This project is part of an open-access BCG Virtual Experience Program with Forage.

Project Background & Objectives

BCG's client for this project is PowerCo, a major utilities company. PowerCo has had declining profits due to significant customer churn. BCG has been engaged to drive churn reduction within their Small & Medium Enterprise (SME) customers. As a data scientist, my task is to build a predictive model that can identify customers at high risk of churn.

Tasks

1. Business Understanding & Hypothesis Framing

• understand business problem

• formulate the hypothesis as a data science problem

• lay out the major steps needed to test this hypothesis

• communicate your thoughts and findings in an email

  The final email is in the file documents/Business Understanding & Hypothesis Framing.pdf.

2. Exploratory Data Analysis

• perform exploratory data analysis (EDA) on the data

• verify the hypothesis of price sensitivity being to some extent correlated with churn

• provide a summary of findings and suggest next steps

  The EDA is in the file notebooks/eda.ipynb.

3. Feature Engineering & Modelling

• clean and engineer features for prediction

• predict churn probability with decision tree models

• evaluate model performance

  The corresponding notebook is in the file notebooks/feature_engineering_and_modeling.ipynb.

4. Findings & Recommendations

• make an executive summary of findings and provide recommendations

  The final report is in the file documents/executive_summary.pdf.