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Life Science — Agent Intent Classification for Enhanced Customer Interaction

Free

This DAG classifies agent intents from customer interactions to improve satisfaction. By leveraging CRM data and conversation histories, it enhances service quality and operational efficiency.

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Overview

The purpose of this DAG is to classify agent intents based on customer interactions, ultimately enhancing customer satisfaction and interaction quality in the life sciences sector. The data sources include CRM systems and historical conversation logs, which are crucial for understanding agent behavior and customer needs. The ingestion pipeline begins with data extraction from these sources, followed by preprocessing steps that clean and standardize the data for analysis. Next, a classification m

The purpose of this DAG is to classify agent intents based on customer interactions, ultimately enhancing customer satisfaction and interaction quality in the life sciences sector. The data sources include CRM systems and historical conversation logs, which are crucial for understanding agent behavior and customer needs. The ingestion pipeline begins with data extraction from these sources, followed by preprocessing steps that clean and standardize the data for analysis. Next, a classification model is trained using machine learning techniques to accurately identify agent intents. Quality control measures are implemented throughout the process to ensure the precision of classifications, including validation against a test dataset and regular performance evaluations. The results of the classification are exposed through APIs, enabling seamless integration with customer service tools. Key performance indicators (KPIs) such as classification accuracy and recall are monitored to assess the effectiveness of the model. The business value of this DAG lies in its ability to provide actionable insights that lead to improved customer interactions, increased agent efficiency, and ultimately higher customer satisfaction in the life sciences industry.

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

Use cases

  • Improved customer satisfaction through targeted interactions.
  • Enhanced operational efficiency for customer service teams.
  • Data-driven insights for continuous improvement.
  • Faster response times to customer inquiries.
  • Increased alignment between agent actions and customer needs.

Technical Specifications

Inputs

  • • CRM transaction records
  • • Historical conversation logs
  • • Customer feedback data

Outputs

  • • Classified agent intent data
  • • API access for service tools
  • • Performance reports on classification accuracy

Processing Steps

  1. 1. Extract data from CRM and conversation logs
  2. 2. Preprocess data for analysis
  3. 3. Train classification model on prepared data
  4. 4. Validate model accuracy with test dataset
  5. 5. Implement quality control measures
  6. 6. Expose results via API for integration
  7. 7. Monitor KPIs for ongoing performance evaluation

Additional Information

DAG ID

WK-1444

Last Updated

2025-12-24

Downloads

4

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