Reinforcement learning models customer retail journeys for layout optimization

Researchers have developed a new reinforcement learning (RL) framework to model customer movement in retail environments, aiming to provide practical insights for store layout optimization. This approach treats customer trajectory prediction as a maximum entropy RL problem, balancing reward with stochasticity to account for bounded rationality. Experiments using real-world convenience store data show that RL-generated trajectories are more accurate than traditional methods like TSP and PNN, leading to better estimates of impulse purchases and shelf traffic. The RL method also enables more effective product repositioning strategies that align with actual customer behavior, making advanced layout optimization more accessible. AI

IMPACT Provides a more accessible and behaviorally grounded method for retailers to optimize store layouts and predict customer purchasing behavior.

RANK_REASON The cluster contains an academic paper detailing a new methodology for modeling customer behavior using reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• Customer Trajectories
• Probabilistic Nearest Neighbours
• Reinforcement Learning
• Retail

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