Submodular Maximization under Fading Model: Building Online Quizzes for Better Customer Segmentation
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E-Commerce personalization aims to provide individualized offers, product recommendations, and other content to customers based on their interests. The foundation of any personalization effort is customer segmentation. The idea of customer segmentation is to group customers together according to identifiable segmentation attributes including geolocation, gender, age, and interests. Personality quiz turns out to be a powerful tool that enables costumer segmentation by actively asking them questions, and marketers are using it as an effective method of generating leads and increasing e-commerce sales. In this paper, we study the problem of how to select and sequence a group of quiz questions so as to optimize the quality of customer segmentation. In particular, we use conditional entropy to measure the utility of a given group of quiz questions. We model the user behavior when interacting with a sequence of quiz questions as a Markov process. Then we develop a series of question allocation strategies with provable performance bound.
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