Building an association rules framework for target marketing
The discovery of association rules is a popular approach to detect cross-category purchase correlations hidden in large amounts of transaction data and extensive retail assortments. Traditionally, such item or category associations are studied on an ‘average’ view of the market and do not reflect heterogeneity across customers. With the advent of loyalty programs, however, tracking each program member’s transactions has become facilitated, enabling retailers to customize their direct marketing efforts more effectively by utilizing cross-category purchase dependencies at a more disaggregate level. In this paper, we present the building blocks of an analytical framework that allows retailers to derive customer segment-specific associations among categories for subsequent target marketing. The proposed procedure starts with a segmentation of customers based on their transaction histories using a constrained version of K-centroids clustering. In a second step, associations are generated separately for each segment. Finally, methods for grouping and sorting the identified associations are provided. The approach is demonstrated with data from a grocery retailing loyalty program.
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