Predictive Analytics for Customer Retention: Machine Learning Models to Analyze and Mitigate Churn in E-Commerce Platforms

Abstract

The competitive e-commerce business environment in the USA now identifies customer retention as the critical factor in deciding long-term business achievement. Research shows that an organization reaps more benefits by retaining existing customers rather than spending money on customer acquisition. The main purpose of this research project was to develop highly precise machine learning algorithms that detect customers prone to leaving the company using multiple behavioral patterns combined with transaction histories and demographics. The dataset assembled for this analysis included a broad range of characteristics that reflect both static and dynamic facets of customer behavior in the online store. User attributes like age, gender, location, and account signup date give essential context regarding the profile of the customers. Adding depth to this are rich purchase behavior measures, such as frequency of purchase, basket size, overall spending, accepted methods of payment, and usage patterns for discounts. Order history is carefully documented, including the quantity of completed, canceled, and returned orders, and the time since the last orders. Top-level product category preferences are also monitored to discern preferences for types of merchandise (e.g., electronics, clothing, home, and garden), providing greater insight into changing interests. We used three very different models to best tackle the issues of churn prediction for customers. To ascertain the strength of our models, we adopted a systematic strategy for training and testing the models. XG-Boost generally has the best performance overall with the highest scores for all four measures, always above 0.9. Random Forest is second with scores slightly less than for XG-Boost but generally high (above 0.85). Implementing a machine learning-based churn alert system is a major advancement toward enabling customer retention tactics within e-commerce platforms. A churn alert system actively tracks user behavior and activity levels, using predictive algorithms to allocate the risk of churn within near real-time. Predictive analytics for churn is a key factor in safeguarding and forecasting revenue streams for Internet businesses, where even minor fluctuations in customer retention can have disproportionate effects on profitability. To provide richer, more practical insights from churn models, research and development must focus largely in the coming period on the incorporation of richer, more detailed data sources.