Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design

Designing products to meet consumers' preferences is essential for a business's success. We propose the Gradient-based Survey (GBS), a discrete choice experiment for multiattribute product design. The experiment elicits consumer preferences through a sequence of paired comparisons for partial profiles. GBS adaptively constructs paired comparison questions based on the respondents' previous choices. Unlike the traditional random utility maximization paradigm, GBS is robust to model misspecification by not requiring a parametric utility model. Cross-pollinating the machine learning and experiment design, GBS is scalable to products with hundreds of attributes and can design personalized products for heterogeneous consumers. We demonstrate the advantage of GBS in accuracy and sample efficiency compared to the existing parametric and nonparametric methods in simulations

Editorial: Defining interesting research problems

We argue that research problems are only interesting relative to some external audience. Interesting academic research should impact,at least,that external audience. Hence, we should target our research toward specific external audiences. Several foreboding trends that exacerbate the urgency of this targeting are discussed. To facilitate the targeting task,a partial list of fifteen possible audiences for academic research in marketing is identified. We discuss some of them,including practitioners,in detail. For example,we conclude that,for our research to be interesting to practitioners,practitioners must have the ability to improve and to make better decisions with enhanced understanding. Finally,we strongly suggest that we focus our research on fundamental problems in marketing. These are problems with the property that external audiences would first look to the marketing literature for answers

Editorial: Marketing Science, Models, Monopoly Models, and Why We Need Them

No abstract available