Reporting & Dashboards
Reporting covers the historical and aggregate view of how your support operation is performing. Six built-in report types cover the metrics that matter across most support teams, custom dashboards let supervisors save the views they use most, and AI analytics break down bot performance, credit usage, and return on investment. This guide covers the built-in reports, how to build custom dashboards, how breakdowns and filters work, and how to export or schedule reports for stakeholders.
Overview
Reporting lives at Admin > Reports and complements the real-time supervisor dashboard. The supervisor dashboard answers "what's happening right now?" Reporting answers "how did we do, and how has that changed?" Both matter; both are available.
Data is rolled up at multiple grain levels (hourly, daily, monthly) so reports load quickly whether you're reviewing yesterday or the past quarter.
Built-in Reports
Six standard report types cover most of what a support operation needs to track.
Agent Performance
Individual agent metrics over a chosen time range: conversations handled, first response time, resolution time, CSAT scores, adherence to business hours, ticket status distribution, and closure reasons. Useful for one-on-ones, capacity planning, and identifying agents who need support (either extra load or coaching).
Team Summary
Team-level aggregates for comparing workload and throughput across teams. Conversation volume, average resolution time, SLA compliance rate, peak queue depth, agent utilization. Good for identifying which teams are overloaded or under-resourced and for comparing performance trends between teams.
Queue Analytics
How the queue behaves over time: average and peak depth, wait times, abandonment rates, time-of-day patterns, day-of-week patterns. Useful for staffing decisions and for identifying when routing strategies need tweaking.
SLA Compliance
Tracks whether tickets are meeting defined service level agreements for first response and resolution. Shows compliance percentage, breach count, tickets approaching breach, and patterns by team, priority, or channel. Essential for customers with contractual SLA commitments.
Conversation Volume
Total conversations started, resolved, abandoned, and transferred over the chosen period. Broken down by channel, team, and time. The volume view is how you answer "is our support load growing?" and "which channels are driving that growth?"
CSAT Summary
Customer satisfaction scores rolled up across all channels and teams. Average score, distribution of ratings, comments sentiment, trends over time, and breakdowns by team or agent. See CSAT Surveys for how CSAT data is collected.
AI Analytics
A dedicated AI analytics view covers metrics specific to AI-handled conversations:
- Bot resolution rate — percentage of bot conversations that resolved without escalating to a human. Broken down per bot so you can see which bots are working well.
- Credit usage by feature — how many credits bot conversations consumed vs. AI analysis vs. ticket description generation. Helps identify where AI spend is concentrated.
- Topic performance — which topics the bot handles well (high resolution, positive feedback) and which it struggles with (high escalation, negative feedback). The clearest signal for where to improve knowledge base content.
- Return on investment — a headline metric showing estimated agent time saved by bot resolutions, alongside credit cost, for a net impact view. Useful for leadership reporting and for justifying continued AI investment.
- Escalation reasons — breakdown of why bots escalated: customer request, out-of-scope, knowledge gap, guardrail triggered. Guides where to invest next (more KB content, tighter scope, better guardrails).
Custom Dashboards
Supervisors and admins can build custom dashboards that combine multiple reports with persistent filters. Common examples:
- A weekly ops review dashboard combining Conversation Volume, Queue Analytics, and SLA Compliance for the past week
- A team lead's daily dashboard showing Agent Performance for just their team plus the relevant CSAT Summary
- An AI effectiveness dashboard combining bot resolution rate, credit usage, and topic performance
- An executive dashboard showing trend charts for volume, CSAT, and SLA compliance month-over-month
Custom dashboards are saved per user and can optionally be shared with other users or made tenant-wide.
Filters and Breakdowns
Every report can be filtered and broken down by:
- Time range — last 24 hours, last 7 days, this month, last month, custom range
- Team — one team, multiple teams, or all teams
- Channel — chat, email, web form, or combinations
- Agent — specific agents or all
- Priority — Low, Normal, High, Urgent, or combinations
- Tags — any tags applied to tickets
- Custom fields — any custom field value. "Average resolution time for Enterprise-tier customers" is a filter combination, not a separate report.
Filters compose: a report showing "SLA compliance for the Billing team's urgent tickets from Enterprise-tier customers in the last quarter" is four filters applied to the SLA Compliance report.
Exports
Any report or dashboard can be exported as CSV (for external analysis in spreadsheets or BI tools) or PDF (for stakeholder reports). Exports respect the current filters so what you see is what you export.
For data teams that want the raw data, a structured export of conversation, ticket, and event data is available for customers on plans that include API access.
Scheduled Reports
Any report or custom dashboard can be sent on a schedule — daily, weekly, monthly — to a list of email recipients. Useful for:
- Weekly team performance to the support lead every Monday morning
- Monthly SLA compliance to the customer success team
- Quarterly CSAT trends to leadership
- Daily volume summary to an ops channel via email-to-Slack bridge
Scheduled reports include the filter configuration, so each recipient gets the same view they'd see if they opened the report manually.
Best Practices
- Use the supervisor dashboard for real-time, reports for historical. They answer different questions; don't try to make one do the other's job.
- Break down by custom fields that matter for your business. The default breakdowns are a starting point; custom fields like subscription tier, region, or product area are where real operational insight comes from.
- Schedule the reports stakeholders actually read. An unopened weekly report is a waste; a well-targeted monthly summary to the right people can change staffing decisions.
- Pair AI analytics with knowledge base reviews. Bot topic performance data points directly at what content needs to be added or updated — act on it rather than just observing it.
- Track trends, not just snapshots. "This week's SLA compliance is 94%" is less informative than "SLA compliance has dropped from 98% to 94% over the past three weeks." Reports support time-series views for exactly this reason.
Related Topics
- Supervisor Dashboard — the real-time operational view that complements historical reporting.
- CSAT Surveys — how customer satisfaction data is collected and feeds into CSAT Summary reports.
- AI Credits & Billing — credit usage details that feed into the AI analytics view.
- Custom Fields — configuring fields that can be used as breakdowns and filters in any report.