Assume you just landed a great analytical job with MegaTelCo, one of the largest telecommunication firms in the United States. They are having a major problem with customer retention in their wireless business. In the mid-Atlantic region, 20% of cell phone customers leave when their contracts expire, and it is getting increasingly difficult to acquire new customers. Since the cell phone market is now saturated, the huge growth in the wireless market has tapered off. Communications companies are now engaged in battles to attract each other’s customers while retaining their own. Customers switching from one company to another is called churn, and it is expensive all around: one company must spend on incentives to attract a customer while another company loses revenue when the customer departs You have been called in to help understand the problem and to devise a solution. Attracting new customers is much more expensive than retaining existing ones, so a good deal of marketing budget is allocated to prevent churn. Marketing has already designed a special retention offer. Your task is to devise a precise, step-by-step plan for how the data science team should use MegaTelCo’s vast data resources to decide which customers should be offered the special retention deal prior to the expiration of their contracts. Think carefully about what data you might use and how they would be used.
a) What exactly is the business decision you want to support with this solution? (Specifically, what is the business action you are considering? Discuss briefly the timing of the decision and the eventual outcome.)
b) Describe the use phase. Be very clear on how exactly the model/predictions will be used for the decision.
c) Explain precisely why and how you expect doing the predictive modeling will add value.
d) What exactly is the quantity that you inherently do not know and need to predict?
e) Is this a classification, ranking, or probability estimation problem?
f) What are the features? Provide a list of at least 5 features that you think (a) you can get and (b) you think might be useful.
g) What exactly would be your training data (examples)?