce churn, marketing expenses
CARY, NC (08. Nov. 2011) – Analyzing customer behavior with SAS boosted profitability for Sprint’s no-contract or prepaid service, Virgin Mobile USA, by reducing churn, improving campaign ROI, and aligning price plans with customer requirements. The brand selected SAS, the leader in business analytics, to better understand and serve customers.
Customers don't need credit checks to purchase no-contract brands – including Virgin Mobile, Boost Mobile and payLo by Virgin Mobile – so the company has fewer background details than for postpaid customers. Instead, Virgin Mobile analyzes usage patterns. "SAS Forecast Server helps us project subscriber needs,’’ said Antoine Georges, the Director of Analytics and Campaign Management for the prepaid business unit of Sprint. “And SAS® Enterprise Miner™ built-in models help us with churn propensity, response modeling and segmentation. SAS has the most flexible tools for tapping into different databases and manipulating big data. No other software delivers the statistical and analytical benefits of SAS.''
Could Sprint improve prepaid customer loyalty? No-contract plans traditionally churn at a higher rate. So, in a new plan, Sprint targeted a different kind of customer, one who can afford a postpaid phone but doesn't want to be locked into a contract. Georges' group helped marketers test assumptions through predictive modeling. Based on analytic insights, Sprint tried offering a variety of handsets with a monthly price plan on Virgin Mobile. Marketers quickly discovered that the higher-end handset attracted subscribers to the plan and kept them there, leading to the creation of a successful new cell phone product: a low-churn monthly prepaid plan.
Increased marketing lift, improved ROI
Other analytic successes are numerous. For example, Virgin Mobile increased marketing lift after developing a response model that predicted which lapsed customers were likely to respond to a win-back offer. "Win-back campaigns are relatively expensive. By predicting which customers were likely to respond, we reduced the cost of the win-back program and improved ROI,'' Georges explained. "Analyzing data with SAS, we have consistently met or exceeded our churn objective for the past three years."