Abstract
Data mining methods have been successfully used in direct marketing to model the behavior of responders. But these response models do not take in account, the behavior of customers who would take an action irrespective of marketing action. Redundant marketing communications sometimes annoy the customer and reduce the brand value of the company. Accurate targeting of customers also reduces direct marketing cost. Incremental response modeling aims to predict the behavior of customers who respond positively only in the case of marketing. In this paper, we propose a two-step approach for incremental response modeling. In the first step, we segment the data using uplift decision trees using traditional and modified divergence metrics. Then, in the second step we use the standard incremental response modeling methods. Experiments on real world direct marketing campaign data showed that the proposed method outperforms the uplift decision trees.
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Kondareddy, S.P., Agrawal, S., Shekhar, S. (2016). Incremental Response Modeling Based on Segmentation Approach Using Uplift Decision Trees. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2016. Lecture Notes in Computer Science(), vol 9728. Springer, Cham. https://doi.org/10.1007/978-3-319-41561-1_5
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DOI: https://doi.org/10.1007/978-3-319-41561-1_5
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