arXiv:2606.16183v1 Announce Type: cross Abstract: We develop an LLM-powered virtual population model that simulates demand for pricing decisions, in settings where products are described by rich unstructured information, such as text descriptions and images, and where decision makers need not only mean-demand predictions but also uncertainty estimates for counterfactual prices. Our model represents exposed customers as draws from a finite mixture of customer personas. For each persona, product, and candidate price, an LLM elicits a persona-level purchase probability using both structured perso
The rapid advancement and accessibility of large language models (LLMs) enable the creation of sophisticated, persona-driven simulation tools that can process unstructured data previously inaccessible to traditional models.
This development allows for more accurate and nuanced demand forecasting and pricing strategies in complex markets, providing granular insights into customer behavior and competitive dynamics.
Traditional demand forecasting, which often relies on structured data and aggregate models, now has a powerful alternative that leverages rich textual and image data to simulate individual and market responses with greater fidelity.
- · E-commerce platforms
- · Retailers
- · Pricing analytics companies
- · AI software developers
- · Traditional market research firms
- · Legacy statistical modeling platforms
- · Companies with undifferentiated products
Businesses gain significantly improved ability to optimize pricing and product offerings in real-time based on simulated customer responses.
Increased efficiency in resource allocation and reduced inventory waste across various industries as demand becomes more predictable.
Markets may become more dynamic and efficient, but also potentially more volatile as pricing algorithms compete and adapt at unprecedented speeds, leading to new forms of market manipulation or flash crashes in demand.
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