How to calculate Average Resolution Time in Zendesk using AirOps

Average Resolution Time: What it is, why it’s important, and how to (easily) measure it.

How to calculate Average Resolution Time in Zendesk using AirOps

What is Average Resolution Time?

Average Resolution Time tracks the total time it takes for a ticket to get resolved from the moment it's opened. It’s a critically important metric because it’s closely tied to customer satisfaction.

In addition to managing to your desired service levels, this metric can be instrumental in forecasting ticket volumes and optimizing staffing levels. You can track Average Resolution Time for individual customer service agents or for the customer service function as a whole.

Average Resolution Time is also sometimes referred to as Average Time to Resolution or Average Ticket Resolution Time.

How to calculate Average Resolution Time

Calculate your customer service function’s Average Resolution Time by dividing the total resolution time* for all tickets solved in a specific time frame by the total number of tickets solved in that same time frame.

Average Resolution Time = Total resolution time for all tickets solved ÷ Number of tickets solved

* A note on total resolution time & Zendesk

If you’re using Zendesk to measure and monitor the performance of your customer service function, note that total resolution time at the ticket level is called full resolution time. If you’re looking for the sum of all resolution times for all tickets inside of Zendesk, that’s the metric name to look for.

Some organizations opt to subtract the time a closed ticket spent on hold or pending when calculating their Average Resolution Time. This is because customer service agents don't have control over the time a ticket spends pending (waiting on the requestor) or on hold (waiting on someone other than the requestor). Zendesk even has a support article that you can use to set up a report that excludes those tickets: Explore recipe: Average ticket resolution time without pending or on-hold time.

Here are some best practices to use when measuring Average Resolution Time:

  • If you're trying to measure Average Resolution Time for individual customer service reps, don’t include pending and on-hold time.
  • If you're trying to measure Average Resolution Time in order to see how it correlates with customer satisfaction, do include time on hold (people generally aren’t too thrilled if they get stuck on hold for ages).
  • Sometimes you even may want to include pending time, for example, if the requestor of the ticket is internal or you have SLAs for total resolution time.

Track Average Resolution Time alongside other important customer service metrics

On its own, Average Resolution Time doesn’t provide a complete picture of your customer service team’s performance. If you want to get a real handle on your team’s performance, Average Resolution Time should be tracked alongside other customer service metrics.

And while tools like Zendesk are awesome (obviously), our experience has shown that customer service reps (CSRs) and managers find it way more convenient and powerful to access and analyze data in operating documents that they already know and love, like Google Sheets ❤️. 

Getting high-quality data into an operating document like a GSheet isn’t necessarily easy, though. Especially if it requires you to spend hours manually downloading CSVs from different sources and copying the data into a rickety, VLOOKUP-filled spreadsheet 👎. Plus, this process doesn’t allow you to codify and document business logic, like the Zendesk recipe for removing on-hold and pending time when calculating Average Resolution Time that we mentioned earlier. 

Luckily, there’s an easier way: AirOps.

A screenshot of a Google Sheet template created with AirOps that includes service metrics.

With AirOps, business teams can create amazing sheets, docs, and tools using data from Zendesk, Hubspot, and countless other data sources. In addition to Average Resolution Time, AirOps makes it easy to track other customer service metrics such as:

… and more! Get in touch with our team to learn more and get started!

Use AirOps to prepare and sync business data to Google Sheets, Notion, & more

Book a demo

While Average Resolution Time is an important metric for customer service teams to measure, you need a more complete picture if you want to measure the team’s overall performance and effectiveness. 

Frequently asked questions about Average Resolution Time

Should my organization track Average Resolution Time?

Like so many data-related questions, the answer to this one is “Yes, and… ” There's no one answer to whether or not your organization should measure Average Resolution Time. Ultimately, it depends on your specific goals and needs. 

Average Resolution Time is a helpful metric for measuring the performance of customer support teams and individual CSRs. It can also show you where there’s room for improvement with your internal workflows and processes. If those benefits will help your organization reach its customer service goals, you should track Average Resolution Time.

Like many other KPIs, however, it’s always a good idea to assess Average Resolution Time alongside other related customer service metrics in order to provide context. If you’re trying to improve your organization’s CSAT scores, for example, Average Resolution Time is a helpful metric to look at. Customers don’t want to wait ages to get their issues resolved, and high Average Resolution Times can quickly lead to low CSAT ratings.

Additionally, it’s worth mentioning that an organization might choose to measure two different Average Resolution Time metrics based on different use cases: 

1. With on hold and pending time counted if you're trying to measure Average Resolution Time in order to see how it correlates with customer satisfaction.

2. Without on hold and pending time counted when tracking Average Resolution Time for individual customer service reps.

If you’re going to track different Average Resolution Times for different use cases, you also want to make sure that the business logic behind those metrics definitions is well-documented. 

This can be a deceptively tricky task – many data catalogs aren’t very business-user friendly. A tool like AirOps that makes it easy to search, prepare, document, and sync trusted data sets is a great way to make sure everyone is on the same page regarding metrics definitions. 

What is a “good” Average Resolution Time?

What constitutes a “good” Average Resolution Time depends on two main factors:

1. Your customers’ expected resolution timeframes.

When resolving an issue, it's important to keep in mind the timeframe that your customer expects to receive a solution. This will help you manage expectations and ensure that all parties involved are on the same page.

Timeframe expectations vary depending on the type of customer and the type of resolution they are looking for. Some customers may expect a quick resolution while others may want a long-term solution. Some customers might expect to be contacted immediately while others are completely fine waiting a bit longer, especially if the issue is complicated.

2. The complexity of the issue and how long it will take to solve it.

Some fixes take longer than others and you don’t want to push for quick resolutions to complicated issues, especially if doing so will cause more problems in the future. 

In order to set reasonable expectations, encourage CSRs to develop an understanding of exactly what’s involved in resolving an issue, especially if it’s not something the customer service team can directly solve. A great way to customize Average Resolution Time to your organization is to customize the metric definition. For example, you can exclude tickets tied to incidents (e.g., waiting on engineering to fix a bug) or remove on-hold time from the total. 

However, a low Average Resolution Time isn’t always a good sign. Here’s why ⬇️.

What are some of the limitations of Average Resolution Time?

It’s important not to take quick resolution times at face value. 

For example, it’s not uncommon for reps to tag tickets as resolved (when they aren’t resolved) in order to meet their targets. If resolution times are high but customer satisfaction (CSAT) rates are low, you need to investigate further. 

The opening and reopening of tickets is another consideration to keep in mind when tracking Average Resolution Time. The number of times a ticket is reopened can be a helpful secondary metric, though Zendesk has its own way of calculating this metric for a ticket that’s been reopened: It calculates the metric as if the ticket was never closed in the first place. 

How can I improve my organization’s Average Resolution Time?

Resolution times can vary depending on a variety of factors, such as the type of support ticket, the severity of the issue, and the location or country from which it was submitted. 

With that being said, here are some things you can do to improve your customer service team’s Average Resolution Time:

  • Use available resources wisely, such as assigning different support reps specific areas of expertise or using chatbots or other automated tools where possible.
  • Create specific queues for different types of tickets (e.g. development issues vs. customer service inquiries) in order to reduce wait times for certain types of tickets.
  • Utilize automated systems that can quickly identify and prioritize support tickets based on their severity.
  • Make use of live chat and telephone support when possible in order to reduce wait times.
  • Provide clear instructions and screenshots whenever possible so that users understand what they need to do in order to resolve an issue.
  • Hire additional CSRs and/or offer 24/7 customer service via email or social media if necessary.
  • Before a change or new feature is introduced, estimate how many tickets will need to be resolved in order for a particular change or new feature to go live and set goals for your target resolution times accordingly.
  • Use software that can monitor ticket status and provide alerts when certain thresholds are reached (e.g., when a ticket has been open for more than 24 hours).
  • Regularly review progress towards targets, adjust timelines as needed, and take any additional steps needed (such as resubmitting high-priority tickets if required) in order to hit deadlines.

Try a few of these suggestions out and monitor how the changes impact your Average Resolution Time.

What does an increase in Average Resolution Time mean?

If you notice an increase in Average Resolution Time, there’s probably an underlying problem that needs to be addressed. Here are some common scenarios that lead to increases in resolution times:

  • Your customer support team needs additional training to handle more complex issues.
  • There’s not enough coordination and communication between the customer support team and other teams that will be solving the issue (e.g., engineering or product).
  • There are multiple resolution times during the lifecycle of a ticket (e.g., First Resolution Time, which tracks the time between when a ticket was first created and the first timestamp marking the ticket as resolved, and Full Resolution Time, which tracks the final or most recent marking as solved).

Recommended resources related to Average Resolution Time

15 common customer service metrics and how to use them

How to measure Customer Satisfaction Score (CSAT) in Zendesk using AirOps

How to measure average response time in Zendesk using AirOps 

How to design a winning metrics framework

Published on Sep 14, 2022 by AirOps Team