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Predictive Data Mining Model for Electronic Customer Relationship Management Intelligence

Author

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  • Bashar Shahir Ahmed

    (Computer Science and Systems Engineering Laboratory, University Abdelmalek Essaadi, Tetouan, Morocco)

  • Mohamed Larabi Ben Maâti

    (Computer Science and Systems Engineering Laboratory, University Abdelmalek Essaadi, Tetouan, Morocco)

  • Mohammed Al-Sarem

    (College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia)

Abstract

The rising adoption of e-CRM strategies in marketing and customer relationship management has necessitated to more needs especially where a specific customer segment is targeted and the services are personalized. This paper presents a distributed data mining model using access-control architecture in a bid to realize the needs for an online CRM that intends to deliver web content to a specific group of customers. This hybrid model utilizes the integration of the mobile agent and client server technologies that could easily be updated from the already existing web platforms. The model allows the management team to derive insights from the operations of the system since it focuses on e-personalization and web intelligence hence presenting a better approach for decision support among organizations. To achieve this, a software approach made of access-control functions, data mining algorithms, customer-profiling capability, dynamic web page creation, and a rule-based system is utilized.

Suggested Citation

  • Bashar Shahir Ahmed & Mohamed Larabi Ben Maâti & Mohammed Al-Sarem, 2020. "Predictive Data Mining Model for Electronic Customer Relationship Management Intelligence," International Journal of Business Intelligence Research (IJBIR), IGI Global, vol. 11(2), pages 1-10, July.
    • Handle: RePEc:igg:jbir00:v:11:y:2020:i:2:p:1-10
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