Appendix A presented a set of guidelines and questions useful for evaluating data science proposals. Chapter 13 contained a sample proposal (Example Data Mining Proposal) for a “customer migration” campaign and a critique of its weaknesses (Flaws in the Big Red Proposal).
We’ve used the telecommunications churn problem as a running example throughout this book. Here we present a second sample proposal and critique, this one based on the churn problem.
You’ve landed a great job with Green Giant Consulting (GGC), managing an analytical team that is just building up its data science skill set. GGC is proposing a data science project with TelCo, the nation’s second-largest provider of wireless communication services, to help address their problem of customer churn. Your team of analysts has produced the following proposal, and you are reviewing it prior to presenting the proposed plan to TelCo. Do you find any flaws with the plan? Do you have any suggestions for how to improve it?
Churn Reduction via Targeted Incentives — A GGC Proposal
We propose that TelCo test its ability to control its customer churn via an analysis of churn prediction. The key idea is that TelCo can use data on customer behavior to predict when customers will leave, and then can target these customers with special incentives to remain with TelCo. We propose the following modeling problem, which can be carried out using data already in TelCo’s possession.
We will ...