INTELLIGENTPERSONALIZATION APPROACHESFORCOMPLEX CUSTOMER BEHAVIOUR: AN OVERVIEW | ||
| International Journal of Intelligent Computing and Information Sciences | ||
| Article 4, Volume 17, Issue 4, October 2017, Pages 45-57 PDF (542.72 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijicis.2018.7927 | ||
| Authors | ||
| Mohamed Galal1; Ghada Hassan* 2; Mostafa Aref* 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 | ||
|
Statistics Article View: 307 PDF Download: 504 |
||
