Customer Churn Prediction Model for E-commerce Company

In today's competitive market, E-commerce companies are grappling with the challenge of retaining existing customers amidst fierce competition. Account churn poses a significant threat, with one account potentially representing multiple customers. To address this, the company aims to develop a churn prediction model to identify potential churners and offer targeted incentives to mitigate churn effectively. As an analyst, the task is to develop a robust churn prediction model and provide strategic business recommendations for customer retention campaigns.
Objectives of the Research:
Primary Objective: Develop a churn prediction model to identify accounts at risk of churn.
Significance of the Project: Enhance customer retention strategies and minimize revenue loss due to churn.
Final Objective: Contribute to the field of customer relationship management by providing actionable insights for reducing churn rates and increasing customer loyalty.
Research Methodology
Type of Research: Empirical
Data Collection Method: Secondary data provided by the E-commerce company.
Data Analysis Techniques:
Exploratory Data Analysis (EDA)
Feature Engineering
Machine Learning Modeling (e.g., Logistic Regression, Random Forest)
Model Evaluation Metrics (e.g., AUC, F1 Score)