A data mining framework for targeted category promotions
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 produc...
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Published in | Zeitschrift für Betriebswirtschaft Vol. 87; no. 3; pp. 337 - 358 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.04.2017
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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ISSN: | 0044-2372 1861-8928 |
DOI: | 10.1007/s11573-016-0823-7 |