15 common customer service metrics and how to use them

15 customer service metrics that your customer service team can use to measure performance.

Published on Apr 30, 2024 by Isaac Chambers

Measuring the right metrics is critical to the success of your customer service function… but which metrics should you measure and how can you use those metrics to assess performance?

It’s a surprisingly difficult question to answer, especially since it can be difficult to tie customer service back to revenue. Plus, there’s a seemingly endless number of possible customer service metrics to consider adding to your metrics framework.

While I can’t choose your metrics for you, I can share 15 of the most common customer service performance metrics that teams can use to measure their efficiency, effectiveness, and how customers feel. I’ll also give you some tips on how to use each one so that you can make educated, data-backed decisions for your customer service team.

But first, let’s do a quick review of customer service metrics and briefly touch on why it’s so important for growth-minded organizations to pay attention to customer service-related data.

What are customer service metrics?

Customer service metrics help you gauge the effectiveness of your customer service team's processes. These metrics can include things like average first response time or number of tickets closed per day.

By tracking these data points, you can make sure that your team is providing the best possible service to your customers.

Why is tracking customer service metrics important?

I strongly believe that the customer service function should be viewed as a growth engine rather than a cost center.

Why is that, you ask? I’d love to tell you 🤑.

Excellent customer service helps your organization retain existing customers, which raises your average customer lifetime value (LTV). Plus, satisfied customers mean less churn and more user growth through word-of-mouth referrals. 

At a minimum, running efficient customer service operations can help reduce costs and prevent fire drills. 

In total, this means you can grow your business faster and more profitability. Don’t just take my word for it, though – this article from Hubspot is full of research-backed facts that showcase the importance of customer service.

What customer service metrics should my organization track?

The best customer service metrics to monitor will vary based on several factors, including your industry, performance goals, your customer service team, the company itself, and the metrics framework you’re using to guide data decisions

Still, most customer service teams share common core goals. With this in mind, we’ve broken the 15 customer service metrics that we’re about to review into two categories:

  • Operational customer service metrics that only speak to your customer service team’s performance.
  • Organizational customer service metrics that help you get into the mindset of your customers and how they feel about your brand. However, note that the organizational metrics included here aren’t exclusively used to measure customer service performance. For example, net promoter score (NPS) measures long-term customer health and satisfaction, but may not be explicitly related to service. Still, metrics like NPS, customer satisfaction score (CSAT), and churn can be heavily influenced by the customer service function.

In the explanations below, you’ll get a brief overview of the metric, a calculation, and a note on its metric type. 

However, keep in mind that this list is a broad overview of customer service metrics. They won’t all apply to every business or industry – for example, SaaS, ecommerce, and other industries all have unique performance metrics that their individual teams monitor.

A quick note on metrics types

In How to Design a Winning Metrics Framework, we explained the importance of tracking a few key metric types, including growth, quality, and efficiency + inputs, outputs, and outcomes.

But, since it can be very difficult to directly relate customer service performance to revenue, you won’t see any growth metrics or outcome metrics here. 

If you aren’t familiar with the different types of metrics you can measure, be sure to take a look at the metrics framework blog linked above – it’ll teach you how to make an educated decision about which metrics are most important to your customer service team and/or the overall company.

So, without any further ado, let’s dive into the world of customer service metrics.

Operational customer service metrics

Each of these metrics will provide insights into the customer service team’s performance, particularly as it relates to efficiency and speed.

1. Average Ticket Count (Daily/Weekly/Monthly)

Metric Type: Efficiency, Output

Average ticket count doesn’t really measure performance of a customer service team, but it does heavily impact the team’s ability to optimize the rest of the metrics on this list. For example, a high number of tickets doesn’t mean the team is doing a good or bad job. It does impact their ability to optimize other things like first response time and resolution rates, though.

Average ticket count is the total number of customer issues that have been reported during a specific time period. It’s more than just a number, though. If your average ticket counts are rising or falling, there could very well be something going on that requires attention. 

For example, are your ticket numbers increasing? If they are, it could be a sign of an underlying issue that needs to be addressed, like a software bug or a broken process. An increase in average ticket volume could also be due to user growth! Average tickets per active user is a great way to filter out the noise from legitimate user growth. 

Keeping a close eye on your average ticket counts and performing root cause analysis when needed can help your team resolve open tickets efficiently and prevent future increases in overall ticket volume.

Pro Tip: Help desk tools like Zendesk offer features to link multiple tickets to a single problem or incident. This functionality can help your team understand and document how other teams are impacting service operations.

2. Average First Response Time (FRT)

Metric Type: Efficiency, Input

Average first response time (FRT) is the average amount of time taken for a customer service agent to respond to a support ticket or customer inquiry.

In today’s world of instant gratification, customers’ expectations for speedy responses are higher than ever. 

According to data from Zendesk, here are the average first response times that today’s customers expect. However, note that what counts as “good,” “better,” or “best” will ultimately vary based on industry and severity of the issue – these are just some good benchmarks to strive for.

Channel Good Better Best
Email 12 hours or less 4 hours or less 1 hour or less
Social media 2 hours or less 1 hour or less 15 minutes or less
Live chat 1 hour or less 5 minutes or less 1 minute or less

Even if CSRs (customer service representatives) aren’t able to resolve tickets right away, letting customers know that someone is aware of their issue can help improve the customer’s experience and drive satisfaction. Furthermore, average first response time is a common SLA (service level agreement) in service agreements you may be accountable for.

You should have easy access to average FRT in whatever help desk platform you’re using, but here’s how it’s calculated:

Average First Response Time = Total Time to Send the First Response in the Selected Timeframe ÷ Number of Tickets Whose First Responses Were Sent in the Selected Timeframe

Pro Tip: Customers appreciate a low average FRT. A positive first experience with your brand can be a powerful differentiating factor that entices customers to stay loyal and spend more.

3. Average Response Time

Metric Type: Efficiency, Input

The first response to a customer’s problem or inquiry is important, but overall response times are important, too. Some tickets require a few back-and-forths to resolve and your customers will expect speedy responses to their questions and concerns.

Tracking average response times is important because it can show you whether CSRs are doing an adequate job of responding to customers. Monitoring this metric can also help prevent CSRs from gaming the system by prioritizing responses to new tickets.

Average Response Time = Total Time Taken to Respond During the Selected Timeframe ÷ Number of CSR Responses in the Selected Timeframe

Pro Tip: Similar to other input metrics, average response times are solidly within your control. To improve response times, here are some tactics for your customer service team to try:

  • Leverage response templates whenever possible
  • Automate workflows
  • Triage responses (aka categorize and prioritize – for example, sales-ready leads would be a higher priority than general inquiries)

4. First-Contact Resolution Rate

Metric Type: Efficiency, Output

First-contact resolution rate tracks the percentage of cases that are closed after a single interaction. This metric shows how good your customer support team is at understanding and addressing problems when they’re first brought up by the customer.

While some cases definitely require more extensive communication, there’s little else that’s more frustrating to a customer than going back and forth with an agent or getting passed from one agent to another to get help. 

This is an important metric because it can incentivize CSRs to take the time to fully understand a customer’s concern, versus responding quickly to get the ticket out of their queue. First-contact resolution can also be a warning sign for training issues or inefficient routing of tickets.

Pro Tip: In an ideal world, your team can solve customer issues before they happen and this metric is zero. Since that’s unlikely, it’s helpful to determine the ideal number of interactions it will take to resolve issues so that your CSRs have a concrete goal to work towards.

5. Average Resolution Time

Metric Type: Efficiency, Output

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.

Don’t always take quick resolution times at face value, though. As I alluded to above, 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.  

Pro Tip: Response times are important, but the most effective and efficient customer service teams focus the bulk of their energy on improving resolution times. 

6. Number of Interactions per Case

Metric Type: Efficiency, Output

This metric measures the number of interactions it takes for a CSR and a customer to resolve an issue.

A high number of interactions per case can signal that your processes for resolving tickets need improvement. For example, are you collecting the right information from customers to handle their requests without the need for additional clarification? 

Tracking this metric over time in tandem with process improvements can help turn your service operations into a well-oiled machine, or even completely automate some service requests.

Pro Tip: You’ll get similar insights from measuring First-Contact Resolution Rate, so there’s little need to monitor both of these metrics.

7. Issue Resolution Rate

Metric Type: Efficiency, Output

Issue resolution rate measures the percentage of issues your team resolves compared to the total number of tickets received. 

It’s rare to achieve an issue resolution rate of 100% – some tickets get closed without ever being solved. For example, a customer might give up on their request if they feel it’s more hassle than it’s worth. This is a frustrating experience and can erode customer satisfaction.

When your organization has an unresolved issue, don’t ignore it. Instead, investigate further to discover the reasons behind the lack of resolution and try to provide the customer with a resolution.

Pro tip: Analyzing how issue resolution impacts churn can help you to prioritize where to invest in improvements. For example, are customers asking for a feature and then leaving because it hasn’t been built?

8. Rate of Answered Calls

Metric Type: Efficiency, Input

Answering calls promptly is still a marker of good customer service, even in our digitally driven era. This metric is pretty self-explanatory – it’s the number of calls answered divided by the number of calls received. 

Think about the last time you had to call a customer service line. If you’ve ever been stuck on hold listening to the same loop of generic jazz that’s occasionally interrupted by an empty promise that “your call is very important to us,” you already know that little induces more ire.

Pro Tip: Even though many customer service interactions take place online, there are still plenty of people who prefer an old-fashioned phone call. Phone support is an option that many customers appreciate, so don’t neglect it entirely. 

9. Average Handle Time

Metric Type: Efficiency, Output
Average handle time tracks the total time spent talking to the customer, on hold with the customer, and working to resolve the customer's concern after the call (or chat). Both your team’s time and your customer’s time are valuable and tracking this metric helps to ensure that issues are being resolved efficiently.

The calculation for average handle time is simple:

Average Handle Time = (Time Spent Talking to Customer + Hold Time + Post-Call Time) ÷ Total Number of Calls

Pro Tip: Customer service teams should be careful with this metric because it can be difficult to strike the right balance between speed and quality. While it’s good to resolve issues quickly, there is such a thing as too quickly.

10. Ticket Backlog

Metric Type: Efficiency, Input
Ticket backlog tracks the number of unresolved customer support requests in a particular timeframe. 

Ticket backlog is a helpful operational metric that highlights how many tickets are still unresolved beyond your team’s normal response times. A backlog generally happens due to employee performance, large ticket volumes, or because of additional complexities that exist.

Pro Tip: This metric comes in handy when you need to decide if it makes sense to hire more CSRs. When a major event or issue causes a surge in tickets, meticulously tracking your backlog can also help your team get back on track.

Organization-wide customer support metrics

These last five customer service metrics reflect the performance of the entire organization, including the performance of the product and brand (not just the customer service team).

11. Net Promoter Score (NPS)

Metric Type: Quality, Output
NPS is a measure of customer loyalty that was first popularized by the management consulting firm Bain & Company. Variations of NPS have emerged over the years, but NPS is commonly calculated by asking customers, “How likely are you to recommend our product or service to a friend or colleague?”

Respondents are given a label of promoter, passive, or detractor based on their answers.

A scale showing detractors, passives, and promoters for Net Promoter Score (NPS)

The NPS score is the percentage of promoters minus the percentage of detractors:

NPS = Percentage of Promoters - Percentage of Detractors

For example, if 50% of your customers are promoters and 30% are detractors, your NPS score is +20%. Average NPS scores vary widely across industries, so it’s helpful to benchmark your performance against similar organizations when possible.

Some studies have shown that NPS scores are strongly correlated with growth rates. Promoters are likely to stay as customers longer and spend more, are less costly to serve, and provide free marketing by recommending you to their friends.

However, there are some NPS skeptics out there because this is a lagging metric that’s hard to definitively tie to revenue. Other customer service metrics, such as response times, reflect both quality and efficiency.

Pro Tip: Bain & Company, the creators of this metric, have some sage advice you should keep in mind when assessing NPS (emphasis is mine): 

“There's another important caveat to the connection between high Net Promoter Scores and growth: A high score in and of itself is not the real objective. A high score by itself does not guarantee success. Net Promoter merely measures the quality of a company's relationships with its current customers, and high-quality relationships are a necessary but insufficient condition for profitable organic growth.”

12. Customer Satisfaction Score (CSAT)

Metric Type: Quality, Output
Unsurprisingly, CSAT is another popular measure of customer satisfaction. CSAT is tracked by asking customers how satisfied they are with a product, service, or encounter. CSAT is calculated by taking the number of satisfied respondents and dividing it by the number of total respondents. 

When using a 5-point scale, 4 and 5 are typically considered satisfied. Sometimes, a simple thumbs up / thumbs down scale is used, especially for support interactions (e.g., “Did we fully resolve your concern today?”)

Like NPS, CSAT is a powerful tool for predicting customer growth and loyalty. Similarly, what constitutes a “good” score varies by industry. Generally, CSAT scores can be categorized into these ranges:

  • Below 50% – Needs improvement
  • 50-70% – Fair
  • 70-90% – Good
  • Above 90% – Excellent 

Pro Tip: CSAT doesn’t always paint a complete picture, which is why it’s wise to assess it alongside other customer service metrics. Here’s an example: All too often, it’s the angriest customers that are the most vocal and opinionated. If you’re seeing low CSAT scores and high NPS scores, for example, figure out how to encourage more happy customers to fill out your surveys, too. 

13. Frequency of Up-Sells and Cross-Sells

Metric Type: Growth, Output
At first glance, this may seem like more of a sales metric than a customer service metric, but hear us out! 

Earlier, I mentioned that the customer service function should be viewed as a growth engine rather than a cost center. After all, satisfied customers stay longer and spend more. 

The percentage of customers that are trying multiple of your products or service offerings can be a great signal that your customers are happy and are unlikely to be swiped by competitors. Furthermore, when customer service agents are trusted by customers, they are in a position to influence customers to try a new product or service.

Pro Tip: Not sure what new features to add to increase up-sells? Schedule some customer interviews with power users to pick their brains about the features and functionalities they would love to see. 

14. Customer Retention Rate

Metric Type: Quality/Efficiency, Output
Customer retention is an often overlooked part of the growth equation but is increasingly getting the attention it deserves. After all, what use is it to spend good money and effort to acquire a customer if you can’t keep them coming back?

Customer retention rate is calculated for a specified time frame. For example, “80% of customers stay with us for at least 12 months.” Understanding and improving your customer retention rate is a critical piece of boosting your growth rate.

Pro tip: Understanding the relationship between your service levels and your retention rate can help you identify your optimal service level targets. You can do this by randomly segmenting customers into cohorts and providing differentiated service levels (such as average time to first reply) to each cohort. 

Over time, differences in retention may emerge between cohorts. This information can help you set your service levels in a way that balances the cost of serving customers with the benefits you get from increased customer satisfaction and retention.

15. Customer Churn

Metric Type: Quality, Output
Customer churn is the inverse of retention. It’s the average percentage of your customer base that quits over a given interval (usually a week, a month, or a quarter).

Tracking your churn rate is important for sales forecasting and financial planning activities. Furthermore, carefully tracking changes in churn over time can help to identify problems before they get out of hand. Plus, it’s a whole lot easier to keep current customers than it is to attract new ones.

Pro Tip: Customers can churn for any number of reasons. Here’s what to do if churn rates are higher than you’d like: 

  • First, take a look at your customer satisfaction metrics. 
  • If your organization sends customer surveys, assess the general attitudes of respondents to see if there are any patterns you can identify. 
  • If it’s possible to do an “exit interview” with a churned customer, take the opportunity to gain deeper insights into the issues they experienced. It may be an awkward conversation, but you’ll learn a ton.

Measure customer service performance with a modern data stack

There are a variety of ways to track customer service metrics, including simple spreadsheets, SaaS tools, and CRM solutions. 

To get the most out of your customer service data, consider implementing a modern data stack in your organization. It’s one of the most powerful and efficient ways to facilitate data analysis. 

With the guidance of an effective metrics framework and the right data infrastructure, your sales team can be agile enough to stay ahead of the game and maximize their sales performance.