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Time to Resolution(TTR): Definition, Affecting Factors, How to Interpret

Many theories, debates, and guidelines exist about measuring time to resolution. The metric is old and seemingly clear to everyone.  

So, since the first customer in the world left a request and the first support service, the Time to Resolution (TTR) metric timer has been running. And after that, there was no more peace in the support leads. 😀



Time to resolution is the time elapsed from when a problem or issue is reported until it is resolved or closed.

Factors Affecting TTR

  1. The complexity of the request. The more complex the task or request, the more time it takes to resolve it. Therefore, you must grade problems in your whitepaper and consider different time values to resolve them.
  2. Availability of resources. A simple and fast tool for accepting a user's request, the availability of qualified and educated staff significantly affects the speed of problem resolution. Subjectively, TTR begins to be measured when your user only considers contacting your support. And if this process is complicated and inconvenient. If you use the right software, it's almost a success.
  3. The severity of the problem. This refers to the prioritization of requests. The human resources required to process a request are limited and finite. And when there can be too many requests at one time, running to extinguish the fire is necessary where a considerable need has already been created. All requests are essential, but there will always be more priority than all the others.
  4. Effectiveness of the process. The scenario of the problem-resolution process should be clearly defined and understood. The time to resolution is often delayed precisely because the team needs to learn how to proceed when processing requests.
  5. Communication. Effective communication between team members, stakeholders, and customers can speed up problem-solving. 
  6. Technical dependencies. Dependence on external systems, software updates, or third-party services can increase the time to resolution.
  7. Experience and skill level. The experience and skill level of the people involved in the problem-resolution process can affect the speed of problem resolution. Service department specialists should be involved in your product as much as possible and know all the subtleties and nuances of the functionality to ensure that user support is at a high level.

How to Interpret Time to Resolution Metric

Interpreting a TTR score involves understanding the context of a particular situation and comparing it to pre-defined goals or benchmarks. Here are some considerations: 

🟢 Benchmarking. Compare TTR to industry standards or internal metrics to assess performance.

Industry standards may need to be updated. It is rarely possible to fit into this framework. Plus, they could be more flexible. So, it's good to consider them and study the acceptable metric values for different sectors but not try to achieve an absolute result, neglecting quality.

However, studying historical performance within the company is a good practice. This allows you to review the team's performance over different periods, the productivity of individual employees, and so on.

To do this, we recommend using the Time Between Statuses app.

Everything works very simply. After installing the app, you configure your work schedule (to exclude non-working hours from the calculation):

ttr 1.png

Next, you create a status group, and the time spent within it will be the calculated TTR metric. A little tip: exclude the Waiting for customer status from the calculation. You can't influence this external factor, so don't consider it. For comparison, you can create two status groups for yourself - with and without this status. You will see how the values in the TTR column will change dramatically.

You can also set time limits for warning and critical time limits. To visually highlight problematic or critical TTR values.

ttr 2.png

Based on the settings, you can quickly generate a report in the Support project. All you have to do is select the time margin and other parameters you need. The column titles will also show you the Average time to resolution value.

ttr 3.png

It is also essential to evaluate the TTR value of individual Assignees responsible for processing customer requests:

ttr 4.png

🟢 Service Level Agreements (SLAs). Evaluate TTR against SLAs or goals agreed upon with stakeholders. 

🟢 Trend analysis. Track TTR over time to identify patterns and trends and adjust to improve performance. 

For example, in the Time Between Statuses app, try generating histograms to compare two months to understand a trend visually, repeating patterns if the time to resolution is increasing depending on the number of requests, etc.

ttr 5.png

ttr 6.png

🟢Root cause analysis. Investigate cases where TTR exceeds expectations to identify root causes and areas for improvement. 

🟢Customer satisfaction. Analyze customer feedback and satisfaction with TTR to ensure timely problem resolution meets customer expectations. 

🟢Continuous improvement. Use time to resolution as a performance metric to drive continuous organizational improvement. 

In summary, time to resolution is a key performance indicator used to measure the effectiveness of problem-resolution processes. By understanding the factors that influence TTR and interpreting the metric in the context of organizational goals and benchmarks, teams can identify areas for improvement and increase overall operational efficiency.

To research and effectively respond to changes in TTR, try the Time Between Statuses app:

  • Easy creation of status groups
  • Possibility to exclude non-working hours from report calculation
  • Email notifications when time limits are exceeded
  • Get the Average time of the created status group
  • Get the visual color marks on the issues with the exceeded time limits (for both Warning and Critical Time)
  • Export data as XLSX or CSV files 

Free 30-day trial - Time Between Statuses for Jira Cloud

Free 30-day trial - Time Between Statuses for Jira Data Center

See you in the SaaSJet team! 😊



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