Webinar: Smarter Segmentation — How Bayesian Networks and GenAI Decode Consumer Diversity
Live Webinar with Q&A Session on Thursday, July 17, 2025, at 11:00 a.m. (EDT, UTC-4)
The Webinar at a Glance
Join us for a live webinar that reexamines the logic of consumer targeting and segmentation. We explore how Bayesian networks can learn and represent high-dimensional joint probability distributions, enabling marketers to identify, evaluate, and optimize target groups. You’ll see how this framework supports data-driven segmentation strategies that incorporate both purchase potential and communication reach. Through real-world examples, including psychographic clustering and automotive buyer segmentation, we’ll demonstrate how BayesiaLab and its GenAI assistant, Hellixia, deliver interpretable and actionable consumer segments.
Webinar Abstract
The Rationale for Targeting and Segmentation
While the terminology of consumer targeting and market segmentation may appear self-evident, this webinar adopts a formal perspective to examine the underlying logic and implications of segmentation strategies in marketing science.
We begin by revisiting the rationale for targeting: under what conditions does promoting a product to a specific subset of the population outperform a universal outreach strategy? This seemingly intuitive question quickly reveals its complexity when formalized through probabilistic modeling. In particular, we explore how the intersection of multiple targeting attributes can drastically reduce the size and feasibility of a target market, underscoring the critical role of joint probability distributions.
Joint Probability Distribution and Bayesian Networks
To address this challenge, we introduce Bayesian networks as a computationally efficient framework for learning and representing high-dimensional joint probability distributions. Beyond their representational capacity, Bayesian networks support integrated reasoning about consumer characteristics, purchase likelihood, and channel reachability, all while explicitly modeling uncertainty and incorporating both data-driven insights and expert knowledge.
Consumer Clustering and Interpretation with GenAI
The webinar further demonstrates how Bayesian networks enable the optimization of target market definitions and facilitate the identification of multiple, distinct consumer segments. Finally, we present the integration of Generative AI, via BayesiaLab’s subject matter assistant Hellixia, to assist in the interpretation and communication of segment profiles, bridging statistical validity and plain-language interpretation.
Webinar Examples and BayesiaLab Demonstrations
Psychographic Clustering Example
The first practical example presented in the webinar illustrates a hierarchical clustering process based on a psychographic survey comprising 240 personality-related attributes. Using BayesiaLab’s learning and clustering algorithms, we first induce latent factors from the manifest variables, followed by a meta-clustering that identifies distinct personality segments. To enhance interpretability, BayesiaLab’s Hellixia component leverages Generative AI to propose meaningful, context-aware names for both the factors and the resulting clusters.
Automotive Buyer Segmentation
The second demonstration is based on an automotive buyer survey that includes a more concise set of personality variables, along with a broad range of attributes concerning purchase intent, demographics, and media consumption behaviors.
In this context, BayesiaLab is used to create consumer segments that are not only differentiated by behavioral and demographic characteristics but also quantified by their associated sales potential. The inclusion of media consumption data further enables the identification of communication channels that can effectively reach each segment.
The result is a set of interpretable and relevant consumer segments, each with a defined sales potential and actionable communication pathways.
About the Presenter
Stefan Conrady
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.