Interpretable prediction of private brand purchases by pet type in e-commerce for consumer behavior analysis using real-world transaction data

Background The global pet care market is rapidly expanding, and private brand (PB) products are becoming increasingly important for e-commerce retailers. However, how PB purchasing behavior differs between dog and cat owners remains underexplored. Methods This study analyzed PB purchasing behavior in pet e-commerce using real-world transaction data and machine-learning techniques. We developed separate predictive models for dog and cat owner segments, with extreme gradient boosting (XGBoost) demonstrating superior performance (F1-scores: 0.7806 and 0.7876, respectively). SHapley Additive exPlanations (SHAP)-based interpretability analysis identified key drivers of PB purchasing behavior for each segment. Results Pet supplies and snacks emerged as universal predictors across both segments; however, their relative importance and underlying mechanisms differed significantly. Dog owners showed stronger associations with delivery convenience features, whereas cat owners demonstrated greater sensitivity to price and product quality factors. Implications These findings suggest segment-specific marketing strategies: convenience-focused approaches for dog owners and value-oriented trust-building strategies for cat owners. This work contributes to the limited literature on PB behavior in pet e-commerce and demonstrates the practical applicability of explainable artificial intelligence (XAI) for customer segmentation in digital retail.