Data-Driven Pricing Strategy in E-Commerce: Predictive Modeling Using Consumer Purchase Behavior

Adaptive, data-driven pricing is a necessity for online merchants experiencing volatile demand and intense competition. This research suggests an integrated predictive model that uses historical transaction logs, click-stream traces, and exogenous market indicators to infer best price points at SKU, segment, and session levels. The pipeline in this model begins with Recency-Frequency-Monetary (RFM) features and K- Means/Hierarchical clustering to obtain behaviorally meaningful customer segments; then elastic-net regression predicts short-term price-elasticity for each segment; lastly, a reinforcement-learning layer adjusts prices in near-real-time to maximize expected contribution margin subject to inventory and competitor-price constraints. We test the framework on 2.8 million orders for a mid- size fashion e-retailer during January 2023 – December 2024. Our system compares favorably with the company’s rule-based approach. It increases gross profit by 7.9 %, conversion among high-lifetime-value customers by 5.4 %, and reduces markdown expenditure by 11.2 %. Robustness checks under severe demand shocks—e.g., flash sales and influencer-driven traffic spikes— verify steady performance. The contribution is two-fold: (i) methodological—integrating segmentation, econometric elasticity, and machine-learning control within one loop; (ii) managerial— showing how granular behavioral data can translate to defensible margin gains with customer goodwill intact. Ethical and regulatory aspects of personalized prices are also examined