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The Large Behavioral Model (LBM) integrates customer decision-making processes with large-scale retail transaction data to enhance predictive accuracy and explainability in behavior modeling. By employing a unified Person-Environment framework, the LBM utilizes behavioral profiles and retrieval-augmented generation to ground its predictions in real-world data. Evaluation results show that LBM significantly outperforms existing language models on various retail tasks and effectively generalizes across different retailers and decision contexts, highlighting the potential of transaction histories in training language models for customer behavior simulation.
Behavioral knowledge from transaction histories can be harnessed to create a powerful digital twin of retail customers, outperforming traditional models in both accuracy and explainability.
Customer behavior modeling underpins recommendation, marketing, and decision support, yet existing approaches either optimize predictive accuracy without explaining decisions or simulate users without grounding them in real behavioral data. We present the Large Behavioral Model (LBM) that learns customer decision making directly from large-scale retail transactions through a unified Person-Environment formulation. Customer state is represented by a behavioral profile derived from historical purchases, while product context is incorporated through retrieval-augmented generation. The model is trained using continued pre-training on verbalized behavioral data, supervised fine-tuning for decision generation, and reinforcement learning with verifiable rewards for evidence-based calibration. We evaluate the proposed framework on purchase prediction, hard-negative discrimination, basket completion, promotion response, and cross-domain voucher redemption. The model consistently outperforms frontier general-purpose language models on in-domain retail tasks while demonstrating strong zero-shot and fine-tuned transfer across retailers and decision domains. Ablation studies show that continued pre-training is the primary driver of behavioral generalization, retrieval is most effective when applied during both training and inference, and reinforcement learning improves reliance on explicit behavioral evidence over generic language-model priors. These results demonstrate that behavioral knowledge encoded in transaction histories can be effectively learned by language models, providing a scalable foundation for customer digital twins and behavior simulation.
