Segmenting Customers Based on Purchase Behavior and Estimating Segment-Specific CLV Drivers in E-Commerce
DOI:
https://doi.org/10.54536/ajebi.v5i1.6433Keywords:
Behavioral Segmentation, CLV Drivers, Customer Lifetime Value (CLV), Customer Segmentation, E-commerce, K-Means Clustering, Marketing AnalyticsAbstract
Digital commerce enterprises face dual challenges: effectively categorizing their customer base while simultaneously understanding the diverse factors that influence Customer Lifetime Value (CLV). This research demonstrates an empirical approach to identifying CLV drivers that vary by customer segment, moving beyond uniform management strategies. This investigation analyzes 3,900 e-commerce fashion retail customers through a dual-phase framework. Initial phase applies K-Means clustering to behavioral metrics including Purchase Amount (USD), Previous Purchases, Frequency of Purchases, and Subscription Status, revealing distinct customer groups. Subsequently, proxy CLV calculations enable segment-specific Ordinary Least Squares (OLS) regression analysis to identify unique value drivers such as Review Rating, Discount Applied, and Category preferences within each segment. Four customer segments emerge: “Low-Value Occasional,” “High-Value Loyalists,” “High-Spenders at Risk,” and “New/Lapsed Subscribers.” Regression analysis validates segment-level heterogeneity in CLV drivers. Customer satisfaction significantly influences “High-Value Loyalists” but shows minimal impact on “Low-Value Occasional” segments. Price incentives drive occasional purchasers and new subscribers while demonstrating negligible influence on loyal customers. Results enable targeted marketing strategies customized to each segment’s validated CLV drivers, replacing generalized approaches with precision-focused retention, up-selling, and cross-selling tactics.
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