AI RESEARCH
Modelling Customer Trajectories with Reinforcement Learning for Practical Retail Insights
arXiv CS.LG
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ArXi:2605.18449v1 Announce Type: new Understanding customer movement within retail spaces is essential for optimizing layouts. Real-world trajectory data can provide highly accurate insights, but collecting it is costly and often infeasible for many retailers. Heuristics such as Travelling Salesman Problem (TSP) and Probabilistic Nearest Neighbours (PNN) are commonly used as inexpensive approximations, but actual customer trajectories deviate by an average of 28% from shortest paths, highlighting a tradeoff between accuracy and practicality.