Retail — Agent Performance Monitoring and Customer Interaction Analysis

Overview

The purpose of this DAG is to monitor and enhance the performance of customer service agents in the retail sector by collecting and analyzing metrics from customer interactions. It ingests data from various sources, including call logs, chat transcripts, and customer feedback surveys. The data pipeline begins with the extraction of these inputs, followed by data cleansing to ensure quality and consistency. Next, the system processes the data through advanced analytics to identify trends in agent

The purpose of this DAG is to monitor and enhance the performance of customer service agents in the retail sector by collecting and analyzing metrics from customer interactions. It ingests data from various sources, including call logs, chat transcripts, and customer feedback surveys. The data pipeline begins with the extraction of these inputs, followed by data cleansing to ensure quality and consistency. Next, the system processes the data through advanced analytics to identify trends in agent performance, such as first contact resolution rates and average handling times. Anomaly detection algorithms are employed to flag any significant deviations from established performance benchmarks, triggering alerts for further investigation. The processed results are then visualized in a real-time dashboard, which displays key performance indicators (KPIs) that allow managers to track agent effectiveness and customer satisfaction. Monitoring these KPIs not only helps in maintaining service quality but also aids in making data-driven decisions for training and resource allocation. The business value lies in improved customer experience, increased operational efficiency, and enhanced agent productivity, ultimately leading to higher customer retention and satisfaction in the competitive retail landscape.

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

Use cases

• Enhanced customer satisfaction through improved service quality
• Increased agent productivity and efficiency
• Proactive issue resolution via anomaly alerts
• Data-driven decision-making for training needs
• Competitive advantage through superior customer service