A product-centric data mining algorithm for targeted promotions

Moodley, Raymond
Chiclana, Francisco
Caraffini, Fabio
Carter, Jenny
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Peer reviewed
Targeted promotions in retail are becoming increasingly popular, particularly in the UK grocery retail sector where competition is stiff and consumers remain price sensitive. Given this, a targeted promotion algorithm is proposed to enhance the effectiveness of promotions by retailers. The algorithm leverages a mathematical model for optimizing items to target and fuzzy c-means clustering for finding the best customers to target. Tests using simulations with real life consumer scanner panel data from the UK grocery retailer sector shows that the algorithm performs well in finding the best items and customers to target whilst eliminating "false positives" (targeting customers who do not buy a product) and reducing "false negatives" (not targeting customers who could buy). The algorithm also shows better performance when compared to a similar published framework, particularly in handling "false positives" and "false negatives". The paper concludes by discussing managerial and research implications, and highlights applications of the model to other fields.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Association rule mining, targeted marketing, clustering
Moodley, R., Chiclana, F., Caraffini, F. and Carter, J. (2019) A product-centric data mining algorithm for targeted promotions. Journal of Retailing and Consumer Services, 101940
Research Institute
Institute of Artificial Intelligence (IAI)