arXiv:2607.06993v1 Announce Type: new Abstract: 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
The proliferation of digital transaction data and advances in AI, particularly large language models, now enable a more holistic and explanatory approach to customer behavior modeling.
This development allows for a deeper, more transparent understanding of customer decisions, moving beyond mere prediction to actionable insights for personalization, marketing, and strategic business planning.
Retailers and other consumer-facing businesses can transition from opaque predictive models to promptable, interpretable digital twins, fundamentally altering how they interact with and understand their customer base.
- · e-commerce platforms
- · marketing technology companies
- · retailers with large transaction datasets
- · AI/ML developers
- · traditional market research firms
- · companies reliant on black-box predictive models
- · consumers susceptible to highly targeted manipulation
Businesses gain unprecedented capabilities to simulate and respond to individual customer decision-making.
This deep understanding could lead to hyper-personalized offerings and marketing, potentially blurring ethical lines regarding consumer autonomy.
The technology might enable sophisticated 'nudging' at scale, potentially reshaping consumer choice architecture and market competition.
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Read at arXiv cs.AI