Telecom — Customer Service Optimization Workflow for Telecom

Overview

The primary purpose of this DAG is to enhance customer service operations within the telecom industry by minimizing the processing time of customer requests. It ingests data from various sources, including support ticket logs, customer interaction records, and feedback surveys. The ingestion pipeline normalizes these inputs to ensure consistency and accuracy across the dataset. Following data normalization, the DAG applies analytical techniques to identify friction points in customer service wor

The primary purpose of this DAG is to enhance customer service operations within the telecom industry by minimizing the processing time of customer requests. It ingests data from various sources, including support ticket logs, customer interaction records, and feedback surveys. The ingestion pipeline normalizes these inputs to ensure consistency and accuracy across the dataset. Following data normalization, the DAG applies analytical techniques to identify friction points in customer service workflows, such as delays in ticket resolution or common issues faced by customers. Quality control measures are implemented at multiple stages to validate the integrity and reliability of the data, ensuring that insights drawn from the analysis are actionable and trustworthy. The results of this processing are visualized through an interactive dashboard, which provides customer service teams with real-time insights into performance metrics and areas for improvement. Key Performance Indicators (KPIs) such as average resolution time, customer satisfaction scores, and ticket backlog are monitored to assess the impact of implemented changes. By leveraging these insights, telecom companies can streamline their customer service processes, ultimately leading to enhanced customer satisfaction and loyalty, reduced operational costs, and improved service delivery.

Part of the Market & Trading Intelligence solution for the Telecom industry.

Use cases

• Reduced average handling time for customer requests
• Increased customer satisfaction and loyalty
• Enhanced operational efficiency in service teams
• Data-driven decision-making for service improvements
• Proactive identification of recurring customer issues