The 5 Metrics Every IT Manager Should Measure (And 90% Ignore)

There's a paradox in hotel IT team management: managers excel at solving technical problems but rarely measure their own team's performance with the same rigor. The result is lots of activity but little visibility into whether that activity is effective, whether it can be improved, or whether there are systemic issues requiring attention.

Without data, IT team management is reactive by definition. Each incident is treated as a unique event with no connection to historical context. Decisions about resources, training, or hardware investment are made by intuition rather than evidence.

Metric 1: MTTR (Mean Time To Resolution)

Mean resolution time is the most important metric for a support team. It measures how much time passes between ticket opening and closure. A high MTTR can indicate team capacity issues, lack of adequate tools, external dependencies slowing resolution, or prioritization problems.

MTTR should be segmented by incident type. It's not reasonable to compare the MTTR of a password reset (should be minutes) with a network infrastructure installation (can be days). The goal is to establish benchmarks by type and monitor whether the team improves or worsens over time.

Metric 2: Tickets per Site

Ticket volume per site reveals a lot about the state of each property's hardware and infrastructure. A site that consistently generates more tickets than others has a systemic problem worth addressing: older hardware, weaker network infrastructure, staff less trained in correct system use, or simply higher operational activity.

Metric 3: First Contact Resolution (FCR)

The first contact resolution rate measures what percentage of tickets are closed without needing escalation or additional interactions. A low FCR can indicate that technicians don't have the tools or access needed to resolve the problem on first contact, that initial ticket descriptions are insufficient, or that problems are being treated superficially rather than addressing root cause.

Metric 4: Recurring Incidents

The recurrence rate identifies problems that are closed but reappear because they weren't resolved at the root cause. A TV that restarts every week is a recurring ticket that should generate a permanent solution (device replacement, firmware update, configuration review) rather than successive temporary resolutions.

Recurring incident analysis is especially valuable for justifying hardware renovation investments. If a specific device generates 8 tickets per year at 30 minutes each, the annual support cost likely exceeds the price of a new device.

Metric 5: SLA Compliance

The SLA (Service Level Agreement) defines maximum response and resolution time by incident type. For a hospitality IT team, a reasonable SLA might be: critical incidents (payment system down, blocked access) within 1 hour; high priority incidents (room TV, public area WiFi) within 4 hours; normal incidents within 24 hours.

The percentage of tickets meeting the established SLA is a direct indicator of the team's capacity to respond according to the organization's expectations.

Conclusion

Measuring these five metrics requires tickets recorded in a structured system. Without that, the data doesn't exist. ITBoard automatically records timestamps for opening, assignment, update, and closure of each ticket, enabling calculation of all these metrics without additional team effort. The ITBoard dashboard shows the evolution of these metrics in real time, giving the IT manager a performance view that's impossible to obtain with WhatsApp or informal systems.