Applying Bayesian Network Models to Fuse Information from Different Data Sources for Product Innovation and Consumer Understanding
Yong Zhang, Ph.D., Senior Scientist, Procter & Gamble
Successful product innovation relies heavily on multiple types of product tests. These tests include virtual online concept test at earlier stage, blind and identified usage test of prototype product in the middle, and household panel survey when the product is in market. Currently, most of data analytics were conducted based on separate analysis of sporadic and piecemeal data from different tests. The product development decisions were often made through team meeting to qualitatively summarize isolated analytics of different product tests. We developed a Bayesian framework based on Bayesian Belief Network (BBN) model to systematically aggregate data from different tests and fuse information quantitatively from different sources for better product innovation and consumer understanding. The developed methods can be used either to identify “Body of Evidence” from all available data sources or to conduct cross inference from one data source to another data source.
Dr. Yong Zhang leverages Bayesian data and modeling science to develop strategy of product design, manufacturing, storage and transportation across P&G to improve consumers’ life quality and drive positive influence on environment and society under different scenarios of climate change. 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 variety of data sources to find “Body of Evidence” for consumer and product research based on Nonparametric Bayesian statistics and deep learning algorithms.