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Predictive Data Mining Model for Electronic Customer Relationship Management Intelligence

Predictive Data Mining Model for Electronic Customer Relationship Management Intelligence

Bashar Shahir Ahmed, Mohamed Larabi Ben Maâti, Mohammed Al-Sarem
Copyright: © 2020 |Volume: 11 |Issue: 2 |Pages: 10
ISSN: 1947-3591|EISSN: 1947-3605|EISBN13: 9781799807162|DOI: 10.4018/IJBIR.2020070101
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MLA

Ahmed, Bashar Shahir, et al. "Predictive Data Mining Model for Electronic Customer Relationship Management Intelligence." IJBIR vol.11, no.2 2020: pp.1-10. http://doi.org/10.4018/IJBIR.2020070101

APA

Ahmed, B. S., Ben Maâti, M. L., & Al-Sarem, M. (2020). Predictive Data Mining Model for Electronic Customer Relationship Management Intelligence. International Journal of Business Intelligence Research (IJBIR), 11(2), 1-10. http://doi.org/10.4018/IJBIR.2020070101

Chicago

Ahmed, Bashar Shahir, Mohamed Larabi Ben Maâti, and Mohammed Al-Sarem. "Predictive Data Mining Model for Electronic Customer Relationship Management Intelligence," International Journal of Business Intelligence Research (IJBIR) 11, no.2: 1-10. http://doi.org/10.4018/IJBIR.2020070101

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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.