Data-driven and economic insights into complementary and substitutive purchase behaviors in consumer segments

Purpose This paper proposes a data-driven framework for segmenting retail customers using machine learning, identifying complementary and substitutive product relationships within segments via cross-elasticity of demand, and applying main path analysis to reveal key bundling and substitution patterns. Design/methodology/approach Machine learning is used for customer segmentation; cross-elasticity of demand (XED) identifies product bundling and substitution patterns within segments; a weighted product network is constructed; and main path analysis (MPA) determines key product chains. Findings Applied to a large retail transaction dataset, the framework calculates XEDs, defines arc weights by relationship likelihood and constructs a product network; MPA then identifies the key product bundles and substitution patterns most frequently occurring in customer purchases. Practical implications The framework equips retailers to optimize product offerings and marketing strategies by identifying key product relationships, thereby enhancing targeted promotions, improving inventory management and boosting sales and customer satisfaction. Originality/value The paper integrates machine learning, XED and MPA to model product relationships as a network problem, demonstrating how real-world transaction data yields new insights into bundling and substitution patterns that support retail marketing, pricing and inventory decisions.