The telecom sector now prioritizes real-time data accuracy because of increased consumer demand for top-notch customer experiences. The study investigates the connection between real-time data accuracy and customer satisfaction in telecom services. Accurate real-time information becomes essential for shaping customer experiences throughout service delivery and support functions as businesses increasingly depend on data-driven decision-making. This research demonstrates the connection between data inconsistencies and problems that include billing errors along with service interruptions which result in customer dissatisfaction. The study employed a mixed-method approach with qualitative interviews and quantitative surveys among telecom customers and service providers to examine the relationship between data accuracy and customer satisfaction. Real-time data accuracy builds customer trust while reducing resolution time for service problems and increasing customer loyalty. The research emphasizes that inaccurate data erodes customer trust leading to service churn which negatively impacts telecom companies' reputation. The research explores how technology such as AI and machine learning helps maintain real-time data accuracy while automation presents opportunities to reduce human errors. The study proposes several strategies for telecom companies to utilize precise real-time data to boost service quality while enhancing operational efficiency and achieving greater customer satisfaction. The research enhances comprehension of how data precision interacts with customer-focused business tactics in the telecommunications sector.

Real-Time Data Accuracy, Telecom Services, Network Downtime, Quality of Service (QoS), Predictive Analytics, Machine Learning, Big Data in Telecom

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