Webinar: A Gaussian Process Model for Response Time in Conjoint Surveys

This post is by Eric.

This Wednesday, at 11:30 am ET, Elea Feit is stopping by to talk to us about her recent work on Conjoint models fit using GPs. You can register here.

Abstract

Choice-based conjoint analysis is a widely-used technique for assessing consumer preferences. By observing how customers choose between alternatives with varying attributes, consumers’ preferences for the attributes can be inferred. When one alternative is chosen over the others, we know that the decision-maker perceived this option to have higher utility compared to the unchosen options. In addition to observing the choice that a customer makes, we can also observe the response time for each task. Building on extant literature, we propose a Gaussian Process model that relates response time to four features of the choice task (question number, alternative difference, alternative attractiveness, and attribute difference). We discuss the nonlinear relationships between these four features and response time and show that incorporating response time into the choice model provides us with a better understanding of individual preferences and improves our ability to predict choices.

About the speaker

Elea Feit is an Associate Professor of Marketing at Drexel University. Prior to joining Drexel, she spent most of her career at the boundary between academia and industry, including positions at General Motors Research, The Modellers, and Wharton Customer Analytics. Her work is inspired by the decision problems that marketers face and she has published research on using randomized experiments to measure advertising incrementality and using conjoint analysis to design new products. Methodologically, she is a Bayesian with expertise in hierarchical models, experimental design, missing data, data fusion, and decision theory. She is also the co-author of R for Marketing Research and Analytics. More at eleafeit.com.

Video recording is available here.

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