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Chat Analysis

Report #32

Written by Keith Winhall

Updated at March 31st, 2025

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Table of Contents

Introduction Key Features Header Per Time Segment Totals Footer Use Case Conclusion

Report Example

 
 

Introduction

The Chat Analysis report offers insights into the performance of web chat conversations managed by the selected Router(s). It measures how web chats are assigned to Agents and evaluates performance metrics based on Router configurations. This report helps supervisors understand chat efficiency, agent availability, and customer satisfaction.

Key Features

The report provides the following information:

Header

Name of the Report: As defined during report setup.

Original Report Type and Period: Type and date range covered by the report.

Selected Routers: The Routers included in the analysis.

Per Time Segment

Time Segment: The interval for data measurement.

Received: Number of chat conversations received.

Closed: Chats received outside office hours.

No Agent: Chats received when no agent was available.

Abandoned: Chats ended before being assigned to an agent.

Handled: Chats that were managed by an agent.

Response Time:

Avg: Average response time before an agent handles the chat.

Max: Maximum response time before an agent handles the chat.

Service Level: Percentage of chats handled within the defined service level time.

Evaluation Score: Score given based on chat performance and customer feedback.

Totals

Total: Aggregated figures for all recorded metrics.

Footer

Date and Time: When the report was generated.

Use Case

A supervisor can use this report to evaluate how efficiently chats are being managed by agents. For example, if the 'No Agent' metric is high, it might indicate a staffing issue during specific periods. An elevated 'Abandoned' rate could suggest that customers are not willing to wait for responses, indicating a need for quicker agent availability or improved queue management.

Conclusion

By using the Chat Analysis report, supervisors can identify areas where chat handling can be improved. Addressing issues such as high abandonment rates or inadequate staffing during peak times can significantly enhance customer satisfaction and operational efficiency.

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