Average Resolution Time KPI

Average Resolution Time (ART) is a critical performance indicator that reflects the efficiency of customer service operations.

It directly influences customer satisfaction, operational efficiency, and financial health.

A lower ART indicates a streamlined process, leading to enhanced customer loyalty and retention.

Conversely, a higher ART can signal inefficiencies that may erode trust and increase operational costs.

Organizations that effectively track this metric can make data-driven decisions to optimize workflows and improve service delivery.

By focusing on ART, companies can align their resources strategically to meet customer expectations and drive better business outcomes.

How Average Resolution Time Connects to Your Strategy

Average Resolution Time is a home metric for two KPI groups. In Support Ticket Management it ranks first of sixty-one members, and in Legal Department Efficiency it also ranks first of fifty-four. In both it holds the top priority slot, so treat it as a lead operational metric rather than a secondary one. In all, it appears in twelve KPI groups across support, legal, and managed services contexts, but those two are where it anchors the set.

Its balanced scorecard perspective is internal, and its role is leading: it moves early and pulls other outcomes behind it. In Support Ticket Management the headline co-metrics that sit closest to it are First Contact Resolution Rate, First Response Time, Resolution Rate, and SLA Compliance Rate, with Customer Satisfaction Score (CSAT) carrying the customer view. In Legal Department Efficiency it leads a different cast: Litigation Win Rate, Legal Department Operational Efficiency, then Legal Matter Cycle Time and Contract Turnaround Time on the workflow side, with Internal Client Satisfaction Rate as the experience check.

The genuine tension is speed against quality. Driving Average Resolution Time down can push agents to close tickets before the underlying issue is fully fixed, which shows up as a rising Reopened Ticket Rate and a softening First Contact Resolution Rate, and ultimately a lower CSAT. In the legal group the same pull applies against Internal Client Satisfaction Rate: a matter closed fast but incompletely satisfies no one. Read this KPI next to those co-metrics, never on its own.

Measuring Average Resolution Time in Practice

The formula is total time to resolve all tickets divided by the number of resolved tickets, which looks simple until you fix the clock. The data usually lives in the ticketing or case system, joined to agent and queue tables, but the honest join depends on decisions the raw timestamps do not make for you. Decide first what resolution means: the moment an agent marks a ticket solved, the moment the customer confirms, or auto-close after a waiting period. Decide when the clock starts, at ticket creation or at first agent assignment, and whether it stops during time spent waiting on the customer or a third party. Decide business time versus calendar time, since a ticket opened late Friday will look very different under each. Decide how reopens are handled: a reopened ticket that resets to solved a second time can either extend the original duration or spawn a fresh record, and the two conventions produce different averages from identical events.

Segmentation matters more than the headline. A single blended average hides priority, channel, and ticket type, so a shift in mix, more low-effort tickets or fewer complex ones, can move the number while nothing about actual speed changed. Report the metric split by priority and by category, and watch the median alongside the mean, because a handful of very long-running tickets will drag an average upward and mislead anyone reading it as typical.

The instrumentation pitfalls are specific. Bulk-closing stale tickets injects long durations that distort the average. Timezone and business-calendar misconfiguration silently inflates or deflates elapsed time. Agents who reopen and re-solve to hit a target can make the number look good while the underlying work degrades, which is exactly why this metric should never be graded without First Contact Resolution Rate and Reopened Ticket Rate beside it.

Common Pitfalls

Many organizations overlook the impact of ART on overall customer satisfaction, leading to missed opportunities for improvement.

• Failing to analyze root causes of delays can perpetuate inefficiencies. Without understanding why resolutions take longer, teams may struggle to implement effective solutions.
• Neglecting to invest in training for customer service representatives often results in inconsistent performance. Staff lacking proper skills may take longer to resolve issues, frustrating customers.
• Overcomplicating the resolution process with unnecessary steps can lead to longer wait times. Streamlined workflows are essential for reducing ART and enhancing customer experience.
• Ignoring customer feedback can mask underlying issues. Without insights from customers, organizations may miss critical pain points that extend resolution times.

Improvement Levers

Improving Average Resolution Time requires a focus on process optimization and employee empowerment.

• Implementing a robust knowledge management system can provide representatives with quick access to information. This reduces the time spent searching for answers and accelerates resolution.
• Regularly reviewing and refining workflows helps identify bottlenecks. Streamlining processes can significantly cut down on unnecessary steps that prolong resolution times.
• Investing in advanced analytics tools allows teams to track performance metrics in real-time. This data-driven approach enables proactive adjustments to improve ART.
• Encouraging cross-department collaboration fosters a more holistic approach to customer issues. When teams work together, they can resolve complex problems more efficiently.

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Average Resolution Time Benchmarks

We have 2 relevant benchmarks in our benchmarks database.

Browse the Top Benchmarked KPIs in Support Ticket Management

Reading the Benchmarks for Average Resolution Time

The two tracked figures for this metric both come from Moveworks, one framed as a threshold and one as an average, and both drawn from internal IT help desk tickets across the organizations in that dataset. Before trusting any external number here, a customer should verify three things. First, the population: the Moveworks figures describe internal IT help desk work, so applying them to customer-facing support or to a legal department is a construct mismatch, since a help desk ticket and a legal matter start and stop on very different clocks. Second, whether the source counts calendar time or business hours, because that choice alone can move the figure without any real change in performance. Third, what the source treats as resolution, in particular whether reopened tickets reset the clock. None of these are visible from the headline number, so a Moveworks average is a reference point for internal IT, not a target for every group this KPI belongs to.

OKRs That Use Average Resolution Time

Average Resolution Time works cleanly as a key result under the Support Ticket Management objective optimize operational efficiency to manage ticket workload without sacrificing quality. Here the direction is downward: cut average resolution time across ticket types while holding closure and utilization steady. The objective names this KPI as a key result directly, and its own wording, without sacrificing quality, is the guardrail, so pair the reduction with First Contact Resolution Rate and Reopened Ticket Rate so speed gains are not bought with rework.

It ladders equally well to the Legal Department Efficiency objective enhance legal process efficiency to accelerate service delivery and reduce bottlenecks. In that framing the key result is again directional, shortening the time to resolve matters, sitting alongside Legal Matter Cycle Time and Contract Turnaround Time. Keep any figure your team commits to as an illustrative goal you set for a period, not as a benchmark, and let Internal Client Satisfaction Rate confirm that faster did not mean worse.

See OKR Examples for Support Ticket Management

KPI Categories

This KPI is associated with the following categories and industries in our KPI database: