Mohamed Galal
Ghada Hassan
ABSTRACT
Intelligent techniques have been used in the marketing and sales sectors of business to improve analysis, increase revenues and save time. In customer-centric institutions, one of the areas in which intelligent techniques and data mining algorithms have been used is the personalization for enhanced CRM (Customer Relationship Management) performance. However, with a growing number of customers, the diversity of products on offer, the complex behavior of customer groups and the continuous change of personalization parameters, producing a tailored personalized recommendation that predicts their future needs is a challenging task.
In this paper, we propose multi dimension personalization framework architecture to improve personalized targeting. The framework presented improves on the automation of existing systems by using multiple supervised and unsupervised data mining techniques, and enhances the level of targeting by considering more effective dimensions in multiple stages of the framework. A theoretical case study explaining the practical working and perceived advantages of the new framework is presented.
- G. Mansingh, L. Rao, K. M. O. Bryson, and A. Mills. Profiling internet banking users: A knowledge discovery in data mining process model based approach, Information Systems Frontiers, Volume 17, Issue 1, pp 193--215, Springer US, February 2015. Google Scholar
Digital Library
- M.A.H. Farquad, V. Ravi, R.S. Bapi. Churn prediction using comprehensible support vector machine: An analytical CRM application. Appl. Soft Computing, Volume 19, June 2014.Google ScholarCross Ref
- M. Gorgoglione and U. Panniello. Beyond Customer Churn: Generating Personalized Actions to Retain Customers in a Retail Bank by a Recommender System Approach, Journal of Intelligent Learning Systems and Applications, Vol. 3 No. 2, 2011.Google ScholarCross Ref
- H. Tang, S. S. Liao, and S. X. Sun. A prediction framework based on contextual data to support Mobile Personalized Marketing, Decision Support Systems, Elsevier B.V, Volume 56, December 2013.Google Scholar
- X. Yang, J. Chen, P. Hao, and Y. Wang. Application of Clustering for Customer Segmentation in Private Banking, in: Seventh International Conference on Digital Image Processing, ICDIP 2015.Google Scholar
- F. Hamka, H. Bouwman, M. D. Reuver, and M. Kroesen. Mobile customer segmentation based on smartphone measurement, Telematics and Informatics, Volume 31, Issue 2, May 2014. Google ScholarDigital Library
- R. G. Javalgi, J. J. B. Jr., V. K. Prasad, and S. R. Rao. A Life Cycle Segmentation Approach to Marketing Financial Products and Services: Proceedings of the 1988 International Conference of Services Marketing, Springer International Publishing, Pages 11--22, 2015.Google Scholar
- A. H. Azadnia, P. Ghadimi, and M. M. Aghdam. A Hybrid Model of Data Mining and MCDM Methods for Estimating Customer Lifetime Value, in: Proceedings of the 41st International Conference on Computers & Industrial Engineering, Los Angeles, CA, USA, 23-25 July 2012.Google Scholar
- K. K. Tsiptsis and A. Chorianopoulos. Data Mining Techniques in CRM: Inside Customer Segmentation, WILEY, ISBN-13: 978-0470743973, 2009. Google ScholarDigital Library
- Z. Tabaei and M. Fathian. Using Customer Lifetime Value Model for Product Recommendation: An Electronic Retailing Case Study, IJEEEE 2012 Vol.2(1), pp. 077--082, Feb. 2012.Google Scholar
- A. J. Cuadros and V.E. Domínguez. Customer segmentation model based on value generation for marketing strategies formulation, Estudios Gerenciales, Volume 30, Issue 130, January-March 2014.Google ScholarCross Ref
Recommendations
Empowering CRM Through Business Intelligence Applications: A Study in the Telecommunications Sector
The application of business intelligence BI techniques for knowledge discovery and decision support empowers organizations in different functions. This article examines the impacts of BI on customer relationship management CRM functions marketing, sales ...
Personalized recommendation based on the personal innovator degree
RecSys '09: Proceedings of the third ACM conference on Recommender systemsThis paper proposes a novel Collaborative Filtering scheme; it focuses on the dynamics and precedence of user preference to recommend items that match the latest preference of the target user. In predicting which items this user will purchase in the ...
Extraction of customer potential value using unpurchased items and in-store movements
KES'11: Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IIIRFID data obtained from customers' movements using radiofrequency identification (RFID) tags contain valuable information for marketing, such as shopping trip time and distance as well as the number of shelf visits. Customers' purchasing behavior and ...
Comments