> ## Documentation Index > Fetch the complete documentation index at: https://koreai-content-gov.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Customer voice This document covers the customer experience layer in Analytics and Insights: the Customer Voice covers intent classification, sentiment trajectory, and frustration detection, and the Feedback for end-user ratings and verbatim comments. ## Customer insights The **Customer Insights** page helps you understand what customers are asking about and how they feel about the experience. It combines intent classification, sentiment scoring, frustration detection, and resolution tracking into a single view to help you identify emerging topics, detect dissatisfaction early, and measure whether the agent resolves the intents it encounters. Navigation: **Project** → **Insights** → **Customer Insights** **Date range selector**: Use the toggle to select **7d**, **30d**, or **90d**. **Filters**: Select **All environments** and **All channels** at the top of the page to narrow the data by deployment environment and channel. Filter selections are reflected in the page URL, so you can bookmark a filtered view or share it with a colleague. Customer Insight ### Customer sentiment summary A banner at the top displays a contextual summary of the analyzed data, for example: "322 analyzed conversations · 144 intent-classified · 322 sentiment-scored · 13 intents detected · 26.7% frustration." A sentiment indicator (for example, "Mixed Sentiment") provides a quick qualitative read. ### KPI metric cards | Metric | Description | | -------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Analyzed Conversations** | Total conversations that pipelines analyzed in the selected period. The sub-label shows the breakdown. | | **Unique Intents** | Number of distinct intents the system identified across all analyzed conversations. | | **Avg Sentiment** | Average sentiment score across all conversations. A score near 0 indicates neutral sentiment; positive values indicate positive sentiment. | | **Frustration Rate** | Percentage of conversations where the system detected user frustration signals (repeated questions, negative language, escalation requests). A warning triangle appears when the rate exceeds the configured threshold. | | **Resolution Rate** | Percentage of conversations reaching successful resolution. A warning triangle appears when the rate drops below the threshold. The sub-label shows the count of resolved conversations the system evaluated. | ### Intent distribution A horizontal bar chart ranks detected intents by volume. Each bar shows the intent name, count, and percentage of total conversations. The chart groups low-volume intents (typically those below a threshold count) into an "Other" category. The footer indicates the total classified intent assignments and how many low-volume intents the chart grouped. ### Sentiment trajectory A side-by-side panel displays conversation counts across three sentiment directions: | Direction | Description | | ------------- | ------------------------------------------------------------------------------------ | | **Improving** | Conversations where sentiment trended positively over the course of the interaction. | | **Stable** | Conversations where sentiment remained consistent throughout. | | **Declining** | Conversations where sentiment deteriorated — candidates for investigation. | The footer indicates the total conversations with sentiment data contributing to the trajectory analysis. ### Trends over time A section below the distribution charts plots intent volumes and sentiment scores as time-series data, enabling you to track whether specific intents are growing or shrinking and whether sentiment is trending in the right direction. *** ## Feedback The **Feedback** page surfaces end-user feedback captured directly from chat sessions. It gives you access to raw ratings and verbatim comments so you can identify satisfaction patterns, detect underperforming agents or channels, and prioritize improvements based on what users say. **Navigation**: **Project** → **Insights** → **Feedback** **Date range selector**: Use the toggle to select **Today**, **7d**, or **30d**. **Filters**: Select **All environments** and **All channels** at the top of the page to narrow the data by deployment environment and channel. Filter selections are reflected in the page URL, so you can bookmark a filtered view or share it with a colleague. ### Filters Four filter controls let you narrow feedback results: | Filter | Description | | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | **All ratings** | Filter by feedback rating. Use the dropdown to select a specific star rating or view all ratings. | | **Comment: any** | Filter by comment presence. Choose whether to show all feedback, only entries with comments, or only entries without comments. Entries with comments are often the most actionable for qualitative analysis. | | **Agent name** | Filter by the agent that handled the conversation. Select an agent from the searchable dropdown, which lists the agents that appear in your feedback data. Matching isn't case-sensitive. | | **Channel** | Filter by the channel the conversation came in on. Select a channel from the searchable dropdown, which lists the channels that appear in your feedback data. | Select **Refresh** to reload feedback data with the current filter selections. ### Feedback table The table lists one row per piece of feedback. Alongside the rating, comment, agent, and channel, two columns identify who left the feedback. | Column | Description | | --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **User ID** | The end-user identifier captured on the session. Use it to trace feedback back to a specific user rather than only to a session. | | **Custom dimensions** | The project-defined values captured on the session, such as a member ID, plan type, or region. Define these as session variables in your project, then group and filter on them in [Custom dashboards](/agent-platform/analytics-insights/custom-dashboards). | ### Export feedback Select **Export** to download the filtered feedback as a CSV file. The export includes **User ID** and **Custom dimensions** alongside the ratings, comments, and session references, so you can join feedback to your own customer records offline.