Mean Time to Resolution: What It is and How to Improve It
Mean Time to Resolution (or MTTR) is a useful metric that customer service teams can use to measure customer service efficiency. Generative AI can help you reduce MTTR.
Mean Time to Resolution (or MTTR) is a useful metric that customer service teams can use to measure customer service efficiency and understand how to improve customer service.
Is your team effectively resolving support requests? On average, how long is it taking them? And why?
For these reasons and more, your CSM team needs a way to track how effectively it is resolving customer requests around the clock.
To figure out how to do that, in the guide below we’ll be breaking down everything you need to know about MTTR and how it might help inform a better customer service experience.
What Is Mean Time to Resolution (MTTR)?
Mean Time to Resolution (MTTR) is a metric used in a variety of contexts, always to refer to the average time it takes to:
- Identify an issue
- Diagnose that issue, and
- Fix it
In the context of customer service, that simply means: how long does it take you to resolve a support request (on average) from the moment you receive it?
Depending on your business model, this can crisscross with your IT or development team, but the point and purpose remain the same.
A Note on the ‘R’ on MTTR
The acronym ‘MTTR’ is used for a variety of different terms, such as:
- Mean Time to Resolve
- Mean Time to Recovery
- Mean Time to Repair, and
- Mean Time to Respond
Each has different uses, so it’s important to clarify with your team which MTTRs you’re tracking and make sure everyone understands the difference between them.
Why Is Mean Time to Resolution So Important?
The higher your MTTR becomes the more damage it can cause an organization.
Think about it: if every time a customer reports an issue you take longer to resolve that issue, you’re becoming more and more inefficient.
The longer it takes an agent to resolve an issue, the less time that agent has to resolve other issues (or do anything else for that matter).
So, the higher your MTTR is in general across your entire team, the less productive your team is as a whole.
Whether you’re just dealing with return requests and order issues or you’re using a SaaS model and you have clients depending on the uptime of your software, in both cases the longer you take to respond the more damage you do to the lifeblood of your business: your customers.
In the latter SaaS example, it becomes ever more important to ensure that your MTTR is low as it can affect your customer’s entire ability to use your product.
In extreme cases, such as your website going down, you need to have systems in place that ensure you can:
- Find out about the issue ASAP, the moment it happens
- Get someone to resolve the issue quickly
- And notify customers who may have been affected
Tracking and improving your MTTR is all about doing just that– and as effectively as possible.
What Is a Good MTTR?
Your MTTR will be entirely unique to your business model, so it’s important to look at apples-for-apples comparisons when trying to gauge what your MTTR should be.
When it comes to SaaS and software-related issues, you’ll hear suggestions everywhere from 4 to 24 hours.
With that said, it depends somewhat on the type of issue, the software, and even the timezone if you’re located in a different timezone from the majority of your customer base.
How to Use MTTR to Improve Your CS Team’s Efficiency
Now, let’s talk about how to use MTTR to improve your team’s ability to serve your customers.
The first place to look is at the KPIs you’re monitoring in your CSM team.
- What are you monitoring currently that might help you get a ballpark of your MTTR?
- Does your business currently have any way to gauge its MTTR?
The truth is that there isn’t one thing alone that you can track to give you the full picture of your MTTR as a whole, as certain issues may complicate the data.
Once you have a clear picture of what your average MTTR is, it’s time to look at specific problems that might be giving you the most issues.
Maybe it’s taking your team too long to locate the right data on customers or general knowledge to aid those customers in finding a resolution.
Maybe they’re handling too many requests at one time, so having to jump from one request to another is leading to a loss of efficiency and mistakes jumping between customers.
Once you have an idea of your average MTTR, you can essentially reverse engineer to uncover issues that you may not have known were even there.
Just make sure to use a variety of metrics and KPIs to get a fuller picture of the problem before jumping to conclusions.
Use Gleen AI to Put Your MTTR Into Hyperdrive
MTTR is a metric that directly reflects your ability to serve your customers, so it’s a valuable metric to keep tabs on.
Taking steps to improve your MTTR might seem daunting, but with the power of generative AI, it just got a whole lot easier.
With Gleen AI, you can reduce resolution times by way of better access to 24/7/365 support via all your customer support channels.
In addition, you can use Gleen AI to assist customer support agents. Gleen AI can search thru your knowledge base and playbooks, write first drafts of customer responses, and provide relevant links. Your agents can then revise these responses as needed.