Machine Learning Classification of High-Value Customers in Multichannel Retailing
(A2024-119333)

AUTHORS

Jorge Brantes Ferreira, Pontifical Catholic University of Rio de Janeiro; Alice Linhares Amigo, Pontifical Catholic University of Rio de Janeiro; Jorge Ferreira da Silva, Pontifical Catholic University of Rio de Janeiro

ABSTRACT

In the hyper-competitive retailing industry, it becomes paramount for companies to comprehend their customers, deliver value to them, and maximize customer value for the business. Thus, identifying high-value customers is of vital importance for firms. Leveraging a dataset comprising real-world data from over 150,000 customers from a prominent retail chain within the supermarket sector, this study aimed to classify high-value customers by employing machine learning models. The results underscore the significance of multichannel behavior in defining high-value customers. The insights gleaned from this investigation hold relevance not only for scholars delving into customer value and multichannel-related topics but also for practitioners, especially those engaged with formulating customer relationship strategies within the retail domain.