Using the Insights Dashboard

Once you have completed the setup and Insights are active, the AI Insights dashboard gives you a comprehensive view of your chat data. Here’s how to navigate and make the most out of your insights:

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Dashboard Overview & metric breakdown

1. Total Chats

Displays the total number of chats analyzed in the selected period, with a comparison to the previous period to help you track trends in your chat volume.

2. Resolution Rate

How it’s calculated: Each conversation is classified based on whether a clear outcome was reached in the chat. If the issue is explicitly resolved, it’s marked as “Solved.” Otherwise, it’s “Incomplete,” “Unsolved,” or “Unsolved / No Info” if unclear.

• Solved: The chat ended with a clear resolution.

• Incomplete: The conversation did not reach a clear outcome, such as the user stopped responding.

• Unsolved: The system could not solve the inquiry in a satisfying manner.

• Unsolved/No Info: The bot expresses specifically that is lacks information to resolve the user's inquiry. This could be interpreted as a knowledge gap.

Resolution Over Time: Shows how resolution types have changed across recent months. This helps you monitor improvements or spot periods needing attention.

3. Sentiment

Displays the distribution of Positive, Negative, and Neutral conversations, helping you gauge overall customer satisfaction and changes in sentiment over time.

How it’s calculated: Only user messages are analyzed. Positive sentiment is flagged if the user expresses gratitude or satisfaction, especially at the end of the chat. Clear complaints or frustration are classified as negative. All other cases, or a mix, are considered neutral. (Bot messages are always ignored.)

4. Most Common Topics

How it’s calculated: AI Insights reviews each conversation and assigns up to two relevant topics that reflect the user's main issues. The first button the user clicks is prioritized, and explicit statements from the user determine which topics are set on the conversation. If no clear issue is mentioned, no topic will be set. Available topics are customized for each customer’s needs on the Config page.

5. Escalation rate

The Escalation Rate measures the efficiency of your automation by tracking how many interactions required human intervention. This metric helps identify automation rates or scenarios where users prefer human assistance.

The "Escalated chats" card provides a breakdown of human interventions into two specific categories:

• Total: The aggregate number of chats that were escalated during the selected period.

• Handovers: The count of conversations successfully transferred to a live agent (e.g., via Live Chat).

• Tickets created: The count of conversations that resulted in a support ticket being logged for asynchronous follow-up.

Note: The percentage indicators (e.g., ↓ -100%) represent the change in volume compared to the previous time period.

Configuration: Tracking Ticket Creation

While "Handovers" are usually tracked automatically by the chat system, Tickets created require specific configuration to be counted in the analytics.

For a ticket to register in the "Escalated chats" statistics, you must explicitly flag the interaction when a ticket is successfully generated. This is done by adding a side_effect to the bot's logic in the success scenario.

Add the following JSON syntax to the Ticket Success action/node in your bot builder:

{
  "side_effects": {
    "set_ticket_created": true
  }
}

6. Topic Overview

This section allows for a granular "drilldown" into specific categories to identify performance gaps or trends. While the global dashboard shows the big picture, the Topic Overview lets you filter data by a single subject (e.g., "Maintenance" or "Rental Agreements") using the dropdown menu on the left.

Once a topic is selected, the data reflects only conversations associated with that specific subject, subject to your global date and filter settings.

Topic-Specific Metrics: Three key cards display performance specific to the selected topic:

• Total Chats: The volume of conversations tagged with this topic during the selected period, including a comparison to the previous period.

• Satisfaction Rate: The percentage of chats within this topic that ended with positive user sentiment.

• Escalation Rate: The percentage of chats regarding this topic that were handed over to a human agent (if applicable) or where the bot could not resolve the issue.

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Chat History Table

Below the main charts, you’ll find a detailed table of individual chat records, showing:

• Username: The participant in the conversation.

• Resolution: The outcome status (e.g., Solved, Incomplete, Unsolved, Unsolved / No Info).

• Sentiment: The tone of the conversation (Positive, Neutral, Negative).

• Topics: The main topics identified in the chat.

Viewing Chat Details and Insight Reasoning

Clicking on any chat entry in the chat history table will open a detailed modal, allowing you to:

• View the full conversation transcript: Read the entire chat to understand the context.

• See detailed insight breakdowns: For each identified topic, sentiment, or resolution, you can see the AI’s reasoning, including the relevant parts of the conversation used for analysis.

• Refresh insights: If needed, you can refresh the insights to reanalyze the conversation with updated settings or data. If a chat has no Insights yet, you can use this button to manually trigger the chat for analysis.

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Making the Most of Your Insights

• Track Trends: Use the dashboard to notice shifts in chat volume, resolution rates, or sentiment, which could signal changes in customer needs or agent performance.

• Identify Focus Areas: Look for frequent unresolved issues, negative sentiment, or trending topics to prioritize improvements.

• Investigate Details: Drill down into individual chat records for a deeper understanding of customer interactions and context behind the metrics.