2025-09-01 –, B05-B06
Consumer choice models are an important part of product innovation and market strategy. In this talk we'll see how they can be used to learn about substitution goods and market shares in competitive markets using PyMC marketing's new consumer choice module.
The market sets the price, but what drives market demand? Classical implementations of discrete choice models discovered that market structure needed to be explicitly encoded in the model to avoid the problem of implausible predictions about the substitution value of distinct products. We demonstrate this issue and how to resolve it by adding more explicit structure to the models of market demand while giving insight into what drives the utility of products for consumers. These consumer choice models find a natural expression in the Bayesian paradigm and we show how to fit them to real data with PyMC Marketing's Consumer Choice module.
Intermediate
Prerequisites:Basics of A/B testing and why randomisation to the control / treatment group is useful
Abstract as a tweet (X) or toot (Mastodon):Improving Product-market-fit with Discrete Choice Models
I’m a data scientist specialising in probabilistic modelling for the study of risk and causal inference. I have experience in model development, deployment, multivariate testing and monitoring.
I’m interested in questions of inference and measurement in the face of natural variation and confounding.
My academic background is in mathematical logic and philosophy where I mostly imagined possible worlds and modal logics.