Mining churning behaviors and developing retention strategies based on a partial least squares (PLS) model

In a very competitive mobile telecommunication business environment, marketing managers need a business intelligence model that allows them to maintain an optimal (at least a near optimal) level of churners very effectively and efficiently while minimizing the costs throughout their marketing programs. As a first step toward optimal churn management program for marketing managers, this paper focuses on building an accurate and concise predictive model for the purpose of churn prediction utilizing a partial least squares (PLS)-based methodology on highly correlated data sets among variables. A preliminary experiment demonstrates that the presented model provides more accurate performance than traditional prediction models and identifies key variables to better understand churning behaviors. Further, a set of simple churn marketing programs—device management, overage management, and complaint management strategies—is presented and discussed.

► Identify the importance of variables by calculating VIP scores with PLS model ► Compare variable selection methods based on PLS model and stepwise regression ► Compare linear and nonlinear PLS models with other classifiers in predicting churners ► Develop three marketing strategies using device, overage, and complaints management ► Integrate three marketing strategies with customer clusters using real data sets