A Conversation with Luca Fiaschi of PyMC Labs: Synthetic Consumers & the Future of Product Testing

In this episode of Retailgentic, Scot sits down with Luca to unpack a research paper that blends academic rigor with real-world implications:
“LLMs Reproduce Human Purchase Intent via Semantic Similarity licitation of Likert Ratings.”

The paper, co-authored with Colgate-Palmolive researchers, explores whether AI can accurately simulate human reactions to product concepts, enough to replace or accelerate traditional consumer panels, which are slow, expensive, and hard to scale.

This one goes deep, but in ways that any retail or AI leader should care about. Scot and Luca discuss:

• Colgate’s Challenge: How to test product concepts faster and at scale.
• Synthetic Consumers: AI models that react to products like human panels.
• Accuracy Breakthrough: Reaching ~73–74% agreement with real consumers.
• Fixing LLM Failure Modes: Why naive prompts don’t work, and what does.
• Bayesian Reasoning: Adding uncertainty so AI stops being confidently wrong.
• Smarter A/B Testing: Using AI to pre-screen ideas before running live experiments.
• Digital Clones: Future consumers earning money by sharing preference data safely.
• Simulated Populations: Matching real audiences for testing and predictions.

AI isn’t just helping brands write copy or generate images, it’s beginning to think like their customers. If synthetic consumers continue to evolve at this pace, product development, A/B testing, and personalization may look completely different in just a few years.

Timestamps:
03:00 — Luca’s background: Rocket Internet, HelloFresh, Lazada, Stitch Fix
08:00 — How PyMC Labs was founded & why Bayesian modeling matters
16:00 — Bayesian thinking explained in simple terms
19:00 — High-stakes decisions & why probabilistic reasoning matters
24:00 — Colgate’s challenge: testing product concepts at scale
26:00 — How synthetic consumer panels work
30:00 — Accuracy results: humans vs. AI (~73–74%)
32:00 — Why naive LLM prompting fails (“mode collapse”)
35:00 — How reasoning → scoring solves accuracy issues
38:00 — Example: synthetic consumers evaluating PyMC’s own website redesign
44:00 — How AI can pre-screen ideas for smarter A/B testing
48:00 — Where AI cannot replace causal testing
49:00 — Digital clones & monetizable consumer preferences
52:00 — Future benchmarks, new LLMs & evaluation methods

👉 Connect with Luca: https://www.linkedin.com/in/lfiaschi/
👉 Learn more about PyMC Labs: https://www.pymc-labs.com
👉 Check out the paper: https://arxiv.org/pdf/2510.08338

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