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.

In this talk I will present two new open-source packages that make up a powerful and state-of-the-art marketing analytics toolbox. Specifically, PyMC-Marketing is a new library built on top of the popular Bayesian modeling library PyMC. PyMC-Marketing allows robust estimation of customer acquisition costs (via media mix modeling) as well as customer lifetime value. In addition, I will show how we can estimate the effectiveness of marketing campaigns using a new Bayesian causal inference package called CausalPy. The talk will be applied with a real-world case-study and many code examples. Special emphasis will be placed on the interplay between these tools and how they can be combined together to make optimal marketing budget decisions in complex scenarios.