Telecom — Customer Intent Classification for Enhanced Service Delivery

Overview

The primary purpose of this DAG is to classify customer intentions based on their interactions, thereby enhancing service delivery in the telecom industry. It ingests data from various sources, including interaction logs and support tickets, which are recorded in the Customer Relationship Management (CRM) system. The ingestion pipeline begins with the automatic triggering of the DAG upon new interactions being logged. The processing steps include sentiment analysis to gauge customer emotions, fo

The primary purpose of this DAG is to classify customer intentions based on their interactions, thereby enhancing service delivery in the telecom industry. It ingests data from various sources, including interaction logs and support tickets, which are recorded in the Customer Relationship Management (CRM) system. The ingestion pipeline begins with the automatic triggering of the DAG upon new interactions being logged. The processing steps include sentiment analysis to gauge customer emotions, followed by intent classification to categorize the nature of the interactions. Quality control measures are implemented to ensure the accuracy of these classifications, which involves cross-referencing results with historical data and applying validation checks. The final outputs are integrated back into the CRM system, allowing for improved personalization of customer service. Key performance indicators (KPIs) monitored include the accuracy rate of classifications and the response time to customer inquiries. By providing deeper insights into customer intentions, this DAG significantly contributes to enhancing customer satisfaction and loyalty, ultimately driving business value in a competitive telecom landscape.

Part of the Fraud & Anomaly Analytics solution for the Telecom industry.

Use cases

• Improved customer satisfaction through personalized interactions
• Faster response times to customer inquiries
• Enhanced understanding of customer needs and preferences
• Increased operational efficiency in handling support tickets
• Data-driven insights for strategic decision-making