Customer Clustering Based on Customer Lifetime Value: A Case Study of an Iranian Bank
Customer lifetime value (CLV) as a quantifiable parameter plays an important role in customer clustering. Clustering based on CLV helps organizations to form distinct customer groups, reveal buying patterns, and create longterm relationships with their customers. Our research aims at the synthesis o...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Iran Telecom Research Center
2015-06-01
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| Series: | International Journal of Information and Communication Technology Research |
| Subjects: | |
| Online Access: | http://ijict.itrc.ac.ir/article-1-103-en.html |
| _version_ | 1811169327002419200 |
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| author | Arezoo Nekooei Mohammad Jafar Tarokh |
| author_facet | Arezoo Nekooei Mohammad Jafar Tarokh |
| author_sort | Arezoo Nekooei |
| collection | DOAJ |
| description | Customer lifetime value (CLV) as a quantifiable parameter plays an important role in customer clustering. Clustering based on CLV helps organizations to form distinct customer groups, reveal buying patterns, and create longterm relationships with their customers. Our research aims at the synthesis of a CLV model and a clustering algorithm in a new comprehensive framework. First, a model for calculation of CLV is suggested, which is called Group LRFM or GLRFM briefly. In this model, four parameters, Length, Recency, Frequency, and Monetary, are determined according to the products/services used by customers. Then, a novel framework based upon the model is presented in eight steps for customer clustering. In traditional methods, the customers of valuable cluster are treated the same. But in proposed framework, company can design different and proper strategies for each cluster based on the use of products/services. The experimental results in banking industry verify that proposed approach allows an accurate and efficient cluster analysis; it provides appropriate information to create clear sales and marketing policies for three identified segments. |
| first_indexed | 2024-04-10T16:40:34Z |
| format | Article |
| id | doaj.art-eb2372bb3c464cbfbdbb70c0f50b8c9f |
| institution | Directory Open Access Journal |
| issn | 2251-6107 2783-4425 |
| language | English |
| last_indexed | 2024-04-10T16:40:34Z |
| publishDate | 2015-06-01 |
| publisher | Iran Telecom Research Center |
| record_format | Article |
| series | International Journal of Information and Communication Technology Research |
| spelling | doaj.art-eb2372bb3c464cbfbdbb70c0f50b8c9f2023-02-08T07:54:44ZengIran Telecom Research CenterInternational Journal of Information and Communication Technology Research2251-61072783-44252015-06-01727190Customer Clustering Based on Customer Lifetime Value: A Case Study of an Iranian BankArezoo Nekooei0Mohammad Jafar Tarokh1 Customer lifetime value (CLV) as a quantifiable parameter plays an important role in customer clustering. Clustering based on CLV helps organizations to form distinct customer groups, reveal buying patterns, and create longterm relationships with their customers. Our research aims at the synthesis of a CLV model and a clustering algorithm in a new comprehensive framework. First, a model for calculation of CLV is suggested, which is called Group LRFM or GLRFM briefly. In this model, four parameters, Length, Recency, Frequency, and Monetary, are determined according to the products/services used by customers. Then, a novel framework based upon the model is presented in eight steps for customer clustering. In traditional methods, the customers of valuable cluster are treated the same. But in proposed framework, company can design different and proper strategies for each cluster based on the use of products/services. The experimental results in banking industry verify that proposed approach allows an accurate and efficient cluster analysis; it provides appropriate information to create clear sales and marketing policies for three identified segments.http://ijict.itrc.ac.ir/article-1-103-en.htmlclusteringdata miningcustomer relationship management (crm)customer lifetime value (clv) |
| spellingShingle | Arezoo Nekooei Mohammad Jafar Tarokh Customer Clustering Based on Customer Lifetime Value: A Case Study of an Iranian Bank International Journal of Information and Communication Technology Research clustering data mining customer relationship management (crm) customer lifetime value (clv) |
| title | Customer Clustering Based on Customer Lifetime Value: A Case Study of an Iranian Bank |
| title_full | Customer Clustering Based on Customer Lifetime Value: A Case Study of an Iranian Bank |
| title_fullStr | Customer Clustering Based on Customer Lifetime Value: A Case Study of an Iranian Bank |
| title_full_unstemmed | Customer Clustering Based on Customer Lifetime Value: A Case Study of an Iranian Bank |
| title_short | Customer Clustering Based on Customer Lifetime Value: A Case Study of an Iranian Bank |
| title_sort | customer clustering based on customer lifetime value a case study of an iranian bank |
| topic | clustering data mining customer relationship management (crm) customer lifetime value (clv) |
| url | http://ijict.itrc.ac.ir/article-1-103-en.html |
| work_keys_str_mv | AT arezoonekooei customerclusteringbasedoncustomerlifetimevalueacasestudyofaniranianbank AT mohammadjafartarokh customerclusteringbasedoncustomerlifetimevalueacasestudyofaniranianbank |