Marketing strategies evaluation based on big data analysis: a CLUSTERING-MCDM approach

Hannan Amoozad Mahdiraji, Edmundas Kazimieras Zavadskas, Aliakbar Kazeminia, Ali Asghar Abbasi Kamardi

Research output: Contribution to journalArticlepeer-review

53 Citations (Scopus)
167 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)2882-2898
Number of pages17
JournalEconomic Research-Ekonomska Istrazivanja
Issue number1
Early online date3 Sept 2019
Publication statusPublished - 2019
Externally publishedYes

Bibliographical note

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


  • BWM
  • clustering
  • Data mining
  • RFM

ASJC Scopus subject areas

  • Economics and Econometrics


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