Banking — Agent Interaction Quality Monitoring Pipeline

Free

This DAG monitors agent interactions to enhance performance and customer satisfaction. It analyzes metrics from various sources to identify trends and quality issues, enabling proactive management.

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Overview

The Agent Interaction Quality Monitoring Pipeline is designed to enhance the performance of banking agents by systematically collecting and analyzing metrics related to their interactions with customers. The primary data sources include agent interaction logs, customer feedback surveys, and performance metrics from contact center software. The ingestion pipeline begins with the extraction of these data sources, followed by data cleansing and normalization to ensure consistency. Processing steps

The Agent Interaction Quality Monitoring Pipeline is designed to enhance the performance of banking agents by systematically collecting and analyzing metrics related to their interactions with customers. The primary data sources include agent interaction logs, customer feedback surveys, and performance metrics from contact center software. The ingestion pipeline begins with the extraction of these data sources, followed by data cleansing and normalization to ensure consistency. Processing steps involve analyzing interaction logs to identify patterns, sentiment analysis of customer feedback, and benchmarking against established quality standards. Quality controls are implemented to flag anomalies, triggering alerts for further investigation. Key performance indicators (KPIs) monitored include customer satisfaction rates, average handling time, and cost per interaction. The outputs of this pipeline consist of detailed reports on agent performance, alerts for quality issues, and dashboards visualizing trends over time. By leveraging these insights, banks can optimize agent training, improve customer service strategies, and ultimately drive higher customer loyalty and retention.

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

Use cases

  • Improved customer satisfaction through better agent performance
  • Enhanced training programs based on data-driven insights
  • Reduced operational costs by identifying inefficiencies
  • Increased agent accountability through performance tracking
  • Proactive issue resolution leading to higher retention rates

Technical Specifications

Inputs

  • Agent interaction logs from contact center software
  • Customer feedback survey responses
  • Performance metrics from CRM systems

Outputs

  • Detailed agent performance reports
  • Alerts for identified quality issues
  • Dashboards visualizing interaction trends

Processing Steps

  1. 1. Extract data from interaction logs and feedback surveys
  2. 2. Cleanse and normalize the collected data
  3. 3. Analyze interaction logs for performance patterns
  4. 4. Conduct sentiment analysis on customer feedback
  5. 5. Benchmark performance against quality standards
  6. 6. Generate alerts for detected anomalies
  7. 7. Produce reports and dashboards for stakeholders

Additional Information

DAG ID

WK-0092

Last Updated

2025-06-30

Downloads

76

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