Q1. Is customer churn at Cell2Cell predictable from the customer information that Cell2Cell maintains? Can you develop a predictive model and demonstrate that the model is able to distinguish among customers who are likely or unlikely to churn? Here you can apply what you learned in the predictive modeling lecture. In the data, you observe if customers cancel their account (churn = 1) or not (churn = 0). Try to estimate a statistical model that predicts this churn outcome using the customer variables in the data set. Provide a validation analysis to check if the model is able to distinguish among customers who are more or less likely to churn. Remember the steps for predictive modeling and the customer economics that we discussed in the predictive modeling lecture.
Q2. What factors (variables) in particular influence customer churn? Which factors are particularly important based on their quantitative impact on churn? Note: Remember Step 3 in the churn management discussion.
Q3. What incentives should Cell2Cell offer to prevent customer churn? Base your proposals on the analysis showing which variables have the biggest impact on churn in question 2 above.
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