A Data Mining Framework for Targeted Category Promotions

Thomas Reutterer, Kurt Hornik, Nicolas March, Kathrin Gruber

Publication: Scientific journalJournal articlepeer-review

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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.
Original languageEnglish
Pages (from-to)337 - 358
JournalJournal of Business Economics (JBE) (früher: Zeitschrift für Betriebswirtschaft ZfB)
Volume87
Issue number3
DOIs
Publication statusPublished - 2017

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 502019 Marketing
  • 502052 Business administration
  • 502
  • 502020 Market research

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