INTELLIGENTPERSONALIZATION APPROACHESFORCOMPLEX CUSTOMER BEHAVIOUR: AN OVERVIEW
Galal, M., Hassan, G., Aref, M. (2017). INTELLIGENTPERSONALIZATION APPROACHESFORCOMPLEX CUSTOMER BEHAVIOUR: AN OVERVIEW. EKB Journal Management System, 17(4), 45-57. doi: 10.21608/ijicis.2018.7927
Mohamed Galal; Ghada Hassan; Mostafa Aref. "INTELLIGENTPERSONALIZATION APPROACHESFORCOMPLEX CUSTOMER BEHAVIOUR: AN OVERVIEW". EKB Journal Management System, 17, 4, 2017, 45-57. doi: 10.21608/ijicis.2018.7927
Galal, M., Hassan, G., Aref, M. (2017). 'INTELLIGENTPERSONALIZATION APPROACHESFORCOMPLEX CUSTOMER BEHAVIOUR: AN OVERVIEW', EKB Journal Management System, 17(4), pp. 45-57. doi: 10.21608/ijicis.2018.7927
Galal, M., Hassan, G., Aref, M. INTELLIGENTPERSONALIZATION APPROACHESFORCOMPLEX CUSTOMER BEHAVIOUR: AN OVERVIEW. EKB Journal Management System, 2017; 17(4): 45-57. doi: 10.21608/ijicis.2018.7927

INTELLIGENTPERSONALIZATION APPROACHESFORCOMPLEX CUSTOMER BEHAVIOUR: AN OVERVIEW

International Journal of Intelligent Computing and Information Sciences
Article 4, Volume 17, Issue 4, October 2017, Page 45-57  XML PDF (542.72 K)
Document Type: Original Article
DOI: 10.21608/ijicis.2018.7927
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Authors
Mohamed Galalorcid 1; Ghada Hassan email 2; Mostafa Aref email orcid 3
1Commercial International Bank
2Computer Science department Ain Shams University and British University in Egypt
3Computer Science department Ain Shams University
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, the production of a tailored personalized
recommendation and the prediction of future needs are a challenging task. Within these institutions,
personalization that is more true to the customer needs leads to better targeted marketing campaigns
and enhances customer satisfaction with the ultimate aim of increasing the rates of customer retention,
and improving competitive advantage. Intelligent techniques and data mining algorithms have been
used to produce a more accurately tailored action or service to individual customers or segments of
customers. However, many limitations still exist in the CRM personalization lifecycle that undermine
the scope of personalized actions that follow; especially in evaluating of effectiveness of targeting,
ensuring the coverage of a large segment customers and the control on the decision making process.
Keywords
Personalization; Intelligent DSS; Dimensional Modeling; Clustering; Association Rules; User Profile; Group Profile; Object Profiling; Recommendation; Business Intelligence; Customer Churn; Data mining
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