Seminar: Key Drivers Analysis and Optimization
Stefan Conrady, Bayesia USA
Overview
This half-day seminar presents a complete workflow for developing a Probabilistic Structural Equation Model (PSEM) based on Bayesian networks and utilizing the BayesiaLab software platform. Our objective is to identify key drivers of satisfaction with a PSEM that is machine-learned from consumer survey data. A key challenge in this context is to resolve the conflict between "driver" as a causal concept versus the non-causal nature of non-experimental survey data. Furthermore, we illustrate how quantifying the joint probability of hypothetical scenarios is critical for establishing priorities for improving customer satisfaction.
Background & Theory
Structural Equation Modeling is a statistical technique for testing and estimating causal relations using a combination of statistical data and qualitative causal assumptions. Structural Equation Models (SEM) allow both confirmatory and exploratory modeling, meaning they are suited to both theory testing and theory development.
What we call Probabilistic Structural Equation Models (PSEMs) in BayesiaLab are conceptually similar to traditional SEMs. However, PSEMs are based on a Bayesian network structure as opposed to a series of equations. More specifically, PSEMs can be distinguished from SEMs in terms of key characteristics:
All relationships in a PSEM are probabilistic—hence the name, as opposed to having deterministic relationships plus error terms in traditional SEMs.
PSEMs are nonparametric, facilitating the representation of nonlinear relationships and relationships between categorical variables.
The structure of PSEMs is partially or fully machine-learned from data.
Specifying and estimating a traditional SEM requires a high degree of statistical expertise. Additionally, the multitude of manual steps involved can make the entire SEM workflow extremely time-consuming. On the other hand, the PSEM workflow in BayesiaLab is accessible to non-statistician subject matter experts. Perhaps more importantly, it can be faster by several orders of magnitude. Finally, once a PSEM is validated, it can be utilized like any other Bayesian network. This means that the full array of analysis, simulation, and optimization tools is available to leverage the knowledge represented in the PSEM.
Case Study: Key Drivers Analysis from Consumer Survey Data
In this seminar, we present a prototypical PSEM application: key drivers analysis and product optimization based on consumer survey data. We examine how consumers perceive product attributes and how these perceptions relate to the consumers’ purchase intent for specific products.
Given the inherent uncertainty of survey data, we also wish to identify higher-level variables, i.e., “latent” variables that represent concepts that are not directly measured in the survey. We do so by analyzing the relationships between the so-called “manifest” variables, i.e., variables directly measured in the survey. Including such concepts helps in building more stable and reliable models than what would be possible using manifest variables only.
Our overall objective is to make surveys clearer to interpret by researchers and make them “actionable” for managerial decision-makers. The ultimate goal is to use the generated PSEM for prioritizing marketing and product initiatives to maximize purchase intent.
Presentation Video
Presentation Slides
See Also
About the Instructor
Stefan Conrady has over 20 years of experience in decision analysis, analytics, market research, and product strategy with Mercedes-Benz, BMW Group, Rolls-Royce Motor Cars, and Nissan, which included assignments in North America, Europe, and Asia.
Today, in his role as Managing Partner of Bayesia USA and Bayesia Singapore, he is recognized as a thought leader in applying Bayesian networks for research, analytics, and reasoning.
Recently, Stefan and his colleague Dr. Lionel Jouffe co-authored Bayesian Networks & BayesiaLab — A Practical Introduction for Researchers, which is now available as an e-book.
Last updated
