# RetailSynth: Synthetic Data Generation for Retail AI Systems Evaluation

arXiv: https://arxiv.org/abs/2312.14095 · HTML: https://mlsanity.com/paper/2312.14095/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 41 references that are in the map.
- First posted on arXiv 21 Dec 2023.

## Builds on (most cited first, 16 of 16)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [RecSim: A Configurable Simulation Platform for Recommender Systems](https://mlsanity.com/paper/1909.04847/) (2019)
- [DataPerf: Benchmarks for Data-Centric AI Development](https://mlsanity.com/paper/2207.10062/) (2022)
- [Synthetic Data -- what, why and how?](https://mlsanity.com/paper/2205.03257/) (2022)
- [Horizon: Facebook's Open Source Applied Reinforcement Learning Platform](https://mlsanity.com/paper/1811.00260/) (2018)
- [Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters](https://mlsanity.com/paper/2104.04103/) (2021)
- [Validating Causal Inference Methods](https://mlsanity.com/paper/2202.04208/) (2022)
- [SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements](https://mlsanity.com/paper/1711.03560/) (2017)
- [MARS-Gym: A Gym framework to model, train, and evaluate Recommender Systems for Marketplaces](https://mlsanity.com/paper/2010.07035/) (2020)
- [Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index](https://mlsanity.com/paper/1603.09326/) (2016)
- [Foundations of Bayesian Learning from Synthetic Data](https://mlsanity.com/paper/2011.08299/) (2020)
- [TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow](https://mlsanity.com/paper/1709.02878/) (2017)
- [Toward a Taxonomy of Trust for Probabilistic Machine Learning](https://mlsanity.com/paper/2112.03270/) (2021)
- [Counterfactual Inference for Consumer Choice Across Many Product Categories](https://mlsanity.com/paper/1906.02635/) (2019)
- [Generating Realistic Sequences of Customer-level Transactions for Retail Datasets](https://mlsanity.com/paper/1901.05577/) (2019)
- [Multi-Purchase Behavior: Modeling, Estimation and Optimization](https://mlsanity.com/paper/2006.08055/) (2020)

## Built on by (most cited first, 5 of 5)

- [Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Data](https://mlsanity.com/paper/2406.13130/) (2024)
- [RetailBench: Benchmarking long horizon reasoning and coherent decision making of LLM agents in realistic retail environments](https://mlsanity.com/paper/2606.15862/) (2026)
- [Simulation-Based Benchmarking of Reinforcement Learning Agents for Personalized Retail Promotions](https://mlsanity.com/paper/2405.10469/) (2024)
- [RetailBench: Evaluating Long-Horizon Autonomous Decision-Making and Strategy Stability of LLM Agents in Realistic Retail Environments](https://mlsanity.com/paper/2603.16453/) (2026)
- [PrefBench: Evaluating Zero-Shot LLM Agents in Hidden-Preference Personalized Pricing Negotiations](https://mlsanity.com/paper/2605.22855/) (2026)

## Abstract (arXiv)

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.