adityadj98 / bcg-data-science-analytics-intern

Background Problems Task 1

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.

Background Problems Task 2

The BCG project team thinks that building a churn model to understand whether price sensitivity is the largest driver of churn has potential. The client has sent over some data and the AD wants me to perform some exploratory data analysis.

The data that was sent over includes:

Historical customer data: Customer data such as usage, sign up date, forecasted usage etc Historical pricing data: variable and fixed pricing data etc Churn indicator: whether each customer has churned or not

Task

Sub-Task 1: Perform some exploratory data analysis. Look into the data types, data statistics, specific parameters, and variable distributions. This first subtask is for you to gain a holistic understanding of the dataset. You should spend around 1 hour on this.

Sub-Task 2: Verify the hypothesis of price sensitivity being to some extent correlated with churn. It is up to you to define price sensitivity and calculate it. You should spend around 30 minutes on this.

Sub-Task 3: Prepare a half-page summary or slide of key findings and add some suggestions for data augmentation – which other sources of data should the client provide you with and which open source datasets might be useful? You should spend 10-15 minutes on this.

For your final deliverable, please submit your analysis (in the form of a jupyter notebook, code script or PDF) as well as your half-page summary document.

Note: Use the 2 datasets within the additional resources for this task and if you’re unsure on where to start with visualizing data, use the accompanying links. Be sure to also use the data description document to understand what the columns represent. The task description document outlines the higher-level motivation of the project. Finally, use the eda_starter.ipynb file to get started with some helper functions and methods.