A product-centric data mining algorithm for targeted promotions
Fabio Caraffini and
Journal of Retailing and Consumer Services, 2020, vol. 54, issue C
Targeted promotions in retail are becoming increasingly popular, particularly in 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 optimising 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 show that the algorithm performs well in finding the best items and customers to target whilst eliminating â€œfalse positivesâ€ (targeting customers who do not buy a product) and reducing â€œfalse negativesâ€ (not targeting customers who could buy a product). The algorithm also shows better performance when compared to a similar published framework, particularly in handling â€œfalse positivesâ€ and â€œfalse negativesâ€ . The paper concludes by discussing managerial and research implications, and highlights applications of the model to other fields.
Keywords: Association rule mining; Targeted marketing; Clustering (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:54:y:2020:i:c:s096969891831169x
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