Improving CRM Efficiency Through Data Cleansing, Validation, And Batch Workflow Optimization

Authors: Linda Martinez, Karen White, James Parker, Victoria King, Chaitanya Srinivas, Akhilesh Achari

Abstract: Customer Relationship Management (CRM) platforms are essential for managing customer interactions, sales activities, marketing operations, and service processes within modern enterprises. However, the effectiveness of these systems is often hindered by poor data quality, duplicate records, inconsistent information, and inefficient batch processing workflows that reduce operational efficiency and decision-making accuracy. This research examines methods for improving CRM efficiency through comprehensive data cleansing, validation, and batch workflow optimization. The study focuses on identifying and eliminating redundant, inaccurate, and incomplete customer records while implementing robust validation mechanisms to ensure data integrity, consistency, and compliance with organizational standards. Additionally, it explores advanced batch processing strategies, including workflow automation, intelligent scheduling, resource optimization, and performance monitoring, to enhance processing speed and system scalability. By integrating effective data hygiene practices with optimized batch execution frameworks, organizations can improve customer data reliability, minimize processing errors, accelerate business operations, and strengthen overall CRM performance. The research highlights how a structured approach to data quality management and workflow optimization enables enterprises to achieve greater operational efficiency, enhanced customer engagement, reduced maintenance costs, and more informed business decision-making, ultimately maximizing the strategic value of CRM platforms in a rapidly evolving digital business environment.

DOI: http://doi.org/10.5281/zenodo.20819081