In this session, the PyMC Labs team explores how Large Language Models (LLMs) and their Semantic Similarity Rating (SSR) methodology can replicate human purchase intent with up to 90% of human test-retest reliability. Building on this research, we’ll introduce the 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 learn how synthetic consumers and SSR can transform customer research by enabling faster iteration and significantly reducing costs, and how an AI-driven insights platform accelerates testing, validation, and decision-making in marketing research and creative testing.

Date: 2025-10-23. Joint webinar showing how a modern data integration layer can transform Bayesian Marketing Mix Models (MMMs). From automated data ingestion and standardization with Fivetran connectors and dbt models to Bayesian inference and performance optimization with PyMC-Marketing. You will learn: unify ad and commerce data across platforms; transform standardized tables into model-ready datasets; run Bayesian MMMs with uncertainty quantification, adstock and saturation effects, and budget optimization; and scale a production-ready MMM pipeline across markets and brands. Start time: 15:00 UTC (8:00 PT / 11:00 ET / 5:00 Berlin).

A rigorous apples-to-apples benchmark between PyMC-Marketing and Google’s Meridian for Marketing Mix Modeling, covering default priors, model structures, and synthetic datasets that simulate everything from startups to global enterprises. Includes speed, accuracy, scalability comparisons and practical recommendations for choosing the MMM library.

This one-hour tutorial introduces new users to version 5 of PyMC, a powerful Python, open source library for probabilistic programming and Bayesian statistical modeling. Participants will learn the fundamentals of PyMC, best practices for installation and setup, and gain hands-on experience building their first Bayesian model.