Scaling Customer Research with Synthetic Consumers | PyMC Labs

Описание: Here are links to the paper if you want a deeper dive:
https://arxiv.org/html/2510.08338v3
https://arxiv.org/pdf/2510.08338

Traditional customer research depends on human respondents, a process that’s slow, costly, and limited in scale. But what if AI could simulate human decision-making with remarkable accuracy?

In this session, the PyMC Labs team explores how Large Language Models (LLMs) and our Semantic Similarity Rating (SSR) methodology can replicate human purchase intent with up to 90% of human test-retest reliability, the foundation of our viral study, “LLMs Reproduce Human Purchase Intent.”

Building on this research, we’ll introduce our Synthetic Consumers Platform, a breakthrough in simulated customer insights that generates reliable, human-like survey data at scale and in a fraction of the time.

You’ll see how synthetic consumers and our SSR methodology can transform customer research by enabling faster iteration, dramatically reducing costs, and reimagining how we test marketing strategies, ads, and creatives.

You’ll learn:
How LLMs and semantic similarity modeling reproduce human purchase intent with up to 90% accuracy,
What synthetic consumers are and how they reshape survey design and execution,
How SSR bridges behavioral science and AI for deeper, more meaningful insights,
How an AI-driven insights platform accelerates testing, validation, and decision-making in marketing research and creative testing,