Modelling Customer Trajectories with Reinforcement Learning for Practical Retail Insights

2026-05-18 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Retail Technology & Operations · Depth: Expert, quick

Summary

A new agent-based modeling framework utilizes maximum entropy reinforcement learning (RL) to predict customer trajectories within retail spaces, addressing the high cost and impracticality of collecting real-world data. This RL approach balances reward maximization with stochasticity to better reflect bounded rationality in customer behavior. Compared to traditional heuristics like Travelling Salesman Problem (TSP) and Probabilistic Nearest Neighbours (PNN), which deviate by an average of 28% from actual paths, the RL-generated trajectories align more closely with real customer movements. This leads to more accurate estimates of impulse purchase rates and shelf traffic densities. The RL model also yields product repositioning decisions that match those derived from actual data, resulting in comparable estimated profit gains for retailers.

Key takeaway

For AI Product Managers evaluating retail analytics solutions, consider integrating reinforcement learning models for customer trajectory prediction. This approach offers a practical, behaviorally grounded alternative to traditional heuristics, providing more accurate insights for store layout optimization and potentially leading to comparable profit gains from impulse product repositioning. Your teams can leverage the publicly available source code to explore implementation.

Key insights

Reinforcement learning accurately models customer trajectories, outperforming heuristics for retail layout optimization.

Principles

• Customer trajectories deviate significantly from shortest paths.
• Bounded rationality introduces stochasticity in customer movement.

Method

The framework casts customer trajectory prediction as a maximum entropy reinforcement learning problem, balancing reward maximization with stochasticity to simulate realistic customer behavior.

In practice

• Estimate impulse purchase rates more accurately.
• Optimize shelf traffic densities effectively.
• Inform product repositioning decisions.

Topics

• Reinforcement Learning
• Customer Trajectories
• Retail Optimization
• Agent-based Modelling
• Impulse Purchases

Best for: Research Scientist, AI Product Manager, Product Manager, AI Scientist, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.