PowerCo Customer Churn Analysis – BCG X Data Science
Completed the BCG X Data Science Virtual Experience project focused on customer churn analysis for PowerCo. Performed exploratory data analysis, data processing, feature engineering, and predictive modeling using Python. Developed a Random Forest classification model to identify key factors associated with customer churn and analyzed feature importance and model performance to derive data-driven business insights.
Overview
Completed an end-to-end Data Science project as part of the BCG X Data Science Virtual Experience, focusing on customer churn analysis for PowerCo, an energy company. The project aimed to identify the key factors influencing customer churn and develop predictive insights to support customer retention strategies. Performed exploratory data analysis (EDA), data cleaning, preprocessing, and feature engineering to understand customer behavior and uncover patterns associated with churn. Applied statistical analysis to
Problem
PowerCo, an energy company, was experiencing customer churn and needed to understand the factors influencing customer decisions to switch providers. The challenge was to identify customers at risk of leaving and uncover the key drivers behind churn to support better customer retention strategies
Solution
Developed a Machine Learning-based customer churn analysis solution using Python. Applied exploratory data analysis, feature engineering, and a Random Forest classification model to identify important churn patterns and understand customer behavior. Translated the findings into actionable business insights to support customer retention strategies
Features
- 01Customer churn pattern analysis
- 02Exploratory Data Analysis (EDA)
- 03Data cleaning and preprocessing
- 04Feature engineering and selection
- 05Random Forest classification model
- 06Feature importance analysis
- 07Model performance evaluation
- 08Data-driven customer retention insights
Technologies
Development details
Completed an end-to-end Data Science project as part of the BCG X Data Science Virtual Experience. Started by exploring PowerCo's customer and historical data to understand customer behavior and identify potential churn patterns. Performed data cleaning, preprocessing, exploratory data analysis, and feature engineering to prepare the dataset for predictive modeling. Developed a Random Forest classification model to identify customers likely to churn and evaluated its performance using relevant classification metrics. Analyzed feature importance to understand the factors associated with customer churn and translated the technical findings into meaningful business insights. The analysis helped identify opportunities for improving customer retention and supporting data-driven business decisions.
Key learnings
- Understanding real-world customer churn problems
- Performing exploratory data analysis (EDA)
- Data cleaning, preprocessing, and feature engineering
- Building Random Forest classification models
- Evaluating machine learning model performance
- Interpreting feature importance and identifying churn drivers
- Translating machine learning results into business insights
- Understanding how predictive analytics supports customer retention strategies