Media — User Intent Classification for Media Engagement Enhancement

Overview

The primary purpose of this DAG is to extract and classify user intents from various interaction channels, including chat and voice, to enhance customer engagement in the media industry. The data sources include CRM systems and interaction logs, which provide a comprehensive view of user behavior and preferences. The ingestion pipeline begins with data extraction from these sources, followed by normalization to ensure consistency across different formats. Once normalized, the intents are classif

The primary purpose of this DAG is to extract and classify user intents from various interaction channels, including chat and voice, to enhance customer engagement in the media industry. The data sources include CRM systems and interaction logs, which provide a comprehensive view of user behavior and preferences. The ingestion pipeline begins with data extraction from these sources, followed by normalization to ensure consistency across different formats. Once normalized, the intents are classified into predefined categories using machine learning algorithms that are trained on historical interaction data. Quality control measures are implemented at this stage to validate the accuracy of classifications, ensuring that only reliable data is used for further processing. The results of this classification process are then exposed via a RESTful API, allowing seamless integration with action orchestration systems for real-time responses. Key performance indicators, such as classification accuracy, processing time, and user engagement metrics, are monitored to assess the effectiveness of the pipeline. The business value of this DAG lies in its ability to provide actionable insights that can lead to improved customer interactions, tailored content delivery, and ultimately, increased customer satisfaction and retention in the competitive media landscape.

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

Use cases

• Enhances customer engagement through precise intent understanding
• Facilitates real-time responses to user inquiries
• Improves content personalization based on user intents
• Increases operational efficiency in customer support
• Drives higher customer satisfaction and loyalty