What question did this study set out to answer?

The main aim is to develop a machine learning framework for predicting customer churn and its financial implications on Customer Lifetime Value (LTV).

February 28, 2026Open Access

Predicting E-commerce Churn and Boosting LTV

Key Points

The main aim is to develop a machine learning framework for predicting customer churn and its financial implications on Customer Lifetime Value (LTV).
Utilized publicly available transactional datasets for analysis.
Engineered customer-level behavioral features using RFM methodology and engagement metrics.
Evaluated multiple classification models including Logistic Regression, Random Forest, and XGBoost.
Applied survival analysis methods to estimate churn probability and customer lifespan.
Conducted LTV simulation and retention intervention modeling.
Achieved predictive performance for estimating churn probability and customer lifespan.
Quantified the financial impact on Customer Lifetime Value (LTV).
Demonstrated links between churn prediction and revenue protection strategies.

Abstract

This working paper presents a machine learning framework for predicting customer churn in e-commerce and quantifying its financial impact on Customer Lifetime Value (LTV). Using publicly available transactional datasets, customer-level behavioral features were engineered using RFM methodology and extended engagement metrics. Multiple classification models (Logistic Regression, Random Forest, XGBoost) and survival analysis methods were evaluated to predict churn probability and customer lifespan. The study translates predictive performance into measurable business impact through LTV simulation and retention intervention modeling. The objective is to connect predictive modeling directly to revenue protection and long-term margin efficiency. Author: Hanzel LacidaVersion: 1.0Date: February 27, 2026

Predicting E-commerce Churn and Boosting LTV

Key Points

Abstract

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