Dr. Kayode Lanre Bello, Mr. Ignatius Kwamina Baidoo, Mr. Murugappaan Umapathi
This research examines the integration of Artificial Intelligence (AI) technologies within Customer Relationship Management (CRM) systems and their impact on customer retention outcomes. Through a comprehensive review of recent empirical studies and industry data, this paper identifies five primary mechanisms through which AI-enabled CRM systems enhance customer retention: personalized customer experience through behavioral analytics, real-time decision-making via predictive models, enhanced service efficiency with AI chatbots and automation, improved customer segmentation and targeting, and proactive churn management strategies. Analysis of contemporary research reveals that organizations implementing AI-driven CRM solutions experience significant improvements in retention metrics, with studies documenting retention increases ranging from 15% to 70% depending on implementation sophistication and industry context. The findings demonstrate that AI-CRM integration produces measurable positive effects on customer satisfaction scores, customer lifetime value, and overall business profitability. However, the research also identifies critical implementation challenges including data privacy concerns, integration complexities with legacy systems, high implementation costs, and shortage of skilled personnel. This paper contributes to the growing body of knowledge on AI-CRM effectiveness by synthesizing empirical evidence across multiple industries and providing insights for practitioners seeking to leverage AI technologies for enhanced customer retention.
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10.62226/ijarst20262615
PAGES : 1805-1814 | 6 VIEWS | 3 DOWNLOADS
Dr. Kayode Lanre Bello, Mr. Ignatius Kwamina Baidoo, Mr. Murugappaan Umapathi | AI-Based Customer Relationship Management And Its Effect On Customer Retention | DOI : 10.62226/ijarst20262615
| Journal Frequency: | ISSN 2320-1126, Monthly | |
| Paper Submission: | Throughout the month | |
| Acceptance Notification: | Within 6 days | |
| Subject Areas: | Engineering, Science & Technology | |
| Publishing Model: | Open Access | |
| Publication Fee: | USD 60 USD 50 | |
| Publication Impact Factor: | 6.76 | |
| Certificate Delivery: | Digital |