Telecom — Customer Intent Classification for Enhanced Service Delivery

Overview

The purpose of this DAG is to classify customer intents derived from various communication channels, thereby improving customer service in the telecom industry. It ingests data from multiple sources, including CRM systems and support ticket logs, ensuring a comprehensive view of customer interactions. The ingestion pipeline begins with data extraction, where relevant customer interaction data is collected. This is followed by a preprocessing step that cleans and normalizes the data, preparing it

The purpose of this DAG is to classify customer intents derived from various communication channels, thereby improving customer service in the telecom industry. It ingests data from multiple sources, including CRM systems and support ticket logs, ensuring a comprehensive view of customer interactions. The ingestion pipeline begins with data extraction, where relevant customer interaction data is collected. This is followed by a preprocessing step that cleans and normalizes the data, preparing it for analysis. Next, machine learning models are employed to classify the intents based on historical interaction patterns. The classification results undergo validation through quality control measures that ensure accuracy and reliability. The outputs of this DAG are made available via APIs, facilitating seamless integration with management systems for real-time insights. Monitoring key performance indicators (KPIs) such as classification accuracy and response time is crucial for assessing the effectiveness of the model. The business value lies in its ability to provide telecom companies with a deeper understanding of customer needs, enabling proactive service improvements and personalized customer interactions.

Part of the AI Assistants & Contact Center solution for the Telecom industry.

Use cases

• Improves customer satisfaction through accurate intent recognition
• Reduces response times by automating classification processes
• Enables proactive service adjustments based on customer needs
• Facilitates personalized interactions, increasing customer loyalty
• Optimizes resource allocation in contact centers