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A data mining framework for targeted category promotions

Thomas Reutterer (), Kurt Hornik, Nicolas March and Kathrin Gruber
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Thomas Reutterer: WU Vienna University of Economics and Business
Kurt Hornik: WU Vienna University of Economics and Business
Nicolas March: REWE Digital GmbH
Kathrin Gruber: WU Vienna University of Economics and Business

Journal of Business Economics, 2017, vol. 87, issue 3, 337-358

Abstract: Abstract This research presents a new approach to derive recommendations for segment-specific, targeted marketing campaigns on the product category level. The proposed methodological framework serves as a decision support tool for customer relationship managers or direct marketers to select attractive product categories for their target marketing efforts, such as segment-specific rewards in loyalty programs, cross-merchandising activities, targeted direct mailings, customized supplements in catalogues, or customized promotions. The proposed methodology requires customers’ multi-category purchase histories as input data and proceeds in a stepwise manner. It combines various data compression techniques and integrates an optimization approach which suggests candidate product categories for segment-specific targeted marketing such that cross-category spillover effects for non-promoted categories are maximized. To demonstrate the empirical performance of our proposed procedure, we examine the transactions from a real-world loyalty program of a major grocery retailer. A simple scenario-based analysis using promotion responsiveness reported in previous empirical studies and prior experience by domain experts suggests that targeted promotions might boost profitability between 15 % and 128 % relative to an undifferentiated standard campaign.

Keywords: Cross-category purchases; Target marketing; Customized coupons; Clustering; Association rule mining (search for similar items in EconPapers)
JEL-codes: C52 C55 M3 (search for similar items in EconPapers)
Date: 2017
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Handle: RePEc:spr:jbecon:v:87:y:2017:i:3:d:10.1007_s11573-016-0823-7