In this presentation, we demonstrate how to find and characterize segmentation in consumer liking scores through a series of machine learning approaches. First, we show how to find meaningful consumer segments and evaluate these segments for stability. Next, we demonstrate how to apply several common machine learning approaches to predict cluster segment based on consumer demographic, behavioral, and psychographic information. Finally, we show how model explainer tools can help experimenters look inside the black box to better understand the choices made by the prediction model. Throughout this presentation, we illustrate the use of these tools with a real-world example.
Dr. Tian Yu obtained her Ph.D. degree in neuroscience, focusing on taste signal transduction. Dr. Yu has published a dozen peer-reviewed articles and one book chapter. She has presented her work in numerous conferences and received international recognition.
Dr. Thierry Worch is an expert in sensory and consumer methods, sensometrics, and data science who works in the Friesland-Campina Sensory and Consumer R&D department. Besides publishing various papers related to Sensometric, he is also the co-author with Sébastien Lê of “Analyzing sensory data with R” (CRC Press) and is currently working on “Introduction to Data Science for Sensory and Consumer Scientists” (CRC Press).
Dr. John Ennis, co-founder of Aigora, is a world-renowned authority on the use of artificial intelligence within sensory and consumer science. John is a Ph.D. mathematician who conducted his postdoctoral studies in computational neuroscience and who has more than a dozen years of experience as a sensory and consumer science consultant.