Application of Regression Analysis in Pricing of Fast-Moving Consumer Products

Abstract

Against the backdrop of fiercer competition and diversified demands in the fast-moving consumer goods (FMCG) market, scientific new-product pricing is critical for enterprises to capture market share and maximize profits, yet most still rely on experience-based methods. Based on five years of relevant literature, this paper explores regression analysis’s application in FMCG pricing: it clarifies the variable selection logic of pricing regression models, compares the fitting effects and applicable scenarios of mainstream and specialized regression methods, identifies practical implementation obstacles, and discusses the potential of integrating machine learning with regression analysis to capture product substitution effects and optimize profit-maximizing price combinations, providing targeted guidance for FMCG pricing research and practice.