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Document Type

Research Paper

Abstract

A primary task of customer relationship management (CRM) is the transformation of customer data into business value related to customer binding and development, for instance, by offering additional products that meet customers’ needs. A customer’s purchasing history (or sequence) is a promising feature to better anticipate customer needs, such as the next purchase intention. To operationalize this feature, sequences need to be aggregated before applying supervised prediction. That is because numerous sequences might exist with little support (number of observations) per unique sequence, discouraging inferences from past observations at the individual sequence level. In this paper the authors propose mechanisms to aggregate sequences to generalized purchasing types. The mechanisms group sequences according to their similarity but allow for giving higher weights to more recent purchases. The observed conversion rate per purchasing type can then be used to predict a customer’s probability of a next purchase and target the customers most prone to purchasing a particular product. The bias– variance trade-off when applying the models to target customers with respect to the lift criterion are discussed. The mechanisms are tested on empirical data in the realm of cross-selling campaigns. Results show that the expected bias–variance behavior well predicts the lift achieved with the mechanisms. Results also show a superior performance of the proposed methods compared to commonly used segmentation-based approaches, different similarity measures, and popular class predictors. While the authors tested the approaches for CRM campaigns, their parameterization can be adjusted to operationalize sequential features of high cardinality also in other domains or business functions.

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