RetailSynth: Synthetic Data Generation for Retail AI Systems Evaluation
Yu Xia Ali Arian Sriram Narayanamoorthy Joshua Mabry
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.