Service Team Performance & AI Usage Dashboard

The Service Team Performance & AI Usage dashboard gives service leaders a clear read on how the support team performed over the last 30 days, against the prior 30 days so trends are obvious. It pairs throughput and quality metrics with per-technician AI adoption, so you can see who’s getting leverage from Thread’s AI and who hasn’t started. Open it from Thread admin → General → Analytics.

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What this dashboard answers

• Are we closing more than last month, and faster?
• Is quality holding — reopens, single-touch resolutions, first-day resolution?
• Whose queue is going stale?
• Who are our top performers, on volume and on quality?
• Which technicians have adopted Super Magic, and which haven’t touched it?

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Filters and time windows

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What’s on the dashboard

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Service team performance summary

A team-wide scorecard comparing the current period against the previous one:

• Tickets closed and tickets touched, each with percent change
• First-day resolution rate
• Reopen rate
• Single-touch resolution rate
• Average response and resolution time
• Share of tickets touched by Magic AI and by Magic Agents
• Reassignment rate

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Service team performance scorecard

A per-technician breakdown showing, for each team member: tickets closed and touched, distinct customers helped, median response and resolution time, first-day resolution rate, reopen rate, average customer sentiment, average touches per closed ticket, active days, reassignments given and received, and how many tickets in their queue are going stale.

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Member Super Magic usage and insights

How individual technicians used Thread’s AI over the past 30 days — including Super Magic and AI-assisted time entry — with first and last use dates, so adoption and abandonment are both visible.

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Member AI usage with trend

A period-over-period view of AI adoption per technician: Super Magic usage, AI time entry usage, and total AI usage, each with the change from the prior period, plus a count of active AI users.

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Metric definitions

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Things to watch

• Closures made directly in your PSA can’t be credited to a technician. A PSA API limitation around impersonation means only closures made in Thread Inbox or Pods attribute to a member. Read closer metrics as a view of closures happening in Thread. See Things to know & gotchas.
• Volume without quality is a trap. Read tickets closed next to reopen rate and touches per closed ticket before naming a top performer.
• Stale counts are the most operationally useful column in the scorecard — they surface a queue problem before it becomes an escalation.
• Reassignments given and received tell different stories. High received can mean a specialist; high given can mean triage is misrouting.
• Data refreshes daily, so today’s closures generally appear tomorrow.

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Next steps