Transactional data-based customer segmentation applying CRISP-DM methodology: A systematic review
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer International Publishing
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In recent years, as digital transformation picked up stream, the volume of customer transactional data that become available to companies has increased. By making use of such vast amount of transactional data and employing various data mining techniques, customer segmentation has received intensive attention from different industries, while significant research effort has been devoted to this topic, and the body of literature has begun to accumulate. In this context, the aim of this paper is to provide a comprehensive review of literature on transactional data-based customer segmentation to identify different characteristics in the field, analyze the application of data mining techniques, and highlight important points for further research. To review the existing literature in the field, three major online databases were used, and eventually, 84 relevant articles published in journals of well-known publishers are selected. The identified articles then completely analyzed based on the diverse criteria of the stages of CRISP-DM (CRoss Industry Standard Process for Data Mining) framework, and the results were reported. This systematic literature review can be very useful for academics and practitioners by providing a comprehensive overview of research work on customer segmentation using data mining and presenting guidelines for future research in this area as well. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.
Açıklama
Anahtar Kelimeler
Classification, Clustering, Customer segmentation, Data mining, Literature review
