%0 Journal Article
%T Marketing strategies evaluation based on big data analysis: a CLUSTERING-MCDM approach
%A AliAsghar Abbasi Kamardi
%A Aliakbar Kazeminia
%A Edmundas Kazimieras Zavadskas
%A Hannan Amoozad Mahdiraji
%J Economic Research-Ekonomska Istra£¿ivanja
%D 2019
%R https://doi.org/10.1080/1331677X.2019.1658534
%X Abstract 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, silver- low 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
%U https://www.tandfonline.com/doi/full/10.1080/1331677X.2019.1658534