Targeting with Joint Probability: Why Bayesian Networks Matter in High-Dimensional Decision Models

Abstract

Many data-driven targeting strategies in marketing rely on estimating conditional purchase probabilities given consumer attributes. However, a critical component of decision-making often goes unaddressed: estimating the size of a target group defined by multiple intersecting attributes. This requires modeling the joint distribution of those attributes—a task that is often intractable with traditional statistical frameworks.

This talk presents a principled approach to overcoming this limitation using Bayesian networks, which provide a compact and interpretable representation of high-dimensional joint probability distributions. By leveraging conditional independence structures, Bayesian networks allow for efficient computation of both (i) conditional response probabilities and (ii) marginal probabilities over complex subpopulations.

Using a consumer targeting scenario, we demonstrate how Bayesian networks enable simultaneous reasoning about who is likely to buy and how many people match that profile, making them uniquely suited for high-stakes decisions in resource-constrained environments. We also contrast this with common machine learning models that offer predictive performance but lack a tractable probabilistic backbone.

About the Presenter

Stefan Conrady has over 20 years of experience in decision analysis, analytics, market research, and product strategy, having worked with Mercedes-Benz, BMW Group, Rolls-Royce Motor Cars, and Nissan across North America, Europe, and Asia. As Managing Partner of Bayesia USA and Bayesia Singapore, he is widely recognized as a thought leader in applying Bayesian networks to research, analytics, and decision-making. Together with his business partner, Dr. Lionel Jouffe, he co-authored Bayesian Networks & BayesiaLab — A Practical Introduction for Researchers, an influential resource now widely cited in academic literature. With their deep expertise in Bayesian networks for Key Driver Analysis and Optimization, Stefan and Lionel are highly sought-after consultants, advising global leaders such as Procter & Gamble, Coca-Cola, UnitedHealth Group, L’Oréal, the World Bank, and many of the world’s largest market research firms.