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Conjoint analysis is one of the most popular market research methodologies for assessing how customers with heterogeneous preferences appraise various objective characteristics in products or services, which provides critical inputs for many marketing decisions, e.g. optimal design of new products and target market selection. Nowadays it becomes practical in e-commercial applications to collect millions of samples quickly. However, the large-scale data sets make traditional conjoint analysis coupled with sophisticated Monte Carlo simulation for parameter estimation computationally prohibitive. In this paper, we report a successful large-scale case study of conjoint analysis on click through stream in a real-world application at Yahoo!. We consider identifying users' heterogenous preferences from millions of click/view events and building predictive models to classify new users into segments of distinct behavior pattern. A scalable conjoint analysis technique, known as tensor segmentation, is developed by utilizing logistic tensor regression in standard partworth framework for solutions. In offline analysis on the samples collected from a random bucket of Yahoo! Front Page Today Module, we compare tensor segmentation against other segmentation schemes using demographic information, and study user preferences on article content within tensor segments. Our knowledge acquired in the segmentation results also provides assistance to editors in content management and user targeting. The usefulness of our approach is further verified by the observations in a bucket test launched in Dec. 2008.
Chu et al. (Sun,) studied this question.