Consumer Segmentation and Decision: Explainable Machine Learning Insights

The e-commerce market is experiencing a period of rapid growth, with online shopping becoming a prevalent consumer behavior. However, the challenge of identifying and segmenting high-value customer groups, and subsequently enhancing the conversion rate of purchases made on the shop's platform, has remained a significant obstacle for merchants engaged in e-commerce. This study presents a novel approach that combines consumer involvement theory, quality signaling theory and consumer heterogeneity theory to develop a multi-algorithmic, interpretable machine learning model based on shapely additive explanations (SHAP). The results revealed the existence of four distinct consumer groups: the comprehensively involved, interactive, reading-oriented, and low-involvement groups. Comprehensively involved and interactive consumers have the highest purchase conversion rate and should be given priority attention. This study addresses the limitations of single theoretical perspective and "black box" problem of machine learning models for decision-making behavior, and can bring management insights for merchants to improve shop operation.