PowerCo is a major gas and electricity utility that supplies to corporate, SME (Small & Medium Enterprise), and residential customers. The power-liberalization of the energy market in Europe has led to significant customer churn, especially in the SME segment. They have partnered with BCG to help diagnose the source of churning SME customers.
A fair hypothesis is that price changes affect customer churn. Therefore, it is helpful to know which customers are more (or less) likely to churn at their current price, for which a good predictive model could be useful.
Moreover, for those customers that are at risk of churning, a discount might incentivize them to stay with our client. The head of the SME division is considering a 20% discount that is considered large enough to dissuade almost anyone from churning (especially those for whom price is the primary concern).
The Associate Director (AD) held an initial team meeting to discuss various hypotheses, including churn due to price sensitivity. After discussion with your team, you have been asked to go deeper on the hypothesis that the churn is driven by the customers’ price sensitivities.
Your AD wants an email with your thoughts on how the team should go about testing this hypothesis.
The client plans to use the predictive model on the 1st working day of every month to indicate to which customers the 20% discount should be offered.
is to understand what is going on with the client and to think about how you would approach this problem and test the specific hypothesis.
You must formulate the hypothesis as a data science problem and lay out the major steps needed to test this hypothesis. Communicate your thoughts and findings in an email to your AD, focusing on the data that you would need from the client and the analytical models you would use to test such a hypothesis.