Predicting Customer Churn in E-Commerce Using Statistical and Machine Learning Methods

Abstract

This research work aims to develop prediction models and analytical insights to overcome customer churn issues through data-driven approaches. The attrition rate of consumers in e-commerce is a significant issue requiring effective retention strategies. A novel methodology is proposed comprising data preprocessing, using statistical analysis techniques developing the model and carrying out tailored retention strategies. The model is used to identify crucial churn influencers and propose practical recommendations for enhancing consumer retention. The significance of this work lies in its potential to allow e-commerce ventures with insights with the intention of price savings strategies, enhanced revenue measures, and better consumer fulfillment. This research will influence the e-commerce business by facilitating evidence-based methods for reducing customer turnover and increasing long-term customer value. The resulting accuracy of the proposed model using Logistic Regression results in  87 percentage of accuracy which is a good metric to assess overall model performance. The Kaplan-Meier curve is used to check the survival probability of consumers and identify consumers more likely to churn over time.