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Personality quiz is a powerful tool that enables costumer segmentation byactively asking them questions, and marketers are using it as an effectivemethod of generating leads and increasing e-commerce sales. In this paper, westudy the problem of how to select and sequence a group of quiz questions so asto optimize the quality of customer segmentation. We assume that the customerwill sequentially scan the list of questions. After reading a question, thecustomer makes two, possibly correlated, random decisions: 1) she first decideswhether to answer this question or not, and then 2) decides whether to continuereading the next question or not. We further assume that the utility ofquestions that have been answered can be captured by a monotone and submodularfunction. In general, our problem falls into the category of non-adaptiveactive learning based customer profiling. Note that under the our model, theprobability of a question being answered depends on the location of thatquestion, as well as the set of other questions placed ahead of that question,this makes our problem fundamentally different from existing studies onsubmodular optimization. We develop a series of question selection andsequencing strategies with provable performance bound. Although we focus on theapplication of quiz design in this paper, our results apply to a broad range ofapplications, including assortment optimization with position bias effect.

Cascade Submodular Maximization: Question Selection and Sequencing in Online Personality Quiz