A data mining framework for targeted category promotions

Reutterer, Thomas and Hornik, Kurt ORCID: https://orcid.org/0000-0003-4198-9911 and March, Nicolas and Gruber, Kathrin (2016) A data mining framework for targeted category promotions. Journal of Business Economics, 87 (3). pp. 337-358. ISSN 0044-2372

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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 cus- tomers' multi-category purchase histories as input data and proceeds in a stepwise manner. It combines various data compression techniques and integrates an opti- mization 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 pro- posed 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.

Item Type: Article
Additional Information: Open access funding provided by Vienna University of Economics and Business (WU).
Keywords: Cross-category purchases / Target marketing / Customized coupons / Clustering / Association rule mining
Classification Codes: JEL C52, C55, M3
Version of the Document: Published
Depositing User: ePub Administrator
Date Deposited: 03 Jun 2016 12:15
Last Modified: 24 Oct 2019 13:41
Related URLs:
FIDES Link: https://bach.wu.ac.at/d/research/results/77071/
URI: https://epub.wu.ac.at/id/eprint/5074


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