Extracting offline retail shopping patterns: a restricted Boltzmann machines approach to customer segmentation and cross-selling

초록

This study introduces restricted Boltzmann machines (RBM) as a novel approach for customer segmentation and cross-selling optimization in offline retail environments. By extracting interpretable latent shopping patterns from sparse transaction data, RBMs effectively capture nuanced multi-category purchase behaviors by modeling unobservable customer preferences as latent factors. Unlike traditional clustering-based segmentation methods, our RBM approach reveals more meaningful cross-buying patterns that directly inform targeted cross-selling strategies. Our key contributions include: (1) demonstrating RBMs' superior ability to identify actionable segments based on cross-buying patterns compared to benchmark clustering methods, and (2) demonstrating that integrating RBMs into collaborative filtering models significantly improves predictive performance for crossselling recommendations. The results highlight RBMs' dual effectiveness in creating interpretable customer segments while enabling data-driven cross-selling strategies that address the inherent sparsity challenges of offline retail transaction data.

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