Data Quality in Customer Relationship Management: Challenges, Solutions, and Best Practices

Abstract

Customer Relationship Management systems have become essential tools for organizations seeking to understand, engage, and retain customers in increasingly competitive markets. However, the effectiveness of CRM systems depends fundamentally on the quality of the data they contain. Poor data quality in CRM systems leads to flawed customer insights, ineffective marketing campaigns, wasted resources, and damaged customer relationships. This review examines data quality challenges in CRM, analyzing the dimensions of quality that are most critical for CRM success, including accuracy, completeness, consistency, timeliness, and relevance. We review evidence on the prevalence and consequences of CRM data quality problems, drawing on studies from diverse industries and organizational contexts. We analyze the causes of quality problems, including data entry errors, system integration challenges, data decay, and organizational factors. We examine solutions that have been implemented, including data governance frameworks, validation processes, data cleaning approaches, and continuous monitoring. We propose a comprehensive framework for CRM data quality management that addresses technical, organizational, and strategic dimensions.