RetailSynth: Synthetic Data Generation for Retail AI Systems Evaluation
This provides a tool for applied researchers to validate causal demand models and incorporate realistic price sensitivity into benchmarking suites for personalized retail systems, though it is incremental as it builds on existing simulation approaches.
The paper tackles the challenge of benchmarking causal learning systems in retail AI by proposing RetailSynth, a multi-stage model for simulating customer shopping behavior, which was calibrated on grocery data to generate realistic synthetic transactions and used to analyze pricing policies for metrics like revenue and customer retention.
Significant research effort has been devoted in recent years to developing personalized pricing, promotions, and product recommendation algorithms that can leverage rich customer data to learn and earn. Systematic benchmarking and evaluation of these causal learning systems remains a critical challenge, due to the lack of suitable datasets and simulation environments. In this work, we propose a multi-stage model for simulating customer shopping behavior that captures important sources of heterogeneity, including price sensitivity and past experiences. We embedded this model into a working simulation environment -- RetailSynth. RetailSynth was carefully calibrated on publicly available grocery data to create realistic synthetic shopping transactions. Multiple pricing policies were implemented within the simulator and analyzed for impact on revenue, category penetration, and customer retention. Applied researchers can use RetailSynth to validate causal demand models for multi-category retail and to incorporate realistic price sensitivity into emerging benchmarking suites for personalized pricing, promotions, and product recommendations.