RetailSynth: Synthetic Data Generation for Retail AI Systems Evaluation

• URL: http://arxiv.org/abs/2312.14095v1
• Date: Thu, 21 Dec 2023 18:17:16 GMT
• Title: RetailSynth: Synthetic Data Generation for Retail AI Systems Evaluation
• Authors: Yu Xia, Ali Arian, Sriram Narayanamoorthy, and Joshua Mabry
• Abstract summary: We propose a multi-stage model for simulating customer shopping behavior. We embedded this model into a working simulation environment -- Retail Synth. We analyzed for impact on revenue, category penetration, and customer retention.
• Score: 4.182702249796689
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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