Price Elasticity modelling across Customer Segments in Competitive e-commerce markets

Abstract

In the rapidly evolving landscape of e-commerce, pricing strategies are central to consumer engagement and market competitiveness. This paper examines the doctrinal foundations and contemporary applications of price elasticity modelling across differentiated customer segments in digital marketplaces. By exploring classical economic theories and integrating them with modern consumer behavior analytics, the study highlights how firms assess and respond to varying degrees of price sensitivity among users. Customer segmentation—based on demographics, psychographics, behavior, and technographics—plays a pivotal role in determining elasticity and optimizing pricing decisions. Advanced methodologies such as regression analysis, conjoint analysis, and machine learning are utilized to assign segment-specific elasticity scores, enabling dynamic and personalized pricing strategies. However, these innovations raise important legal and ethical concerns, particularly around price discrimination, data protection, and algorithmic fairness. The paper underscores the importance of aligning commercial practices with legal standards, such as consumer protection laws and antitrust regulations, to maintain fairness in pricing mechanisms. Through a doctrinal lens, the study presents a structured analysis of elasticity as both an economic and regulatory construct, advocating for balanced, transparent, and lawful application of data-driven pricing in competitive e-commerce environments.