The Churn Dilemma: Why Traditional CRM Fails and How AI Can Fix It

Victor Obioma Umozurike ()

American Journal of Data, Information and Knowledge Management, 2025, vol. 6, issue 1, 15-22

Abstract: Purpose: The purpose of this paper is to analyze the limitations of traditional Customer Relationship Management (CRM) systems in their attempts to reduce customer churn and propose that Artificial Intelligence (AI) is a revolutionary solution. Customer churn, especially in the retail industry, lowers profit margins and erodes long-term customer value. Traditional CRMs often lack predictive insights and cannot act in real-time. This article demonstrates how AI-powered CRM systems, with machine learning and predictive analytics, provide anticipatory and personalized approaches to customer engagement that dramatically reduce churn. Materials and Methods: A mixed-method research design was used for the study. The research integrates insights derived from empirical research, industry reports, and case studies. Quantitative churn rates were obtained from corporate data dashboards, whereas qualitative inputs were obtained in interviews with CRM managers in retail companies. Findings: The research showed that traditional Customer Relationship Management systems mainly fail due to data silos, reactive processes, and a lack of personalization. In contrast, artificial intelligence-based systems leverage multichannel data, enable real-time churn prediction, and personalize interventions to match individual behaviors. Case studies of leading international retailers support these claims. Unique Contribution to Theory, Practice and Policy: Theoretically, this research contributes to the extension of CRM evolution under digital transformation. Practically, it calls on retail leaders to embrace AI-integrated systems for business competitiveness. For policymakers, the study points towards the call for ethical AI standards in the use of consumer data.

Keywords: Churn; Customer Retention; Artificial Intelligence; CRM; Retail (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ajpojournals.org/journals/index.php/ajdikm/article/view/2710 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bfy:ajdikm:v:6:y:2025:i:1:p:15-22:id:2710

Access Statistics for this article

More articles in American Journal of Data, Information and Knowledge Management from AJPO
Bibliographic data for series maintained by Chief Editor ().