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 us to help diagnose the source of churning SME customers.
One of the hypotheses under consideration is that churn is driven by the customers’ price sensitivities and that it is possible to predict customers likely to churn using a predictive model. The client also wants to try a discounting strategy, with the head of the SME division suggesting that offering customers at high propensity to churn a 20% discount might be effective.
The project is divided into five parts.
- Business Understanding: Understanding what are the factors affecting the customer churn.
- Exploratory Data Analysis: Perform some exploratory data analysis. Look into the data types, data statistics, and identify any missing data or null values, and how often they appear in the data. Visualize specific parameters as well as variable distributions.
- Feature Engineering: Brainstorm and build out features to uncover signals in the data that could inform the churn model.Feature engineering is one of the keys to unlocking predictive insight through mathematical modeling.
- Model Building: Build churn model(s) to try to predict the churn probability of any customer.
- Summary: Develop an executive summary slide synthesizing all the findings from the project so far.