SimPersona: Learning Discrete Buyer Personas from Raw Clickstreams for Grounded E-commerce Agents

Large language model (LLM)-based web agents can navigate live storefronts, yet often collapse to a single average buyer policy, failing to capture the heterogeneous, distributional nature of real buyer populations. Existing personalization relies on hand-crafted prompt-based personas that are brittle, hard to scale, and unable to faithfully represent population-level behavior. We introduce SimPersona, a framework that learns discrete buyer types from historical traffic and exposes them to LLM agents as compact persona tokens. From raw clickstreams, a behavior-aware vector-quantized variational autoencoder (VQ-VAE) induces a discrete buyer-type space capturing the statistical structure of real buyer behavior and merchant-specific population distributions. Each buyer type maps to a dedicated persona token, and the agent is fine-tuned with these tokens on real browsing traces. At inference, each synthetic buyer is assigned a token with a single encoder forward pass, without any retraining or store-specific prompting, and population-level simulation samples types from each merchant's empirical codebook distribution. Evaluated on 8.37M buyers across 42 held-out live storefronts, SimPersona achieves 78% conversion-rate alignment with real buyers, exhibits interpretable behavioral variation across buyer types, and outperforms larger-parameter baselines on goal-oriented shopping tasks. We release an open-source data pipeline that converts raw e-commerce event logs into buyer representations and agent-training traces.