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Track service desk performance through prepackaged dashboards and custom analytics. Monitor lifecycles, performance, customer satisfaction, 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.

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Accessing analytics

Navigate to analytics dashboards from the left sidebar to explore service desk data.
1

Access analytics

From your , click Analytics in the left sidebar.
2

Select dashboard type

Select from seven prepackaged dashboards: Tickets, Agents, Efficiency, Forms, SLAs, CSAT, and Knowledge Base Analytics, or create custom dashboards.
3

Apply filters

Use date ranges, filters, and other parameters to focus on relevant data.

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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:
1

Click a chart value

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.
2

Search and sort

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.
3

Open a ticket

Click any row to open that ticket in a new tab so you keep your place on the dashboard.
4

Open the full tickets list

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.

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Organizing dashboards

Keep analytics organized using collections. Group related dashboards by team, business function, or reporting purpose as your analytics needs grow.

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Creating collections

1

Navigate to analytics

Open Analytics from your workspace sidebar
2

Create collection

Click New and select Folder to create a collection
3

Name and describe

Provide a clear name and description that explains what dashboards belong in this collection
4

Nest collections

Optionally set a parent collection to create hierarchical organization

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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 and drop

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.

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Prepackaged dashboards

Seven ready-to-use dashboards provide immediate insights into service desk operations.

Tickets

Monitor volume, status distribution, and lifecycle patterns.
  • Overall ticket counts and trend indicators
  • Active tickets requiring attention
  • Tickets awaiting assignment
  • Tickets being worked on by
  • Successfully completed tickets
  • Time-series visualizations of creation patterns and 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 and channels
Analyze team performance and AI automation impact.
  • Workload distribution across your team
  • Human vs automated resolution rates
  • Efficiency gains from participation
  • Visual breakdowns of ticket assignments
  • Historical analysis of agent activity and AI impact
Learn more about AI agents and ticket 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 perform across your service desk.
Learn more about creating and managing forms
Monitor performance across all 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 and setting priorities
Track customer satisfaction patterns and feedback trends.
  • Overall customer satisfaction scores across all tickets
  • Satisfaction scores by levels
  • Time-series analysis showing satisfaction trends
CSAT data is only available for where customer satisfaction surveys have been enabled and tickets have been resolved with customer feedback.
Learn more about customer satisfaction surveys
Measure effectiveness and article performance to understand how documentation impacts ticket deflection and user self-service.
  • Total users interacting with knowledge base articles
  • 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

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Custom dashboards

Create tailored dashboards using the flexible dashboard builder for specific reporting needs beyond the prepackaged dashboards.

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Building custom dashboards

1

Create new dashboard

Click Add Card from the top left of any analytics page to start building a custom dashboard or widget.
2

Select card type

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
3

Configure basic settings

Provide a Name and Description for your widget to make it easily identifiable to your team.
4

Select data source

Select your data source:
  • Tickets: Primary service desk data including , , assignments
  • Messages: message data for analyzing communication patterns
  • Workflow Runs: execution data for tracking automation performance
5

Configure analytics settings

Set up your widget with grouping, aggregation, and time interval options based on your selected card type and data source.

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Card types

Metric cards

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 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.

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Data sources and grouping

Group your analysis by key dimensions depending on the data source you select.

Tickets data source

Primary service desk data including , , and assignments.Status and assignments
  • Assignee
  • Requester
  • Author
Categories
  • levels (Urgent, High, Medium, Low)
Customer feedback
  • (1-5 stars, plus a “No CSAT” bucket for tickets that have not been rated)
Service level agreements
  • SLA: Group tickets by the 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
  • Approved
  • Declined
  • Archived dates
AI involvement
  • Whether human or participated in ticket resolution
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
  • Response categorization
  • Time-based analysis
execution data for tracking automation performance and reliability.Workflow details
  • Workflow name
  • Run status (completed, failed, running)
Related ticket data
  • Assignee
  • Source
Workflow Runs only supports count aggregation. Average, sum, min, and max aggregation types are not available for this data source.

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Configuration options

Configure how your custom dashboard aggregates and displays data.

Aggregation

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: 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.

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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.
1

Open the widget menu

On any dashboard, select the actions menu in the top-right corner of the widget.
2

Select export

Select Export from the menu. Ravenna fetches the widget’s data using the dashboard’s current date range and filters.
3

Save the file

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.
Last modified on July 20, 2026