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Transcript Filtering

Transcript filtering for a chatbot with parameters like "recorded in," "recorded after," "recorded before," "longer than," and "shorter than" is a valuable feature that enables users to selectively retrieve and display chatbot conversations or sessions based on specific criteria. The filtered data lists these conversations along with various details, including Thread, Duration, User Messages, Virtual Agent Messages, Human Agent Messages, Escalated status, and Handled status. Users can click on any conversation or session to access and view the full conversation details.

Key Components and Significance

Filtering Predicates

Recorded In

Users can filter conversations that occurred within a specified timeframe, such as a specific date, month, or year.

Use case: Users can narrow down conversations to a particular time period to analyze trends or incidents within that timeframe.

Recorded After

Conversations recorded after a specific date and time can be filtered using this parameter.

Use case: Useful for isolating recent conversations to assess recent user interactions or changes in chatbot performance.

Recorded Before

Users can filter conversations recorded before a specified date and time.

Use case: Helps in reviewing historical data or comparing current interactions with past ones.

Longer Than

Conversations with a duration longer than a defined threshold, often specified in minutes or seconds, can be filtered using this parameter.

Use case: Useful for identifying lengthy conversations that may require special attention or analysis.

Shorter Than

Conversations shorter than a specified duration can be filtered using this parameter.

Use case: Allows users to identify brief interactions that may require a different analysis or follow-up.

Filtered Data Display Details Provided

After applying the desired filters, the chatbot analytics platform lists the filtered conversations or sessions.

Thread

A reference or identifier for the conversation or session, which allows users to distinguish between different interactions.

Duration

The duration of the conversation or session, indicating how long the interaction lasted.

User Messages

The count of messages or inputs provided by the user during the conversation or session.

Virtual Agent Messages

The count of messages or responses generated by the chatbot or virtual agent.

Human Agent Messages

The count of messages sent by a human agent, if applicable.

Escalated

Indicates whether the conversation or session was escalated to a human agent or a higher level of support.

Handled

Indicates whether the conversation or session was successfully handled or resolved.

Clickable Threads

Users can click on any conversation or session entry in the filtered list to access and view the complete details of that specific conversation or session.

Significance and Use Cases

Performance Analysis

Users can analyze chatbot performance during specific time periods using the "Recorded In," "Recorded After," and "Recorded Before" filters.

Quality Control

Monitoring conversations longer or shorter than typical durations can help identify quality issues, user engagement patterns, and opportunities for improvement.

Incident Investigation

Filtering conversations by date and time is crucial for investigating specific incidents, tracking user interactions, and identifying trends.

Trend Analysis

Users can use filtering to track how conversation lengths, escalation rates, and handling success rates change over time.

User Support

Support teams can prioritize handling conversations that were escalated or remain unresolved.

Summary

In summary, transcript filtering for a chatbot with parameters like "recorded in," "recorded after," "recorded before," "longer than," and "shorter than" provides users with a comprehensive tool for exploring, analyzing, and managing chatbot interactions. The inclusion of details such as duration, message counts, escalation status, and handling status enhances the depth of insights and enables effective performance monitoring and improvement of chatbot interactions.