The Qual-Quant Leap: Key Driver Analysis with Bayesian Networks and GenAI
Key Driver Analysis reimagined: machine-learned Bayesian networks now fuse survey data, customer narratives, and LLM knowledge.
Indiana Wesleyan University, Cincinnati Education and Conference Center
9286 Schulze Drive, West Chester Township, OH 45069
September 23, 2026, from 1:00 p.m. to 5:00 p.m. (EDT)
Key Driver Analysis answers a deceptively simple question: which changes matter most for the outcome you care about? This half-day seminar traces how the answer has evolved across three generations of methodology and equips you to practice the newest one, in which numerical data, customer narratives, expert knowledge, and the knowledge encoded in large language models all become admissible evidence in a single causal model.
There are no prerequisites. Familiarity with survey research is helpful, and every concept is demonstrated live as a practical, reproducible workflow in BayesiaLab.
Seminar Program
Three Generations of Key Driver Analysis
How driver analysis evolved from expert-specified structural equation models, to probabilistic models machine-learned from survey data, to today’s models that fuse data, text, and the knowledge encoded in large language models. A simple orientation map, organized by modeling purpose (association versus causation) and model source (theory versus data), frames the journey and recurs throughout the day.
Foundations, and Why Conventional Driver Analysis Fails
A compact introduction to Bayesian networks: models of the joint probability distribution that reason in any direction, represent latent constructs, and measure importance in information-theoretic terms. Then an honest tour of the obstacles: causal language applied to observational data, the astronomically large space of possible causal structures, multicollinearity that produces significant but wrong-signed regression coefficients, ceiling effects in rating scales, and missing values that standard remedies quietly corrupt.
Building a Probabilistic Structural Equation Model, with GenAI Assistance
The core hands-on workflow, end to end, on a consumer survey dataset: unsupervised structure learning, validation through perturbation and arc confidence analysis, variable clustering, induction of latent factors, and assembly of the complete Probabilistic Structural Equation Model. Hellixia, the generative-AI assistant in BayesiaLab, assists throughout, proposing names and descriptions for latent factors as they emerge and cross-checking machine-learned clusters against semantic knowledge. The result is a compact, interpretable model of what shapes the outcome. What it does not yet provide is priorities.
From Association to Causation
What separates an observational effect from a causal one, and what it takes to move from the first to the second. A practical criterion for identifying confounders in minutes rather than weeks, effect estimation under explicitly stated assumptions, and a worked example in which the causal effect turns out to be half the observational one. Large language models enter as an additional source of causal knowledge, treated like an expert panel: elicited, inspected, and corrected, never taken as evidence.
From Drivers to Decisions: Optimization
Why a ranked table of effects must never be read as an action plan. Optimization under realistic constraints: the model’s own joint probability as a built-in plausibility check, competitive benchmarks as achievability limits, and costs where they are known. Priority-sequence optimization for perceptions, which cannot be dialed in directly, versus point optimization for controllable levers. The output is a defensible, ordered plan, not just a ranking.
The Qual-Quant Leap: From Narratives to Networks
The centerpiece of the third generation. A corpus of open-ended customer reviews is transformed into a respondent-level dataset: thematic and emotional dimensions are extracted from the text, and every document is scored on every dimension. The machine-learning workflow from the earlier module then runs unchanged on this text-born data, yielding a full driver model without a survey instrument. Qualitative material has entered the quantitative pipeline.
Synthesis and Q&A
Triangulating across knowledge sources, maintaining an audit trail from raw input to reported effect, and the honest list of pitfalls with their safeguards. Open discussion.
Venue
The seminar takes place at the Indiana Wesleyan University, Cincinnati Education and Conference Center, 9286 Schulze Drive, West Chester Township, OH 45069.
Free parking is available.
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.