> ## Documentation Index > Fetch the complete documentation index at: https://docs.ravenna.ai/llms.txt > Use this file to discover all available pages before exploring further. # Analytics > Track service desk performance with prepackaged and custom dashboards covering tickets, agents, SLAs, CSAT, AI deflection, and team productivity. Track service desk performance through prepackaged dashboards and custom analytics. Monitor ticket lifecycles, agent performance, customer satisfaction, SLA compliance, and operational efficiency to understand and improve support operations. Use seven prepackaged dashboards for immediate insights or create custom dashboards for specific reporting needs. Analytics captures data across all aspects of your service desk to support data-driven decisions. ## Accessing analytics Navigate to analytics dashboards from the left sidebar to explore service desk data. From your workspace, click **Analytics** in the left sidebar. Select from seven prepackaged dashboards: **Tickets**, **Agents**, **Efficiency**, **Forms**, **SLAs**, **CSAT**, and **Knowledge Base Analytics**, or create custom dashboards. Use date ranges, channel filters, and other parameters to focus on relevant data. *** ## Drilling into chart values Click any data point on a chart powered by the **Tickets** data source to open a drill-down modal listing the individual tickets behind that value. Use it to investigate spikes, audit a segment, or jump to a specific ticket without leaving the dashboard. The drill-down is available on metric, grouped, and trend visualizations. The modal title reflects the segment you clicked (for example, the status name on a grouped chart or the series label on a time-series chart) and shows the total ticket count. **To drill in:** Hover a clickable segment, bar, slice, or data point — the cursor switches to a pointer when drill-down is available. Click to open the modal. Type in the search box to filter the result set, or click a column header to sort. The modal keeps the chart's filters (group, date range, and any dashboard filters) in place. Click any row to open that ticket in a new tab so you keep your place on the dashboard. Click the external-link icon in the header to open the same result set in the main tickets list, where you can apply additional filters, bulk-edit, or export. Drill-down is available on widgets that use the **Tickets** data source. Charts built on **Messages** or **Workflow Runs** are not clickable. *** ## Organizing dashboards Keep analytics organized using collections. Group related dashboards by team, business function, or reporting purpose as your analytics needs grow. ### Creating collections Open **Analytics** from your workspace sidebar Click **New** and select **Folder** to create a collection Provide a clear name and description that explains what dashboards belong in this collection Optionally set a parent collection to create hierarchical organization ### Moving dashboards and collections Reorganize dashboards and collections as your reporting structure evolves. Use drag-and-drop or bulk operations to maintain organized analytics. Drag dashboards or collections to move them between locations. **Single item:** 1. Click and hold on a dashboard or collection 2. Drag to the target collection or breadcrumb 3. Release to move **Multiple items:** 1. Select multiple dashboards or collections using checkboxes 2. Drag any selected item 3. All selected items move together **Create new collection during move:** Drag items to the "Move to new folder" button to create and move in one action. Move multiple dashboards at once using bulk actions. 1. Select dashboards or collections using checkboxes 2. Click the **Move** action in the toolbar 3. Select the target collection 4. Confirm the move Bulk move is ideal for reorganizing analytics or restructuring your collection hierarchy. Some moves are prevented to maintain system integrity: * Collections cannot be moved into their own subcollections (prevents circular references) * Moving a collection also moves all dashboards and subcollections within it Prepackaged dashboards cannot be moved or deleted. These system dashboards remain in their default location. *** ## Prepackaged dashboards Seven ready-to-use dashboards provide immediate insights into service desk operations. Monitor ticket volume, status distribution, and lifecycle patterns. * Overall ticket counts and trend indicators * Active tickets requiring attention * Tickets awaiting assignment * Tickets being worked on by agents * Successfully completed tickets * Time-series visualizations of creation patterns and status transitions * **Automation status**: AI-powered classification of resolved tickets as **automated** (handled without human intervention), **automatable** (could be automated with additional configuration), or **non-automatable** (requires human judgment). Use this metric to measure Ravenna's impact and identify opportunities to automate more of your support volume. You can also add **Automation status** as a card condition to scope a widget to one bucket, such as counting only **automated** tickets in a Metric card. * **Deflection status**: AI-powered classification of resolved tickets where an AI agent participated as **deflected** (resolved primarily by AI responses, automated workflows, or knowledge base answers) or **escalated** (a human agent performed substantive work to drive resolution). Acknowledgments, assignments without action, and approving an automated step do not count as substantive human involvement. Use this metric to track your AI deflection rate and quantify how often AI agents resolve tickets end-to-end. You can also add **Deflection status** as a card condition to focus a widget on **deflected** or **escalated** tickets only. * **Resolution path**: Deterministic classification of resolved tickets by who handled them: **human touched** (an agent made a qualifying manual action such as a status change, message, assignment, priority change, form submission, or approval), **AI resolved** (no human touch, but an AI agent participated), **workflow only** (no human touch and no AI agent, but a workflow acted), or **unclassified** (no signal in the event log). Precedence is human touched > AI resolved > workflow only, so any qualifying human action wins even if it happens after an automated resolution. Requester replies on their own ticket are not counted as human touch. Use this metric to see how much of your resolved volume runs end-to-end without human involvement, and group **Resolution path** on any Tickets widget to break down the mix. Learn more about [ticket statuses](/documentation/tickets/organize/statuses) and [channels](/documentation/tickets/channels) Analyze team performance and AI automation impact. * Workload distribution across your team * Human vs automated resolution rates * Efficiency gains from AI agent participation * Visual breakdowns of ticket assignments * Historical analysis of agent activity and AI impact Learn more about [AI agents](/documentation/automate/agents/overview) and [ticket roles](/documentation/tickets/roles) Track operational efficiency and process optimization metrics. * Average response time across all tickets * Ticket assignment speed * Overall resolution performance metrics * Efficiency trend analysis over time Response time and resolution metrics help identify bottlenecks and process improvement opportunities. Understand request patterns and analyze how different forms perform across your service desk. Learn more about [creating and managing forms](/documentation/tickets/forms/overview) Monitor service level agreement performance across all priority levels. * First response times by priority (Total, Urgent, High, Medium, Low) * Resolution performance breakdown by priority level * Historical trend visualization * Closure time analysis across priority categories * Compliance breakdown by SLA, status (Met or Breached), or target type (Time to First Response, Time to Resolution, Time to Close) when building custom cards Learn more about [configuring SLAs](/documentation/automate/slas) and [setting priorities](/documentation/tickets/organize/priorities) Track customer satisfaction patterns and feedback trends. * Overall customer satisfaction scores across all tickets * Satisfaction scores by priority levels * Time-series analysis showing satisfaction trends CSAT data is only available for channels where customer satisfaction surveys have been enabled and tickets have been resolved with customer feedback. Learn more about [customer satisfaction surveys](/documentation/measure/csat) Measure knowledge base effectiveness and article performance to understand how documentation impacts ticket deflection and user self-service. * Total users interacting with knowledge base articles * Ticket deflection metrics showing requests resolved through self-service * Ticket escalation rates when knowledge base cannot resolve requests * Top 10 most viewed articles across all categories * Top 10 articles with highest deflection rates * Top 10 articles with highest escalation rates * Knowledge base usage trends over time Knowledge Base Analytics helps identify which articles effectively resolve user questions and which topics require improved documentation or additional support resources. Learn more about [configuring knowledge sources](/documentation/automate/knowledge/overview) ## Custom dashboards Create tailored dashboards using the flexible dashboard builder for specific reporting needs beyond the prepackaged dashboards. ### Building custom dashboards Click **Add Card** from the top left of any analytics page to start building a custom dashboard or widget. Select from three card types: * **Metric**: Display single values and grouped categorical data * **Trend**: Create time-series visualizations showing data changes over time * **Table**: List individual tickets that match a set of conditions Provide a **Name** and **Description** for your widget to make it easily identifiable to your team. Select your data source: * **Tickets**: Primary service desk data including status, priority, assignments * **Messages**: Ticket message data for analyzing communication patterns * **Workflow Runs**: Workflow execution data for tracking automation performance Set up your widget with grouping, aggregation, and time interval options based on your selected card type and data source. ### Card types Use Metric cards to display key performance indicators with single values or grouped categorical data. **Best for:** * Current totals and counts * Categorical breakdowns * Snapshot views of current state * Comparing values across groups Use Trend cards to create time-series visualizations showing how your data changes over time. **Best for:** * Historical pattern analysis * Identifying trends and seasonality * Tracking progress toward goals * Comparing performance across time periods Use Table cards to surface a list of individual tickets that match a set of conditions, ranked by the field you choose. Unlike Metric and Trend cards, which aggregate values, Table cards show ticket-level detail directly on the dashboard. **Best for:** * Watchlists of tickets that need attention (for example, oldest open, highest priority unassigned) * Surfacing top or bottom rows by a field such as created date, updated date, or due date * Sharing a queue or backlog snapshot on an executive or team dashboard **Configuration:** * **Data**: Select the data source. Table cards currently support the **Tickets** data source. * **Max Tickets**: Set the maximum number of rows to display. Defaults to 10. * **Sort By**: Select the field used to order results (for example, **Created**, **Updated**, or **Due Date**). * **Sort Order**: Select **Descending** to show the highest or most recent values first, or **Ascending** to show the lowest or oldest first. * **Conditions**: Add filter groups to narrow results to the tickets you care about, using the same condition builder as ticket views. Each row shows the ticket's priority, key, summary, status, and assignee, and clicking a row opens the ticket. Table cards are sized wider than Metric and Trend cards by default so ticket rows have room to render. Resize the widget on the dashboard grid if you need to fit more or fewer rows on screen. ### Data sources and grouping Group your analysis by key dimensions depending on the data source you select. Primary service desk data including status, priority, and assignments. **Status and assignments** * Ticket status * Assignee * Requester * Author **Categories** * Priority levels (Urgent, High, Medium, Low) * Channels * Forms * Tags **Customer feedback** * CSAT score (1-5 stars, plus a "No CSAT" bucket for tickets that have not been rated) **Service level agreements** * **SLA**: Group tickets by the SLA attached to them to compare performance across policies * **Status**: Group tickets by SLA outcome — **Met** or **Breached** — to monitor overall compliance * **Target**: Group tickets by SLA target type — **Time to First Response**, **Time to Resolution**, or **Time to Close** — to compare how each commitment is performing **Dates** * Created * Updated * Start date * Due date * Approved * Declined * Archived dates **AI involvement** * Whether human agents or AI agents participated in ticket resolution Ticket message data for analyzing communication patterns. **Authors and sources** * Message author * Source tracking * Associated ticket **AI analysis** * Human vs AI-generated messages * Message feedback comparison **Message types** * Public messages * Private messages * Response categorization * Time-based analysis Workflow execution data for tracking automation performance and reliability. **Workflow details** * Workflow name * Run status (completed, failed, running) * Collection * Workspace **Related ticket data** * Channel * Ticket status * Assignee * Priority * Source * Category Workflow Runs only supports count aggregation. Average, sum, min, and max aggregation types are not available for this data source. ### Configuration options Configure how your custom dashboard aggregates and displays data. Select how to calculate values for your analytics cards. **Count** * Total number of records in your dataset * Best for: Ticket volume, message counts, activity tracking **Average** * Mean value calculation for numeric data * Best for: Response times, resolution times, satisfaction scores **Sum** * Total of all numeric values * Best for: Total time spent, cumulative values **Min and Max** * Minimum or maximum values in your dataset * Best for: Fastest/slowest response times, date ranges **Percent of** * Percentage value with two modes depending on whether a grouping is applied * Available on the **Tickets** data source *Condition mode (no grouping)* Share of tickets matching a specific condition, expressed as a percentage. Select one of the following fields to calculate the percentage against: SLA fields: * **SLA Attached**: Percentage of tickets in the dataset that have an SLA applied * **SLA Met**: Of tickets whose SLA has reached a terminal state, the percentage that were met before the deadline * **SLA Breached**: Of tickets whose SLA has reached a terminal state, the percentage that breached the deadline * **SLA At Risk**: Of tickets with an SLA applied, the percentage currently in an alert state (alert threshold crossed but not yet met or breached) Deflection fields: * **Deflected**: Of tickets with a classified deflection status, the percentage resolved primarily by AI. Use this to track your AI deflection rate. * **Escalated**: Of tickets with a classified deflection status, the percentage that required substantive human work to resolve. Resolution path fields: * **No Human Touch**: Of resolved tickets, the percentage that closed without any qualifying human action — the sum of **AI resolved** and **workflow only** resolution paths. Use this to track how much of your resolved volume runs end-to-end without an agent stepping in. Best for: SLA compliance rates, breach reporting, AI deflection rate reporting, and surfacing tickets that need attention before they breach. *Distribution mode (with grouping)* When you select a **Group by** dimension, the **Field to Aggregate** becomes optional. Leave it empty to show each group's share of the total — the placeholder reads **None (share of total)**. You can still select a field to combine an SLA, deflection, or resolution path condition with the grouping. Best for: showing the breakdown of tickets across statuses, priorities, channels, or assignees as percentages that sum to 100%. **SLA Met** and **SLA Breached** are calculated only against tickets whose SLA has finished (met or breached). **SLA At Risk** is calculated against all tickets with an SLA applied and excludes tickets already met or breached, so the three values do not necessarily sum to 100%. **Deflected** and **Escalated** are calculated only against tickets that have a classified deflection status, so unclassified tickets do not dilute the rate. Time intervals determine how your data is grouped and displayed over time in Trend cards. **Hourly** * Granular intra-day analysis for high-volume monitoring * Best for: Real-time operations, incident response tracking, workflow run monitoring **Daily** * Detailed short-term analysis with individual data points * Best for: Recent activity monitoring, identifying daily patterns **Weekly** * Medium-term trend analysis grouped by weeks * Best for: Sprint cycles, weekly performance reviews **Monthly** * Long-term pattern analysis ideal for identifying seasonal trends * Best for: Monthly reporting, quarterly planning **Quarterly and annual** * Business reporting cycles and year-over-year performance tracking * Best for: Executive reporting, long-term trend analysis Select time intervals based on your analysis needs. Shorter intervals provide more detail but may include more noise, while longer intervals reveal broader trends. Switch any Metric or Trend card from a chart to a data table using the **Table** option in the widget's view mode toggle. Use the table view to read exact values, scan a long list of groups, or compare numbers side by side when a chart becomes hard to interpret. **Best for:** * Inspecting precise values behind a chart * Widgets with many groups that crowd a bar or pie chart * Sharing exact numbers in screenshots or reviews **How it appears:** * Metric cards display each group on its own row with a value column. * Trend cards display one row per time bucket with a column per series. * Group labels render with the same badges, avatars, and icons used in the rest of Ravenna so values stay easy to recognize. **To switch views:** 1. Open the dashboard containing the widget. 2. In the view mode toggle on the widget header, select the **Table** icon. 3. Switch back to a chart at any time by selecting another view (Bar, Stacked Bar, Line, or Pie). Table view is available on both Metric and Trend cards. The selection is per widget, so different cards on the same dashboard can use different views. Enable **Compare Previous Period** on a Trend card to overlay data from the immediately preceding period on the same chart. Use the overlay to see whether a metric is improving or regressing against the equivalent prior window. The comparison window is derived from the dashboard's selected date range. For example, if the current range covers the last 7 days, the overlay shows the 7 days before that. **Best for:** * Week-over-week or month-over-month performance reviews * Spotting regressions after a process or staffing change * Validating the impact of new workflows, agents, or SLAs **How it appears:** * On line charts, the previous period appears as a dashed, semi-transparent line behind the current series. * On bar charts, the previous period appears as faded bars next to the current bars. * The legend continues to reflect only current-period series. **To enable it:** 1. Open or create a Trend card. 2. In the configuration panel, toggle **Compare Previous Period** on. 3. Save the card. The overlay updates automatically as you change the dashboard's date range. Comparison overlays are only available on Trend cards. They follow the dashboard's active date range, so changing the range also shifts the comparison window. ### Exporting widget data Download the underlying data for any Metric or Trend card as a CSV file. Use exports to share data with stakeholders who do not use Ravenna, run additional analysis in a spreadsheet, or attach point-in-time snapshots to reports. On any dashboard, select the actions menu in the top-right corner of the widget. Select **Export** from the menu. Ravenna fetches the widget's data using the dashboard's current date range and filters. The browser downloads a CSV named after the widget (for example, `ticket_volume_export.csv`). Open it in any spreadsheet tool. The exported file reflects the data currently driving the widget: * **Metric cards without a group** include a single value row. * **Metric cards with a group** include one row per group with the group label and aggregated value. * **Trend cards** include one row per time bucket with a column for each series. Exports honor the dashboard's active date range and filters, so refining either before exporting controls the rows that appear in the CSV.