Customer Churn Prediction and Retention Strategy

Project Overview

The goal of this project is to develop a predictive model to identify customers at risk of churning and devise targeted retention strategies to improve customer retention rates. This involves data collection, preprocessing, exploratory data analysis, model building, and strategy formulation.

Key Steps

Data Collection

Gather historical customer data from the company's CRM, including demographics, purchase history, customer service interactions, and subscription details.

Data Preprocessing

Clean the data by handling missing values, removing duplicates, and correcting inconsistencies.

Normalize and standardize the data to prepare it for analysis.

Exploratory Data Analysis (EDA)

Conduct EDA to understand key patterns and trends.

Visualize churn rates across different customer segments using tools like Tableau or Power BI.

Identify potential features that could be predictors of churn.

Feature Engineering

Create new features from existing data that might enhance the model's predictive power.

Perform feature selection to identify the most important variables.

Model Building

Split the data into training and testing sets.

Build and compare various machine learning models (e.g., logistic regression, decision trees, random forests, and gradient boosting) to predict churn.

Evaluate model performance using metrics such as accuracy, precision, recall, and AUC-ROC.

Model Interpretation

Use SHAP values or other interpretability tools to understand the impact of each feature on the prediction.

Identify the top factors contributing to customer churn.

Strategy Formulation

Based on the model's insights, propose targeted retention strategies, such as personalized offers, loyalty programs, or improved customer support.

Simulate the potential impact of these strategies on retention rates.

Reporting and Visualization

Create a comprehensive report detailing the methodology, analysis, and findings.

Develop interactive dashboards to present the results and recommendations to stakeholders.

Implementation Plan

Outline a plan for implementing the retention strategies.

Suggest ways to integrate the predictive model into the company's existing systems for ongoing churn monitoring.

Outcome

This project will showcase your ability to handle end-to-end data analysis, from data collection and preprocessing to advanced modeling and strategic recommendations. It will demonstrate your technical skills in machine learning, data visualization, and business acumen in developing actionable insights.