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Research Paper | Computer Science and Information Technology | United States of America | Volume 13 Issue 3, March 2024 | Popularity: 5.5 / 10

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Enhancing Customer Relationship Management in ERP Systems through AI: Personalized Interactions, Predictive Modeling, and Service Automation

Jayesh Jhurani

Abstract: This research paper delves into the integration of Artificial Intelligence (AI) in Customer Relationship Management (CRM) systems within Enterprise Resource Planning (ERP) frameworks, with a focus on personalized interactions, predictive modeling, and service automation. The primary objective was to empirically investigate how AI enhancements in CRM functionalities can contribute to more efficient and effective customer relationship management within ERP systems. A quantitative research design was adopted, utilizing a structured questionnaire distributed among CRM managers and IT professionals across various industries, with the data analyzed using the Statistical Package for the Social Sciences (SPSS). Key findings from the study reveal significant positive impacts of AI integration on CRM functionalities, notably in personalized customer interactions, predictive modeling accuracy, and service automation efficiency. These enhancements were found to contribute to improved customer satisfaction and operational efficiency, suggesting that AI - driven CRM strategies within ERP systems offer substantial benefits to organizations. The research fills a critical gap in existing literature by providing empirical evidence of the specific benefits of AI in CRM and underscores the strategic value of AI integration in enhancing CRM functionalities. The broader implications of this study highlight the transformative potential of AI in CRM, encouraging organizations to leverage AI technologies to gain a competitive edge in customer relationship management.

Keywords: Artificial Intelligence, Customer Relationship Management, Enterprise Resource Planning, Predictive Modeling, Service Automation, Personalized Interactions

Edition: Volume 13 Issue 3, March 2024

Pages: 892 - 897

DOI: https://www.doi.org/10.21275/SR24313021820

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