Understanding Product and Consumer Segments

Presented at the 6th Annual BayesiaLab Conference in Chicago, November 1-2, 2018.

Abstract

Driver models built on individual products can be biased or even misleading. We model and analyze a single product or a consumer segment by “borrowing” information from other product legs or similar studies. Specifically, we leverage Impact Analysis, Landscape Analysis, and Profile Analysis to sharpen our understanding of a single product leg or consumer segment with a relatively small base size. These new analysis methods have been added as new features into BayesiaLab 8.0.

Presentation Video

Presentation Slides

About the Presenter

Dr. Yong Zhang leverages Bayesian data and modeling science to develop a product design, manufacturing, storage, and transportation strategy across P&G to improve consumers’ life quality and drive positive influence on the environment and society under different climate change scenarios. He develops modeling and simulation methods and tools through Front End Innovation projects to enable and promote the capability across P&G for breakthrough consumer understanding and product innovation. The methods and tools can be used to extract and integrate information from a variety of data sources to find a “Body of Evidence” for consumer and product research based on Nonparametric Bayesian statistics and deep learning algorithms.