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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 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.

Identiferoai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:5074
Date06 1900
CreatorsReutterer, Thomas, Hornik, Kurt, March, Nicolas, Gruber, Kathrin
PublisherSpringer
Source SetsWirtschaftsuniversität Wien
LanguageEnglish
Detected LanguageEnglish
TypeArticle, PeerReviewed
Formatapplication/pdf
RightsCreative Commons: Attribution 4.0 International (CC BY 4.0)
Relationhttp://dx.doi.org/10.1007/s11573-016-0823-7, http://link.springer.com/, http://epub.wu.ac.at/5074/

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