Mean Time to Resolve

A quick first reply does not mean the customer's problem is solved, and mean time to resolve examines the span until a defined resolution. It is commonly shortened to MTTR, an acronym with several other meanings.

Atlassian warns that MTTR can mean repair, recovery, resolve or respond, and those clocks do not necessarily start or stop at the same events, so spell out the measure before comparing teams or vendors. For a support queue, a common approach is ticket creation to resolution, and Zendesk distinguishes first resolution from latest full resolution when a ticket is reopened, so decide how reopening affects the measure.

A fictional service desk resolves 500 tickets with a combined 2,000 elapsed hours under its chosen clock, so the arithmetic mean is four hours, which does not mean every customer waited four hours. Averages can hide long delays, since a few tickets may be fixed in minutes while a complex issue takes weeks, so show a median, distribution or age of unresolved work alongside the mean.

Define the cohort by resolved cases in a period or by cases created in a period, because the two views answer different questions, and a resolved-case measure can temporarily improve while old open cases remain untouched. Decide whether the clock uses calendar or business hours, since a Friday night ticket may count differently under each, and let the service promise and customer's experience guide which view is reported.

A fictional utility provider responds to an outage quickly but takes two days to restore service, so first response and resolution are separate measures and a friendly acknowledgement should not close the clock. Some incident teams define full resolution to include steps that prevent recurrence, beyond restoring service, and Atlassian uses that distinction in its incident metric guidance, while support tickets may use a narrower, customer-confirmed outcome.

A reopened ticket may signal that the first answer failed, so track repeat contacts or reopenings instead of rewarding early closures, because a shorter number achieved by marking unresolved work complete is false progress. Segment by issue type and severity too, since password resets and complex billing disputes have different expected durations and an overall average can change merely because the mix of tickets changes.

Waiting for a customer can lengthen calendar time without using agent labour, so record the waiting status but do not quietly subtract it from a published metric, and explain whether the customer-facing clock pauses. A fictional telecom team routes network complaints among several groups and finds most delay occurs between handoffs, not during repair, so clear ownership reduces elapsed time without asking technicians to skip checks.

Resolution quality matters, because a temporary restoration may fail again, so track recurrence and customer confirmation so the metric does not reward fragile fixes. For safety or legal issues, speed is only one goal, because required investigation, evidence or approval may take time, and a low MTTR does not excuse incomplete work.

Use a stable timestamp source, since ticket creation, incident detection and first customer report can differ, and state which start event the system captures and how missing timestamps are handled. Exclusions can distort results, because if escalated cases are removed from one team's report the number may improve without better service, so keep transfer and cancellation rules visible.