Do you want to read another blog post telling you how important Customer Support is for businesses like yours? Probably not.
Do you want a list of the most essential Customer Support metrics you need to be measuring? You might already have that.
But what you might want to look at is a practical benchmarking of these Customer Support metrics in order to assess where you stand in Customer Support and the kind of corrective action you need to take.
So, here goes our list of Customer Support metrics that we think actually matter and our benchmarks for the same. Some of these might seem obvious but, hey, the purpose here is to hand you a definitive idea from where you can assess your business.
It’s the time lapsed between a customer raising a support ticket and the first response from a support agent. If you are analyzing time to first response, it’s best to discount automated acknowledgements from your support automation systems because your customers know auto replies don’t mean somebody has had a real look at their issue.
Quick first responses show that you have started the process of resolution. Customers can tolerate imperfect products or services but they won’t tolerate a long wait time before getting the first response. Though the time to resolution might vary, the time to first response must be the least possible ranging from a few minutes to an hour at the most. A study by SiteBuilder conducted last year showed that about 21% SaaS companies replied to support queries in less than an hour. That’s a good benchmark.
Less than an hour.
A few hours or less than a day counting off-work times.
More than a day.
Getting into “The Good” zone is not really difficult since time to first response doesn’t actually entail a resolution of the query. And yes, this is generally applicable across all response channels. Chat, on the other hand, should be much faster. Think seconds.
Customer support query resolution should ideally be carried out at three levels. The first level involves scouring existing documentation and other resources. So your support agent should do perform this step and then reply to the query within the first hour. If the query is resolved with this, your resolution time also drops to less than an hour, which is awesome. But either way, customer support teams should prioritize first responses.
The proportion of support tickets resolved in the first reply or contact discounting requests which can’t be solved in one interaction.
A high rate of first contact resolution shows your customer support team is highly efficient. High efficiency is an attractive proposition not just in the SaaS industry but across all verticals. High levels of First Contact Resolutions have also been correlated with high levels of customer satisfaction. However, be sure to be calculating the Net FCR rather than the gross FCR because there will be a lot of support requests which can’t be resolved in one interaction.
More than 85%
Less than 60%
But then again, Net FCR is a tricky metric you should measure but do so with caution. FCR can be interpreted in multiple ways. A low FCR could also mean the product or service is top notch but then that would be true only if the number of support tickets is low. Net FCR negates some of these issues but even then it should be viewed alongside other Customer Support metrics rather than a standalone KPI.
It’s good to get a large number of support tickets which can actually be solved in the first interaction. Just makes the job easy. But significant improvement can be gained by ensuring that replies to single contact resolution queries are automated.
The total number of interactions a customer requires to close a support ticket.
A high Interactions Per Resolution means the customer, and obviously the support agent, had to take a lot of pains to get the support query resolved. This obviously is not healthy for any SaaS company.
Less than 4
More than 7
Time to Resolution is the total time it takes a resolve a customer support ticket. Both Time to Resolution and and Interactions per Resolution are related. Obviously if the Interactions per Resolution for a particular support ticket is just one, the Time to Resolution for that query will be equal to the Time to First Response, which would be the ideal scenario.
We have already said customers hate waiting. B2B customers have a slightly higher patience level with respect to support tickets than B2C customers because they mostly ask questions like that. Yet, slow resolution of tickets is a huge turn off. Support is one of the most important reasons companies reject deals in the SaaS environment which means your Time to Resolution is critical. A high number of interactions is a direct loss of productivity for the client and for you as well. So, if these two metrics are in the red zone for you, it’s likely clients won’t renew.
Less than 12 hours
More than 48 hours
Time to First Response, as already pointed out, can be improved by prioritizing new tickets and responding to them. Time to Resolution can be reduced by using resources from teams other than those dedicated to support as Buffer did. The company decided to target a Time to First Response lesser than 1 hour. Buffer also mandated that every employee spend at least 30 minutes on answering support queries everyday. Pushing engineers to the frontline of support can result in a drastic cutback in Time to Resolution. Even StatusPage does that. But that doesn’t mean every technical support query is routed to available engineers. Customer support should be segregated into multiple levels.
Ticket Density is a metric derived from Ticket Volume. Ticket Volume is a Customer Support metric that depends a lot on the absolute number of customers that a company has. A higher customer count could mean a higher number of tickets which prevents a proper benchmark from being ascertained for it. A ratio of the volume of tickets to the total number of customers is a better KPI for SaaS companies of all sizes.
Ticket Density can present a clearer picture of the amount of work that your Customer Support team is getting and if it’s a good number or not. It’s basically just a rough indicator of the number of tickets each customer is raising over a period of time. Support tickets sometimes also indicate the increasing engagement that customers are having with your product, which is a good thing in the end. However, each customer is encountering a problem every week with your product, it’s not delivering a good customer experience despite the high level of engagement.
Less than 0.5 per week
0.5-1 per week
More than 1 per week
A lot of B2B tickets tend to revolve around how-to-dos, like, setting up an integration with a third party application or features not working as expected by the customer and so on. These issues can easily be resolved with the help of Self-Support tools prior to being directed to real customer support agents. Self-Support tools are like widgets that can crop up before customers Points of Contact actually raise a ticket. The most common customer support issues can be listed in the widget with the solution to it. The upside is quick resolution without a contact obviously leading to higher customer satisfaction lower overhead for customer support teams.
A useful custom metric to broadly ascertain if your churned customers left because of bad customer support. Churn Customer Support Satisfaction is the difference between the average Time to Resolution for churned customers over their last year and the average Time to Resolution for customers who renewed over their last year. A negative score would mean that your customer support did well but a positive score would mean you need to look at your support strategy. Keep in mind that the Standard Deviation of the Time to Resolution for churned customers should be pretty low for this to work.
A very dominant number of SaaS customers switch vendors because of patchy customer support. The fact that customer retention is highly critical across most business verticals has been drilled into our brains pretty well so we know why don’t want to lose customers. The important thing how not to lose them. Churn Customer Support Satisfaction is tells you if your churned group left because of patchy customer support. The first thing to check is the Standard Deviation for Time to Resolution for your churned customer group. If it’s high, customer support is not likely to be the common cause. But if it is low, Churn Customer Support Satisfaction can confirm that you need to have a good, solid relook at your support strategy.
A very high Standard Deviation for the churned customer group coupled with a low Churn Customer Support Satisfaction.
Low Standard Deviation for the churned customer group coupled with a not so high Churn Customer Support Satisfaction.
High Standard Deviation for the churned customer group coupled with a low Churn Customer Support Satisfaction.
In the SaaS business environment, you would definitely do well if you have a great product and stunning customer support. But which of these do you think would do better?
There are a lot of other metrics that a lot of companies measure on a regular basis. But the ones listed above provide a solid platform to improve your Customer Support.