harshal19t / bcg_forage_tasks

Forage-BCG-Data-Analysis-Program

Worked on BCG Data Analysis Virtual Experience Program by Forage, to gain an insight into the real world scenario. BCG GAMMA is transforming the businesses of today through data science and advanced analytics initiatives. This virtual experience program is designed to give a feel of what it is like to work at BCG GAMMA as they help their clients using data science.

The client for this project is PowerCo, a major utilities company. PowerCo has had declining profits due to significant customer churn. We have been engaged to drive churn reduction within their Small & Medium Enterprise (SME) customers.

Tasks

1: Business Understanding & Problem Framing

How to quickly understand the business context

Background information on 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.

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 Lead Data Scientist (LDS) 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 LDS wants an email with your thoughts on how the team should go about to test this hypothesis.

2: Exploratory Data Analysis & Data Cleaning

Understanding business through data

Background information on 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 LDS wants you to perform some exploratory data analysis and data cleaning.

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

These datasets are otherwise identical and have historical price data and customer data (including churn status for the customers in the training data).

3: Feature Engineering

Uncovering signals with data

The team now has a good understanding of the data and feels confident to use the data to further understand the business problem. The team now needs to 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. Based on the data that is available and was cleaned, identify what you think could be drivers of churn for our client and build those features to later use in your model.

4: Modeling & Evaluation

Modeling the problem and evaluating the model

Background information on task 4

Recall that one of the hypotheses under consideration is that churn is driven by the customers’ price sensitivities and that it would be 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.

Build your models and test them while keeping in mind you would need data to prove/disprove the hypotheses, as well as to test the effect of a 20% discount on customers at high propensity to churn.