PAARS : Persona Aligned Agentic Retail Shoppers (2025)

Abstract: In e-commerce, behavioral data is collected for decision making which can be costly and slow. Simulation with LLM powered agents is emerging as a promising alternative for representing human population behavior. However, LLMs are known to exhibit certain biases, such as brand bias, review rating bias and limited representation of certain groups in the population, hence they need to be carefully benchmarked and aligned to user behavior. Ultimately, our goal is to synthesise an agent population and verify that it collectively approximates a real sample of humans. To this end, we propose a framework that: (i) creates synthetic shopping agents by automatically mining personas from anonymised historical shopping data, (ii) equips agents with retail-specific tools to synthesise shopping sessions and (iii) introduces a novel alignment suite measuring distributional differences between humans and shopping agents at the group (i.e. population) level rather than the traditional “individual” level. Experimental results demonstrate that using personas improves performance on the alignment suite, though a gap remains to human behaviour. We showcase an initial application of our framework for automated agentic A/B testing and compare the findings to human results. Finally, we discuss applications, limitations and challenges setting the stage for impactful future work.

## introduction agents, which are llms instructed with a persona (aka role) and have access to tools, are emerging as a powerful paradigm for simulation, surveying and data augmentation. for example, in the domain of election surveying, @xcite find that gpt3 can emulate a wide variety (they coin the term "algorithmic fidelity") of political human traits with high correlation to human judgement, thus concluding that llms encapsulate and can simulate many distributions (when prompted appropriately) rather than a single generic one. @xcite survey the landscape of llm based agents for simulation, including use cases in the physical domain (e.g. urban environments, ecological and supply chain dynamics) and social or hybrid domain (marketing, economy, opinion, political and market dynamics). previous work has primarily focused on using agents to improve performance through collaboration (e.g. @xcite ) or for social psychology simulations. however, there has been limited exploration of real-world applications that can benefit from agent-based simulation, particularly in the retail domain. llms are known to exhibit certain biases that need careful consideration, such as brand bias @xcite , positive rating bias @xcite , and limited representation of certain groups in the population. these biases can impact the reliability of agent-based simulations and need to be carefully benchmarked and aligned to user behavior. in this work, we propose paars, an llmpowered agentic framework specifically designed for simulating human shoppers. an overview of the framework is shown in figure 1 . in contrast to previous studies that rely purely on historical behavioral data, we introduce a persona-driven approach where we induce personas from real and anonymized shopping sessions using them to power llm agents equipped with retail-specific tools (e.g., search, view , purchase, etc). the use of induced personas offers several key advantages: 1) it allows measuring algorithmic fidelity across different customer segments, 2) it enables fine-grained population sub-groups experimentation, including potentially underrepresented ones, 3) it mitigates privacy concerns as the personas are synthetic. to ensure that our agents behave in a manner indistinguishable from human shoppers, we introduce a behavioral alignment suite. as we care about overall trends for applications such as a/b testing, we focus on distributional differences between human and agentic shoppers behavior at the group (population sample) level. for completeness, we also include traditional "individual" alignment tasks. prior work in llm-based recommendation and behavioral modeling has primarily focused on individual alignment -measuring how well an agent replicates a specific user's past behavior (e.g., next-purchase prediction). in contrast, our group alignment methodology evaluates whether aggregated shopping sessions of a population of agentic shoppers captures the same distributional properties as human shoppers. this distinction is critical for downstream applications: for example, surveying or a/b testing tools require group-level similarity but do not need each synthetic agent to mimic real individuals accurately. to summarize the contributions of our work: ensuring llm-powered agents behavior aligns to humans is critical for simulation applications. to this end, prediction tasks are used in the literature to expose potential biases of llms such as brand bias @xcite or (positive) rating bias @xcite . previous work for alignment focuses on emulating user preferences across a diverse set of users. the users are not distinguished by personal traits or preferences. our proposed framework induces personas from historical behavior data. interestingly, obtaining unique personas for different users allows us to measure alignment in two ways: 1) individual alignment -how well an agent replicates the actions of a specific user 2) group alignment -whether a population of simulated agents exhibits similar distribution to human shoppers. closely related to our work is @xcite , who propose a behavioral alignment suite for llmbased movie recommendation tasks, including item selection, reviewing, and search query generatio. in the retail domain, @xcite leverage llms for product recommendation, achieving comparable performance to fine-tuned models but relying on historical behavior data only. both works do not explore persona driven modeling. additionally, given access to historical a/b tests and human sessions, we can directly correlate the tendencies of agentic a/b tests with the human ones. overall, our work differentiates itself by integrating persona induction from real shopping data, enabling the creation of agents that simulate shopping sessions reflective of both individual and group behaviors. this methodology not only improves the alignment suite metrics, but also provides a framework for persona powered simulation enabling future impactful applications such as agent based a/b testing and surveying. ## persona mining the core of paars framework lies in the persona mining methodology, which consists of two subsequent prompting steps. 1. consumer profile: the llm is provided with a shopping history comprising search, view, and purchase actions from a real customer and is tasked with synthesizing a set of generic user information, such as age range, marital status and income. the shopping history is composed by daily sessions for the last 6 months concatenated with the purchase history older than 6 months. the complete persona is eventually composed by: a consumer profile, shopping preferences, and the real shopping history itself. additionally, the reasoning produced by chain-of-thought prompting is also included in the persona. the mined personas will represent the role the llm needs to impersonate while generating responses across the various tasks we define. an illustrative shopping session and induced persona are shown in appendix a. ## experimental results in the following sections we highlight the experimental results obtained on the previously introduced alignment suite, measuring both individual and group alignment. while the proposed framework is agnostic to a specific model choice, we use anthropic claude sonnet 3.0 as the llm backbone and refer to it as base. unless otherwise stated, we default to a @xmath0. all prompts used for persona generation and alignment are provided in appendix c. ## -2 a/b testing simulation to showcase the potential of paars in complex applications, we conducted an initial limited effort to simulate three real a/b tests. we plan to scale a/b testing simulation in the future to ensure reliability and significance. we used the same setup from the session generation task to simulate historical a/b tests carried out on the retail website. we target specific a/b tests whose main effects can be simulated in a textual environment, for example change of result ranking algorithms. we set up two environments, one for control (c) and one for treatment (t), where the two differ solely based on their search result and product content such that these match what was viewed by customers during c and t respectively. for a/b metrics, we track the sales change between the c and t, correlating results from agents with these from real customers. overall, we measured a directional agreement in sales change between historical and simulated test for 2 a/b tests out of 3 . on the other hand, we note that the magnitude of sales change is bigger for the simulated environments (10-30x). we hypothesize that the magnitude increase is a result of the way we define shopping intentions for sessions, where the agents are biased towards purchasing items. intentions improvements, in addition to scaling the number of personas and a/b tests are left for future work. ## discussion our persona aligned agentic retail shoppers (paars) framework offers a robust and scalable approach to modeling and analyzing human shopping behavior through synthetic persona driven agents. paars opens up several promising applications while also highlighting important limitations and ethical considerations that need to be addressed. a general advantage of persona induction is the enhanced explainability of the system. traditional retail modeling often relies on historical behavioral data without explicit persona modeling, making it difficult to understand why certain purchasing patterns occur. associating historical behaviour and purchase data with an induced persona allows debugging of simulation and prediction results. one of the most impactful applications of paars is simulated a/b testing, offering an offline (i.e. automated, not involving humans) testing environment before deployment. this capability is valuable in accelerating the experimentation cycles and increasing the likelihood of success of human based a/b testing. in addition, paars facilitates agent-based surveying, where synthetic shoppers can respond to market research queries. user experience research is fundamental to retail feature launches and relies heavily on slow and costly human surveys. in some cases it is even challenging to perform offline testing for ux features, e.g. to introduce a feature of reviews summarization one can measure the quality of the summaries but we can not predict whether humans would prefer to see the feature without resorting to surveys (or human a/b testing). simulating large-scale consumer analysis without the logistical constraints of tra-ditional human surveys can serve as an effective guardrail to feature launches. another key application of paars lies in targeting underrepresented consumer groups. traditional behavioral data is often skewed toward majority demographics, leaving gaps in understanding the shopping experiences of marginalized or less-studied communities. by generating synthetic personas that model diverse backgrounds and purchasing behaviors, paars enables businesses to proactively design more inclusive shopping experiences. this ability to simulate different market segments is particularly useful for new feature and product launches, where companies need to predict how various consumer groups will interact with new offerings. although our experimentation is limited to the retail domain, paars is highly generalizable and can be applied to various fields requiring behavioral forecasting. simulation is important in many domains including economy and finance (e.g. supply and demand, stock market), public policy and urban planning @xcite . ## conclusions in this work, we introduced paars, a novel framework to simulate human shoppers behaviour by leveraging persona driven llm agents alongside an alignment suite at the individual and group levels to ensure reliability. our experiments show improvements on the alignment tasks when using personas over historical behaviour only. while we demonstrate an initial application for a/b testing, the framework can be further applied to surveying and market research among others. beyond retail, the framework generalizability makes it applicable to a wide range of domains requiring simulations, including financial markets, public policy and urban planning. given the large potential, paars opens the door for challenging and impactful research, such as persona and session generation improvements, multilinguality and multimodality, algorithmic fidelity across marketplaces and research into cultural nuances.