🇺🇸Applying Bayesian Media Influence Network Maps to Women’s Apparel
Martin Block, Ph.D., Northwestern University
To be presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 11, 2024.
Abstract
A Bayesian Media Influence Network solves several problems with a traditional regression-based media marketing mix model. First is the problem of consistent measures across different media types. This is solved by using syndicated survey media and marketing influence measures. Second is the problem of simultaneous consumption and the assumption of independence among predictor variables. Third is the problem of non-linear relationships that may exist between media types and a criterion variable such as sales. A Bayesian Belief Network solves these last two problems and provides an easy-to-understand tool to aid in what has been a traditionally difficult marketing asset allocation decision. Using women’s apparel as an example, the efficacy of the Bayesian Network for monthly spending is shown, identifying a pattern of media types. Factoring past brand purchase behavior into segments shows how the Bayesian Network can be used to target High-End brands, Accessories, and Active Shoes as examples. Other product categories and target definitions can certainly be used.
About the Presenter
Martin Block is a Professor Emeritus in Integrated Marketing Communications at Northwestern University and a Director of the Retail Analytics Council. Prior to 1985, he was Professor and Chairperson of the Department of Advertising at Michigan State University. Prior to that, he worked as a Senior Market Analyst in Corporate Planning at the Goodyear Tire and Rubber Company. Co-author of Understanding China’s Digital Generation, Media Generations: Media Allocation in a Consumer-Controlled Marketplace, Retail Communities: Customer Driven Retailing, Analyzing Sales Promotion, and Business-to-Business Market Research. He has published in many academic research journals and trade publications and has several book chapters. Paul has a Ph.D. from Michigan State.
Register here for the 2024 BayesiaLab Spring Conference, April 11-12, 2024:
Last updated
