Abstract
It is crucial to segment customers intelligently in order to offer more targeted and personalized products and services. Traditionally, customer segmentation is achieved using statistics-based methods that compute a set of statistics from the customer data and group customers into segments by applying clustering algorithms. Recent research proposed a direct grouping-based approach that combines customers into segments by optimally combining transactional data of several customers and building a data mining model of customer behavior for each group. This paper proposes a new micro-targeting method that builds predictive models of customer behavior not on the segments of customers but rather on the customer-product groups. This micro-targeting method is more general than the previously considered direct grouping method. We empirically show that it outperforms the direct grouping and statistics-based segmentation methods across multiple experimental conditions and that it generates predominately small-sized segments, thus providing additional support for the micro-targeting approach to personalization.
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The preliminary version of this paper, titled “Dynamic micro-targeting: fitness-based approach to predicting individual preferences” appeared in the Proceedings of the Seventh IEEE International Conference on Data Mining in 2007.
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Jiang, T., Tuzhilin, A. Dynamic micro-targeting: fitness-based approach to predicting individual preferences. Knowl Inf Syst 19, 337–360 (2009). https://doi.org/10.1007/s10115-008-0149-z
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DOI: https://doi.org/10.1007/s10115-008-0149-z