Research Article
Segmenting the Banking Market Strategy by Clustering
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 45 - Issue 17 |
| Published: May 2012 |
| Authors: Varun Kumar. M, Vishnu Chaitanya. M, Madhavan. M |
10.5120/7000-9473
|
Varun Kumar. M, Vishnu Chaitanya. M, Madhavan. M . Segmenting the Banking Market Strategy by Clustering. International Journal of Computer Applications. 45, 17 (May 2012), 10-15. DOI=10.5120/7000-9473
@article{ 10.5120/7000-9473,
author = { Varun Kumar. M,Vishnu Chaitanya. M,Madhavan. M },
title = { Segmenting the Banking Market Strategy by Clustering },
journal = { International Journal of Computer Applications },
year = { 2012 },
volume = { 45 },
number = { 17 },
pages = { 10-15 },
doi = { 10.5120/7000-9473 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2012
%A Varun Kumar. M
%A Vishnu Chaitanya. M
%A Madhavan. M
%T Segmenting the Banking Market Strategy by Clustering%T
%J International Journal of Computer Applications
%V 45
%N 17
%P 10-15
%R 10.5120/7000-9473
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Customer segmentation is the practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing such as age, gender, interests, spending habits, and so on. One of the easiest definitions is "a group of customers with shared needs". From this definition, it's clear what we need to identify customers with shared needs. The customer segmentation consists of two phases. First phase includes K-Means clustering, where the customers are clustered according to their RFM (Recency Frequency Monetary). In the Second phase, with demographic data, each cluster is again partitioned into new clusters. Finally LTV (Life Time Value of the customers) are used to generate customer's profile.
References
- Waminee Niyagas, Anongnart Srivihok, and sukumal Kitisin "Clustering e-Banking Customer using Data Mining and Marketing Segmentation" The dataset of this study is Internet Banking customer data from one commercial bank in Thailand between January 1st and December 11th of the year 2005.
- Sang Chul Lee, Yung Ho Suh, Jae Kyeong Kim, Kyoung Jun Lee "A cross-national market segmentation of online game industry using SOM" in Elsevier Expert Systems with Applications 27 (2004) 559 – 570.
- Markus Schedl, Elias Pampalk, and Gerhard Widmer 2004 "Intelligent Structuring and Exploration of Digital Music Collection" in Austrian Research Institute for Artificial Intelligence.
- Nan-Chen Hsieh, Kuo-Chung Chu 2009 "Enhancing Consumer Behavior Analysis by Data Mining Techniques" in International journal of information management and sciences.
- K. V. Nagendra, C. Rajendra 2012 "Customer Behavior Analysis using CBA (Data Mining Approach)" in National Conference on Research trends in Computer Science and Technology.
- Derya Birant, Dokuz Eylul University, Turkey "Data Mining Using RFM Analysis" in an article Knowledge oriented applications in Data Mining.
- E. W. T. Ngai, Li Xiu, D. C. K. Chau, 2009 "Application of data mining techniques in customer relationship management: A literature review and classi?cation" in ELSEVIER.
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