CSAT Analysis – HiOperator Support

CSAT Analysis within the HiQ platform utilizes advanced large language models (LLMs) to process and analyze customer satisfaction (CSAT) data at scale. This system provides deeper insights into customer feedback, allowing organizations to identify and address trends across different contact groups. Here’s how the analysis works:

Key Features

Data Aggregation: HiQ gathers all CSAT data from various customer interactions and sends it in batches to LLMs. This enables the processing of large volumes of feedback across different touchpoints and contact types.
LLM Analysis: By leveraging LLMs, HiQ performs detailed analysis of CSAT feedback. These models can identify patterns, detect trends, and uncover underlying themes that are influencing customer satisfaction within major contact groups (e.g., billing, technical support, product inquiries).
Trend Identification: The system categorizes CSAT trends by key contact types or reasons. This helps organizations understand which areas of customer service are excelling and which may require improvement or further attention.

Benefits

Proactive Insights: CSAT analysis through LLMs enables organizations to proactively monitor satisfaction trends, allowing for early intervention in areas where satisfaction may be declining.
Scalability: The ability to process large volumes of data makes it easier to track feedback trends across different teams, departments, and time periods without manually combing through individual CSAT surveys.
Strategic Improvements: The insights gathered from LLM-driven analysis help leaders make informed decisions on where to focus training, policy adjustments, and process improvements.
Informed Decision-Making: By understanding satisfaction trends across major contact categories, customer service leaders can allocate resources effectively, optimize workflows, and enhance overall service quality.

Summary

The CSAT Analysis within HiQ leverages large language models (LLMs) to process and analyze customer satisfaction data at scale. By identifying trends in key contact groups, it empowers organizations to proactively address issues, optimize customer service strategies, and continuously improve overall satisfaction and performance.