الفهرس 
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Abstract
Intelligent techniques have been used in the various business domains to improve anal ysis process, 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 Customer Relationship Management (CRM) performance. However, the expanding of customer base, the diversity of products, the complex behavior of customer groups lead to facing big challenges in producing tailored actions to customer needs.
After applying different techniques to enhance the concept of personalization in the financial institutions, it was found that there are still shortages in customer satisfaction level for reaching the tailored action that satisfies customer needs. Also there are difficulties in applying the personalization approaches in the institutions with a large number of customers using the traditional approaches. Applying the personalization using similarity-based approach from a prospective of customer profile or customer behavior has a limited scope of actions. The limitation of developing a single framework to serve specific customer dimension is considered a very limited approach in frame- work reusability.
In this work, we developed a multi dimension personalization framework (MDPF) architecture to improve personalized targeting. The framework presented improves on the automation of existing systems by using supervised and unsupervised data mining techniques, and enhances the level of targeting by considering more effective dimen- sions in multiple stages of the framework.
This framework overcomes the shortage of old approaches in targeting the customers’ needs. The developed framework is a hybrid approach combines the data-driven (Knowledge Discovery) approaches with the business-driven (Top-Down) approaches to generate a new business competitive opportunities through a complete understandable and profiled customer segments. The framework has been built based on the concept of framework dimensionality which leads to an enhancement in the project agility. Enhancing the agility process leads to adapting to any new changes in the framework quickly without rebuilding the frame- work from scratch.