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Optimized Feature Extraction and Actionable Knowledge Discovery for Customer Relationship Management
In today's dynamic marketplace, telecommunication organizations, both private and public, are increasingly leaving antiquated marketing philosophies and strategies to the adoption of more customer-driven initiatives that seek to understand, attract, retain and build intimate long term relationship with profitable customers . This paradigm shift has undauntedly led to the growing interest in Customer Relationship Management (CRM) initiatives that aim at ensuring customer identification and interactions. The urgent market requirement is to identify automated methods that can assist businesses in the complex task of predicting customer churning.
The immediate requirement of the market is to have systems that can perform accurate
(i) identification of loyal customers (so that companies can offer more services to retain them).
(ii) prediction of churners to ensure that only the customers who are planning to switch their service providers are being targeted for retention.
Keywords
- Berson, A., Smith, S. and Thearling, K. (2000, 1999) Building data mining applications for CRM, New York, McGraw-Hill.
- Chu, B., Tsai, M. and Ho, C. (2007) Toward a hybrid data mining model for customer retention, Knowledge-Based Systems, Vol. 20, No. 8, Pp. 703-718.
- Ester, M., Kriegel, H-P., Sander, J. and Xu, X. (1996) A density-based algorithm for discovering clusters in large spatial databases with noise, Proceedings of the 2nd ACM SIGKDD, Portland, Oregon, Pp. 226-231
- El-Sonbaty, Y., Ismail, M.A. and arouk, M. (2004) An Efficient Density Based Clustering Algorithm for Large Databases, 16th IEEE International Conference on Tools with Artificial Intelligence, Pp. 673-677.
- Han, J. and Kamber, M. (2000) Data Mining: Concepts and Techniques, The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann.
- Han, J., Cai, Y., Cercone, N. (1993) Data driven discovery of quantitative rules in relational databases, IEEE Trans. Knowledge and Data Engineering, Vol. 5, Pp. 29-40.
- http://en.wikipedia.org/wiki/Customer_attrition, Last Accessed during March, 2014.
- Jain, A.K. and Dubes, R.C. (1988) Algorithms for Clustering Data, Prentice-Hall.
- Kaski, S., Kangas, J. and Kohonen, T. (1998) Bibliography of Self- Organizing Map (SOM) Papers 1981-1997, Neural Computing Surveys, Vol. 1, Pp. 102-350.
- Kentrias, S. (2001) Customer relationship management: The SAS perspective, www.cm2day.com, Last Accessed during September 2013.
- Kohonen, T., Hynninen, J., Kangas, J. and Laaksonen, V. (1996) SOM PAK: The SelfOrganizing Map program package, Helsinki University of Technology, Finland, Report A31, Pp. 1-27.
- Oja, M., Kaski, S. and Kohonen, T. (2002) Bibliography of Self-Organizing Map (SOM) Papers: 1998-2001 Addendum, Neural Computing Surveys, Vol. 1, Pp. 1-176.
- Wu, R., Peters, W. and Morgan, M.W. (2002) The Next Generation Clinical Decision Support: Linking Evidence to Best Practice, Journal of Healthcare Information Management, Vol. 16, No.4, Pp. 50-55.
- Yu, C. and Ying, X. (2009) Application of Data Mining Technology in E-Commerce, International Forum on Computer Science-Technology and Applications, Vol. 1, Pp. 291-293.
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