Open Access
Subscription or Fee Access
Customer Segmentation for Enhancing Customer Centric Business
Abstract
Customer segmentation is grouping customers based on similarities so companies can approach each group for marketing effectively and appropriately. Customer segmentation is important for enhancing customer centric business for gaining retention among customers by campaigning them using better marketing strategies, causing more institutional benefit. This paper segments customers by analyzing their characteristics based on both demographic and behavioral attributes using K-Means clustering algorithm and Self-Organizing Maps (SOM). K-Means is a partitioning clustering algorithm which uses centroid based approach to provide promising clustering results with Davies Bouldin score of - 1.632 in 16 seconds. Self-Organizing Maps (SOM) provides easy visualization by reducing dimensionality. This paper combines both algorithmic features by re-clustering the cluster formed from SOM and K-Means algorithm. Experimentation in Rapid Miner and python shows improved results with more number of clusters. The Davies Bouldin score is estimated to be-1.702 and the execution time is 4 seconds.
Keywords
References
Zaki Mohammed J., and Wagner Meira Jr. Data Mining and Analysis: Fundamental Concepts and Algorithms. Kindle Edition. New York: Cambridge University Press; 2014.
Hamadeh M.W. and Abdallah S. Discover Trending Topics of Interest to Governments. International Conference on Advanced Intelligent Systems and Informatics; 31 Aug 2017; Cham: Springer; 2017. 366-373p.
Morteza Namvar, Mohammad R. Gholamian, et al. A two phase clustering method for intelligent customer segmentation. Intelligent Systems, Modelling and Simulation (ISMS); 2010 Jan 27-29; Liverpool, UK, Nwe York: IEEE; 2010. 215-219.
Singh I. and Singh S. Framework for targeting high value customers and potential churn customers in telecom using big data analytics. International Journal of Education and Management Engineering. 2017;7(1):36-45.
Khajvand M. and Tarokh M. J. Estimating customer future value of different customer segments based on adapted RFM model in retail banking context. Procedia Computer Science. 2011;3:1327-1332.
Zadeh R. B. K., Faraahi A. and Mastali A. Profiling bank customers behavior using cluster analysis for profitability. International Conference on Industrial Engineering and Operations Management. 2011 Jan 22-24; Kuala Lumpur, Malaysia; 2011.
Ahuja Vandana and Medury Yajulu. Corporate blogs as tools for consumer segmentation-using cluster analysis for consumer profiling. Journal of Targeting, Measurement and Analysis for Marketing. 2011;19(3):173-182.
Mbarki J. and Jaara E. M. Deployment of Partitioning Around Medoids Clustering Algorithm on a Set of Objects Derived from Analytical CRM Data. esearch Journal of Applied Sciences, Engineering and Technology. 2014;7(4):786-790.
Lincy M., and A. Meena Kowshalya. Leveraging Feature Selection Algorithms for Early Detection of Type-2 Diabetes. Journal of Computer Technology & Applications. 2020;11(1):13-20.
Hassouna M., Tarhini A., et al. Customer Churn in Mobile Markets A Comparison of Techniques. Computers and Society. 2016;8(6):224-237.
Kohonen T. The self-organizing map. Journals & Magazines. 1990;78(9):1464-1480.
Steinbach M., Ertöz L., et al. New Directions in Statistical Physics: The challenges of clustering high dimensional data. Germany: Springer Berlin Heidelberg; 2004. 273-309p.
Goonetilleke T. O. and Caldera H. A. Mining Life Insurance Data for Customer Attrition Analysis. Journal of Industrial and Intelligent Information. 2013;1(1):52-58.
P. van der Putten and M. van Someren (eds). CoIL Challenge 2000: The Insurance Company Case. Advanced Computer Science Technical. Netherlands: Sentient Machine Research; 2000.
Kowshalya A. Meena, R. Madhumathi, and N. Gopika. Correlation Based Feature Selection Algorithms for Varying Datasets of Different Dimensionality. Wireless Personal Communications. 2019;108(3)1977-1993.
Gopika N., and A. Meena Kowshalaya ME. Correlation based feature selection algorithm for machine learning. 3rd International Conference on Communication and Electronics Systems (ICCES); 2018 Oct 15-16; Coimbatore, India, New york: IEEE; 2019. 692-695p.
A. Meena Kowshalya, M. Lincy, R. Suvarna. Review of Feature Selection Methods and Semi Supervised Feature Selection Algorithms for Classification. International Journal of Software Computing and Testing. 2020;639-51.
Refbacks
- There are currently no refbacks.
This site has been shifted to https://stmcomputers.stmjournals.com/