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 want to 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).

An initial team meeting was held to discuss various hypotheses, including churn due to price sensitivity. Deeper exploration is required on the hypothesis that the churn is driven by the customers’ price sensitivities.

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.

We would need to fomulate the hypothesis as a data science problem and lay out the major steps needed to test this hypothesis. Communicate your thoughts and findings to the client, focusing on the data that we would need from the client and the analytical models you would use to test such a hypothesis.