Published Paper PDF: https://ijrmeet.org/wp-content/uploads/2019/06/IJRMEET0619160029_Data-Centric%20AI%20in%20Enterprise%20CRM-%20Optimizing%20Model%20Performance.pdf
Srikanth Balla
Christian Brothers University
Memphis, TN, USA
Abstract
The increasing use of Artificial Intelligence (AI) in Enterprise Customer Relationship Management (CRM) systems has highlighted the significance of data-driven approaches that guarantee the quality of customer data to maximize AI model performance. Previous research in CRM analytics has largely centered on algorithmic development, whereas the significant position of data quality in choosing AI outputs has not received much attention. This has thus led to suboptimal model accuracy, reduced customer insights, and poorer decision-making in organizations. This study puts things right by exploring the significance of thoroughgoing customer data quality audits as a foundational building block for maximizing AI-aided CRM models. Through intensive auditing of customer data, which analyzes completeness, accuracy, consistency, and timeliness, organizations are in a better position to identify and correct data shortcomings that compromise model effectiveness. Through qualitative judgments and quantitative evaluations of enterprise CRM datasets, this study presents evidence of how intentional enhancements in data quality correlate directly with predictive accuracy, customer segmentation, and the efficacy of personalization in AI models. The conclusions are that the inclusion of the application of continuous data quality audits in CRM processes not only steers clear of the dangers of biased or inaccurate AI outputs but also enhances customer engagement and operational efficiency. This study presents a new framework for implementing data quality audits in enterprise CRM AI processes, as well as actionable recommendations for professionals who wish to make use of data-driven AI approaches. Lastly, the study emphasizes that the integrity of customer data is key to achieving the potential of AI in CRM systems, thus making organizations capable of delivering more accurate, personalized, and profitable customer experiences.
Keywords
Data-driven AI, Customer Relationship Management, CRM, Customer data quality, Data quality audits, AI model optimization, Enterprise AI, Predictive analytics, Customer segmentation, Personalization, Data integrity, Model performance.
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