Reopen rate is the percentage of resolved support tickets that are reopened by the customer within a given window. It measures how often issues marked closed were not actually resolved the first time.
Reopen rate is calculated by dividing the number of reopened tickets by the number of resolved tickets over a period, often within a fixed window such as seven days after closing. It is one of the few support metrics that is hard to game, because it surfaces the gap between marked resolved and actually resolved. A team can lower its average resolution time by closing tickets aggressively, but doing so usually pushes the reopen rate up.
This is exactly why reopen rate is a useful check on resolution quality. A low average resolution time paired with a high reopen rate means issues are being closed, not solved. Aide, the agentic AI platform for customer experience, treats reopen rate as a guardrail on automation: a true resolution is one that stays resolved.
In practice, Aide reads reopen rate alongside resolution rate, with intent coverage as the supporting diagnostic. An intent only counts as covered when its automation has been tested on real past conversations and verified, which is what keeps reopens from rising as coverage expands. Recurring issues stay visible in the intent breakdown, so the team learns why customers come back instead of letting automation bury the pattern. A falling reopen rate is real resolution, not deflection.
Frequently asked questions
- How is reopen rate calculated?
- Divide the number of reopened tickets by the number of resolved tickets over a period, often within a fixed window after closing such as seven days. The result is expressed as a percentage.
- Why does reopen rate matter?
- It exposes the difference between tickets marked resolved and tickets actually resolved. A high reopen rate alongside a low resolution time usually means issues are being closed prematurely rather than solved.