Izvorni znanstveni članak
https://doi.org/10.1080/1331677X.2019.1658534
Marketing strategies evaluation based on big data analysis: a CLUSTERING-MCDM approach
Hannan Amoozad Mahdiraji
; Faculty of Management, University of Tehran, Tehran, Iran
Edmundas Kazimieras Zavadskas
; Vilnius Gediminas Technikos University, Vilnius, Lithuania
Aliakbar Kazeminia
; Kish International Campus, University of Tehran, Kish, Iran
AliAsghar Abbasi Kamardi
; Faculty of Management, University of Tehran, Tehran, Iran
Sažetak
Nowadays, a huge amount of data is generated due to rapid Information and Communication Technology development. In this paper, a digital banking strategy has been suggested applying these big data for Iranian banking industry. This strategy would guide Iranian banks to analyse and distinguish customers’ needs to offer services proportionate to their manner. In this research, the balances of more than 2,600,000 accounts over 400 weeks are computed in a bank. These accounts are clustered based on justified RFM parameters containing maximum balances, the most number of maximum balances and the last week number with the maximum balance using k-means method. Subsequently, the clusters are prioritised employing Best Worst Method- COmplex PRoportional ASsessment methods considering the diverse inner value of each cluster. The accounts are classified into six clusters. The experts named the clusters as special, loyal, silver- high interaction, silverlow interaction, bronze, averted- low interaction. silver-low interaction cluster and loyal cluster are picked in order by experts and BWM-COPRAS as the most influential clusters and the digital banking strategy is developed for them. RFM parameters are modelled for customers’ accounts singly. The aggregation of the separate accounts of a customer should be considered.
Ključne riječi
Data mining; clustering; BWM; COPRAS; RFM
Hrčak ID:
229618
URI
Datum izdavanja:
22.1.2019.
Posjeta: 4.177 *