Customer Service Metrics: Which KPIs Matter Most & How To Improve Them
Most brands keep a close eye on marketing performance, sales numbers and other parts of the business that generate revenue. But, not many do a great job in measuring customer support.
The list of metrics we share below paint a fuller picture of the larger impact customer support has on business growth. Some measures team performance— like how quickly and well you respond to tickets.
Other metrics look deeper at your team's impact on larger company goals, like customer retention and revenue generation. We’ll also share how to calculate each of these metrics.
That said, let’s get started!
Customer KPIs are measurable metrics used to determine performance, specifically to optimize customer experience.
These KPIs show how well you're meeting customer expectations. Since those expectations vary across customers and evolve, it's important to analyze customer data continuously. Collect insights and feedback regularly to stay aligned with what your customers actually want.
Predominantly, customer expectations of service can be divided into three categories:
Speed and convenience, including responsiveness, efficient processing, and tailored customer support.
Reliability and transparency are defined as availability, proactive outreach, and communication.
Interaction and care, consisting of personal attention, empathy, clarity, and friendliness. Adding the human touch
From these three categories, we derived ten measurable metrics that can be used to improve customer experience: speed, availability, effectiveness, and friendliness. We'll show you how to calculate and use these KPIs.
It goes without saying that customers are invaluable to your business. They continue to buy your product or services because they're treated well.
Without saying, we know your customer support team is working extremely hard to keep customers happy.
But you can't know which customers are happy, and which ones need some extra support, without first identifying the satisfaction metrics that are important to your business, and having a system in place forcapturing - and analyzing - customer feedback.
Besides this, there are a number of reasons why measuring customer service metrics is important for your customer support:- Improve customer satisfaction
The first reason to measure your customer support team is to identify what satisfies your audience closely. The best companies combine satisfaction data with hard numbers. They link the results directly to customer behavior.
For instance:
Do satisfied customers tend to stick around longer?
How does their behavior differ from that of neutral or dissatisfied customers?
Are they more likely to refer new customers?
Do they spend more over time?
Measuring these metrics helps support teams understand customers' evolving needs, optimize service processes, and enhance overall satisfaction.- Identify areas of improvement
According to Klaus's 2023 Customer Service Quality Benchmark, 30% of customer service professionals find measuring and enhancing support quality challenging.
This is where measuring customer service metrics becomes critical.
By analyzing customer service metrics, businesses can identify customer pain points in their processes. This data-driven approach enables targeted improvements to specific challenges.- Optimize resource allocation
Have you ever felt that your time, budget, or workforce could have been allocated more effectively if only you had a clearer understanding of which activities required what level of investment?
Customer service metrics can provide exactly that clarity. By showing where your resources are actually being spent, they enable you to make well-informed decisions, whether that means scaling up successful efforts, completely reshaping your approach, or fine-tuning your strategy to improve overall efficiency.
Now that you know the important customer service metrics to measure, and at the same time, there are several pitfalls along the way where things can go wrong.
Thankfully, you can avoid these if you have thought things through beforehand. Here are some common mistakes you can avoid when tracking your customer service metrics:- Confusing individual performance with team performance
When analyzing an agent's performance, you need to look at the actual, direct impact of an agent's contribution. It doesn't make much sense, looking at metrics that form part of a wider team effort, since this can cause confusion around who is responsible for a satisfactory resolution or a failed first contact resolution.
The best way to measure an agent's direct contribution is through event metrics ( eg, internal notes, replies, and successful resolutions). This helps you to determine exactly who is having a positive or negative impact on the team's overall performance.- Not understanding the context of key metrics
Your customer service key performance indicators (KPIs) are very important, but they are only useful when properly understood and interpreted.
For instance, two such metrics that are often used to evaluate customer service performance are first contact resolution and reopening rate:
First Contact Resolution (FCR) measures how many customer queries are successfully resolved during the very first interaction with an agent.
Reopening Rate tracks how many previously resolved queries are reopened by customers after the initial resolution.
The trouble with metrics like reopening Rate is that a conversation can be reopened for a variety of reasons, and this doesn't necessarily mean that the answer provided didn't solve the customer's problem.
For instance, a conversation might be reopened when a customer replies with a simple thank-you, returns to the same thread with a completely new question, or if an agent mistakenly closes it before resolving the issue.
This is why customer service leaders need to evaluate metrics with proper context rather than taking them at face value. While a high reopening rate may seem concerning, the actual Rate of meaningful reopenings, such as cases where the original solution was insufficient, is often much lower.- Having Over-ambitious Timelines and Too Many KPIs
Customer success reps have a lot to do. They are responsible for things like onboarding new clients, increasing customer LTV, identifying and creating upsell opportunities, facilitating renewals, and providing references. Asking your team to achieve all these ASAP is impractical.
Focusing on all of these KPIs at once can be overwhelming. Customer success leaders need time to conduct experiments, understand the problems, and do A/B testing before identifying a consistent, scalable solution that can be refined and optimized as the customer success team grows.
Focusing on too many KPIs and goals only leads to chaos and confusion. Avoid this by setting fewer goals and giving your team enough time to develop effective, efficient solutions. Focus on clear priorities, backed by a detailed plan and a realistic timeline for execution.
Source- Confusing Customer Support Team KPIs with Customer Success
Can an Account Executive do the job of an Account Manager? In theory, both roles sit within the sales ecosystem, but in practice, they require very different mindsets, skill sets, and success criteria.
Both teams have different KPIs, technology, and targets. Under no circumstances should a customer success rep be measured on the same KPI as a support rep. It will fail your customer success department due to unrelated responsibilities or derail the great work your customer support team is already doing.
Source
Improving your customer satisfaction not only means happier customers but also contributes to brand advocacy and increased purchases.
Let's dive into a few strategies to improve customer satisfaction:- Collect Comments and Feedback from Customers
When you send a post-interaction survey to customers, whether you're collecting NPS feedback, measuring CES, or gauging your FCR rate, you must give your customers a way to provide real, tangible feedback.
Clicking a numeric button, in truth, doesn't tell you much.
It's just as valuable to your team to understand why someone picks "8" on an NPS survey about whether or not they'd be likely to recommend your brand, as it is to understand why someone picks a "3."
The same can be said for CES and FCR. Why did the customer feel they had to exert a lot ofeffort even after speaking with your team? What happened that they don't believe your agentwas able to resolve their issue?
Collecting feedback, both good and bad, is important. It gives your team tangible ways toimprove on their processes, and also gives your customers a way to feel like their voice is being heard and that it matters.- Include a Follow-Up Question
Follow-up questions are a great way to bolster your feedback insights and dig into a particular topic.
For instance, once a customer completes a post-interaction survey and rates the agent they interacted with, you can follow up by asking what the agent did well if the rating is positive, or what could have been improved if the rating is negative.
Doing so gamifies the feedback, giving you specific insights into what your customers are looking for, and can help inform how you hire, onboard, and train new agents.
Asking a follow-up question gives your customers the opportunity to take a step back and really think about their feedback, why they picked the rating they did, and gives your team a more nuanced understanding of where they excelled or where they may have missed the mark.- Use Customer Feedback for Agent Coaching
That leads into our next strategy, which is utilizing the customer feedback you collect to guide how you coach your front-line agents.
Let's face it: your customer satisfaction scores, no matter how you measure them, are directly tied to how empowered your support agents are in understanding all facets of your business. If they aren't given the proper tools or learning opportunities, how can they improve their performance and thus improve how your customers feel after interacting with them?
Team leaders should set up alerts to flag opportunities for improvement and take immediate action to coach agents on how to better address issues in the future.
If an agent is flagged for unclear communication, you can directly focus your coaching on that specific skill, helping them learn and improve from the situation.
With modern customer feedback tools, you can also set up alerts for negative feedback, add comments or notes within tickets, and provide real-time coaching throughout the day. By using CSAT scores to guide your coaching strategy, you ensure that no opportunity for improvement is overlooked.- Act on Low Ratings or Negative Feedback Right Away
Receiving negative feedback is inevitable, but taking that feedback seriously and turning it into a learning experience is imperative for keeping your CSAT scores high, your agents engaged, and your customers happy with the services they receive.
First, you can ask follow-up questions to understand better what happened that caused them to end theinteraction without having their issue resolved.
Did the agent need to escalate the issue to a manager or another team instead of resolving it independently? Or did the customer feel the agent wasn't helpful enough and chose to resolve the issue themselves?
This additional context is crucial in understanding what went wrong and how that will reflect on your FCR rate. Once you have additional context on the situation, you can utilize tools like Retell AI to follow up with these customers and make up for the negative interaction.
By following up with another "Yes" or "No" question, you'll not only be able to measure the change in customer sentiment, but also help reveal the impact of your service recovery program on your CSAT scores.
Support teams cannot go by instinct when gauging how satisfied customer support teams are with their offerings. It's important to track customer service metrics to quantify whether customer service operations are functioning as they should.
Here are some essential customer service metrics to track:- Net Promoter Score (NPS)
Let's start with one of the most common customer service metrics for customer satisfaction: Net Promoter Score, or NPS.
Net Promoter Score (NPS) measures how likely your customers are to recommend your business (here, customer support) to others. In most cases, a simple (often single-question) survey is sent to randomly selected customers, and the Net Promoter Score is calculated based on the results.
Any score between 0-6 is considered "detractors," and means you're in trouble; 7-8 means your customers are satisfied, but not in a way that moves the needle, and are deemed "neutral"; votes of 9-10 mean that your brand is crushing it, and these customers have become "promoters."
For instance, Retell AI automatically triggers post-call surveys and analyze customer sentiment trends to help teams improve NPS over time. Based on their results, you can utilize this data to improve your agent effectiveness, hone in on specific areas of coaching, and drive improvements across your front-line team.- Customer Satisfaction (CSAT)
This measurement is, of course, the most obvious customer service metric - after all, it's right there in the name!
CSAT is a measure of customer sentiment used to help organizations understand how customersare responding to their products and services. CSAT scores are collected immediately after a customer service interaction through short surveys.
Typically, asking to rate the interaction ranging from 1 (very unsatisfied) to 5 (very satisfied). By taking this numeric scale and dividing composite answers by 100%, you can get your CSAT score; the higher the percentage, the more satisfied your customers are.
Small businesses with dedicated customers might have a CSAT above 95%. A larger business with an array of customers, each with unique needs, may top out closer to 80%.
There are two types of CSAT you can measure in your organization:
Organization-level CSAT focused on your company, brand, and the overall level of service the customer received.
Agent-level CSAT focuses specifically on how satisfied a customer is with the particular agent who handled their issue.
Many customer support teams utilize CSAT as their measurement for understanding how their customers feel about their brand and the service they received. But, with CSAT, teams can also gain insights into customer satisfaction with the individual agent who helped resolve their issue.- Customer Effort Score (CES)
Customer Effort Score measures how much effort the customer had to expend in order to resolve an issue. After a support interaction, the customer is asked to rate how easy it was to resolve their issue, typically on a scale of 1-7.
To calculate CES, add the total number of respondents who agree that the interaction was easy (those who give a 5 or above) and divide that by the total number of customers surveyed.
The support team should aim for as many ones and twos as possible. A high CES (above 3, on average) means customers had to put in a lot of effort to resolve their issues.Retell AI transforms raw feedback into operational intelligence that can be acted on in real time.
The platform supports:
Through eliminating the lag between experience and intervention, enterprises gain the ability to proactively resolve issues and improve outcomes before the customer walks away.- First Contact Resolution (FCR)
First Contact Resolution (FCR) rate measures how often customer issues are resolved in a single interaction, without the need for follow-ups. It's cross-checked with customer satisfaction surveys asking, "Was the agent able to resolve your issue?"
A good FCR falls between 70-80%. As a customer support leader, you want agents to be able to address customer issues without involving other human agents.
Having a low FCR rate is detrimental for two main reasons.
First, the more a customer has to connect with your team, the more money you'd have to spend on that same customer issue over and over again. When agents are equipped to close out issues in just one call, email, or chat interaction, their time is freed up to help more customers.
Second, each time your customers initiate a follow-up or want additional interaction, it decreases the likelihood of them recommending your brand to others (impacting your NPS score), increases the amount of effort they have to expend on the issue (hurting your CES benchmarks), and overall impacts their satisfaction with your brand (lowering your CSAT).
The more interaction touchpoints, the less likely they are to remain a customer.- Average Handle Time (AHT)
Average Handle Time or AHT is how long the agent worked on an incident. It's typically measured from the time someone is assigned an incident to the time the incident is closed. It does not include the time the incident was in the queue. It does, however, include hold time and wrap-up time, the time spent completing the ticket.
AHT is usually expressed in seconds: (customer interaction time + hold time + wrap-up time)/ number of interactions
AHT is used for planning and measuring efficiency. It's a good measure for analysis, but simply driving lower AHT to reduce cost can cause lower first call resolution and customer satisfaction. If a customer support interaction takes too much time, it pulls an agent away from other duties.- Total Output
Let's put it all together. With a good sense for the AHT, availability, concurrency, and occupancy numbersYou want to meet, you can start to model out your ideal output:
# of tickets team should answer= Number support representatives & concurrency x availability x occupancy
For example:
Assume you have a team of 10 support representatives
Assume your team members can handle 4 chats concurrently
Assume an AHT of 15 minutes
If they were robots, working 100 percent of the time and had full occupancy, each person would handle a total of:
4 chats per 15 minutes = 16 chats per 60 minutes (1 hour)
Multiply that by 10 people, and that's 160 total chats per hour for a 10-person team. But once you factor in availability and occupancy, the rate decreases.
Let's assume a highly efficient availability of 90 percent
Also, assume a highly efficient occupancy of 90 percent (your hold time might be a few minutes in this case)
That'd give you (16) x (.90) x (.90) = 12.96 chats per hour per person. With a team of 10, that's about *129-130 chats per hour.
Is your team that efficient? If not, is it because you're not operating at full efficiency or because you've made an intentional choice to invest in more personal service and focus less on efficiency?
This model is still highly useful in determining what output should be for individual team members inan efficient operation. It's also a useful way to clearly compare an existing operation with what's possible.- Cost per contact
In addition to tracking output, it's also important to take a step back and review the cost side using Cost Per Contact.
Cost Per Contact = Total Costs for Support/Number of Tickets Processed
A good, low Cost Per Contact means your team generally, though with some variability, has:
High availability — they're available to assist customers a high percentage of the time directly, and aren't spending too much time on other tasks
High occupancy — they spend a large percentage of working time serving customers, instead of waiting for tickets (low idle time)
Reasonable concurrency — team members handle the right number of concurrent tickets at once, and probably aren't handling only one at a time
Reasonable AHT and TTR — your team can handle tickets in a reasonably quick period of time
Reasonably high FCR — most tickets are typically resolved on the first contact and don't have to be escalated
When calculating costs, it's important to include non-salary or wage costs. Examples include:
Health insurance
Building costs
Human resources costs
Hiring and training costs
Taxes
For instance, human agents cost $6-12 every call, however, Retell AI cost ~$0.086–$0.15+ per minute. See how Swtch reduced 50% of support team cost and handled over 8000+ calls per month through Retell AI agents while maintaining 5s pickup time.- Service level
The service level measures how long it takes to respond to customers once they're in queue, whether via telephone, chat, or email. When establishing this metric, make sure to specify exactly when that customer enters the queue, and the clock starts ticking.
Typically, Service Level is expressed as a fraction of the percentile of interactions that are responded to in a given time. For example, an 80/20 Service Level means 80 percent of tickets get a response within 20 seconds.
Higher service levels require more agents to be available, which increases operational costs. For example, achieving a 90/10 service level is more expensive than an 80/20 target because you need sufficient staffing to handle peak call volumes without long wait times. As a result, some agents may remain idle during periods of lower demand.
Response time SLAs for interactive engagements, such as telephone and chat, should be measured in seconds. Email or other asynchronous interactions should be measured in minutes or hours.
Most companies first decide how long a wait their customers can tolerate. Then, they compromise on the percentile of interactions that must fall within that time to control costs.- Agent Utilization Rate
Agent utilization rate, or AUR, tracks how effectively your organization utilizes its customer service agents. To calculate, simply divide the total time agents spend handling tickets by their total available work hours.The indicator quantifies both the productive and non-productive time of agents:
Productive time refers to the time agents spend directly assisting customers, such as handling interactions, working on tickets, and resolving issues
Non-productive time includes activities like breaks, training sessions, meetings, and administrative work that do not directly contribute to addressing customer inquiries
The support team should aim for a 70% to 80% agent utilization rate. This range allows agents to be productive while leaving time for training, breaks, team meetings, and unexpected spikes in ticket volume.
A low agent utilization rate can signal overstaffing or process inefficiencies, whereas a consistently high rate may result in agent burnout and a decline in service quality. Maintaining the right balance helps ensure agents stay engaged with customers while making efficient use of resources.- Abandonment Rate
Abandonment Rate in customer service refers to the percentage of customers who begin a support interaction, such as calling, chatting, or creating a ticket, but leave before being connected to an agent or before receiving help.
Abandonment rate =1- (number of interactions completed/ number of interactions started) x100%
High abandonment could mean your Service Level is too low, and customers give up waiting in line. Itcould also mean your workflow is frustrating to your customers and they give up, or it could mean there's atechnical problem with your processing system. Take time to find the root cause of high abandonment rates.
For instance, Anker replaced its basic chatbot and IVR menus with Retell's AI IVR routing, delivering human-standard voice conversations. The result? 95% routing accuracy, 80% faster resolution, and a service team that updates call flows without IT.
One important consideration is that if you intentionally operate with a lean support team and your customers tend to be more patient, it may make more sense to prioritize abandonment rate over ASA. In such cases, the key objective would be to keep abandonment rates low rather than focusing primarily on achieving a fast average speed of answer.
Customer support teams use Retell's AI agents to manage large volumes of tickets and calls without losing response quality. This helps human agents work faster, stay consistent, and focus on complex issues that need human attention.
Among all business use cases, customer support delivers the highest ROI because every minute saved directly improves customer experience and agents' productivity.
And, here's how:- Intent Detection and Call Routing
Retell AI takes AI call routing to the next level with warm transfers, allowing AI phone agents to understand caller needs and warm transfer them to live agents when the situation demands it.
This improves your First Contact Resolution (FCR) and Average Handle Time (AHT). By identifying intent correctly from the start, calls are routed to the right agent without unnecessary transfers, increasing the chances of resolving issues in the first interaction.
At the same time, warm transfers eliminate the need for customers to repeat information, reducing handling time and minimizing frustration.- Automate Low-Complexity Inquiries
AI-powered customer service promises a more conversational and efficient way to get support. It can handle simple inquiries and leave the more complex ones to humans.
Here's what an AI agent from Retell can do:
Auto-respond to incoming calls
Book appointments directly into Calendly, etc.
Answer FAQs and qualify leads based on
Handles thousands of concurrent requests with zero wait time
Maintains consistent response quality under load
Supports 50+ languages instantly
24/7 availability across time zones
These capabilities significantly reduce Customer Abandonment Rate by minimizing wait times and ensuring instant responses. At the same time, automation lowers Cost Per Contact because repetitive support requests no longer require live-agent involvement.
Consistent, around-the-clock service also improves overall customer satisfaction while enabling support teams to scale efficiently without increasing headcount proportionally.- Pre-Call Authentication
In traditional contact centers, pre-call authentication can take anywhere from 45 seconds to 2 minutes per interaction, depending on security requirements.Retell AI reduces Average Handle Time (AHT) by authenticating callers before a human agent joins the conversation. The AI agent verifies identity naturally during the interaction using:- Caller ID and device fingerprinting- OTP or secure links sent mid-call- Voice biometrics (where enabled)- CRM and order-history cross-checksAuthentication happens seamlessly in the background while the customer speaks, allowing human agents to begin the conversation with verified customer information already available.This not only shortens call duration but also improves agent productivity by eliminating repetitive verification workflows.- Post-call Assistance
Once a call ends, conversational AI automatically processes everything that happened during the interaction, without adding work for human agents.
Retell AI agent offers the following post-call assistance after the customer hangs up:
These summaries are instantly logged in the CRM or ticketing system, eliminating manual wrap-up time.
Auto-tagging and dispositioning: The agent further classifies calls based on the type of call, the resolution, and customer feedback.
Follow-up automation: Based on the call outcome, your AI agent can send confirmation emails or WhatsApp messages, trigger surveys (CSAT, NPS), or schedule callbacks or technician visits without any agent intervention.
Real-time update of information: Retell AI integrates effortlessly with leading CRMs, collaboration tools, and contact center platforms to sync customer data, call summaries, dispositions, and next actions in real-time.
Instead of relying on third-party tools to evaluate customer service metrics, Retell AI gives you all the data for your customer support team to quickly assess how they are doing. You can also create a dashboard for viewing, filtering, and analyzing conversations in one place.
Pushing agents to move faster to achieve ideal service metrics doesn't increase productivity. On the contrary, it creates pressure—and pressure creates shortcuts, errors, and disengagement from work.In customer support, where even a single mishandled ticket can impact renewals and expansions, speed without structure becomes a liability.
Human agents perform the best when they have full context and real-time visibility into prior conversations across channels. They need smart routing that directs issues to the right experts, along with access to a dependable knowledge base or self-service resources to resolve queries efficiently.
That's where Retell AI helps by keeping conversations, context, and customer interaction in one place and reducing agents' cognitive load by answering repetitive queries.
Ready to see how real-time voice agents can transform your customer interactions? Try Retell AI for free.
Customer service metrics are measurable KPIs used to evaluate how effectively a support team meets customer expectations and delivers quality service. They track aspects like speed, efficiency, and customer satisfaction, helping businesses understand performance and identify gaps. By continuously analyzing these metrics and customer feedback, teams can adapt to evolving expectations and improve the overall customer experience.- What are the most important customer service metrics to track?
The most important customer service metrics typically include CSAT, NPS, Customer Effort Score (CES), First Contact Resolution (FCR), and Average Handle Time (AHT). Together, they provide a balanced view of satisfaction, loyalty, efficiency, and service quality. Tracking the right combination helps teams understand both customer sentiment and operational performance, ensuring they focus on what truly impacts experience and outcomes.- What is the difference between CSAT and NPS?
CSAT measures how satisfied customers are with a specific interaction, usually through a short survey immediately after support. NPS, on the other hand, measures long-term loyalty by asking how likely customers are to recommend your brand. While CSAT captures immediate sentiment, NPS reflects overall perception and advocacy, making them complementary but distinct indicators of customer experience.- How do you measure customer service performance?
Customer service performance is measured by analyzing a combination of quantitative metrics and qualitative feedback. Teams track KPIs like response time, resolution rates, and satisfaction scores, while also reviewing customer comments and behavior. This approach helps identify trends, measure efficiency, and understand customer needs, enabling continuous improvement in both processes and agent performance.- What customer service metrics improve customer satisfaction the most?
Metrics that improve customer satisfaction the most are those that reduce effort and resolve issues quickly, such as First Contact Resolution (FCR) and Customer Effort Score (CES). When customers get fast, accurate solutions without repeated interactions, satisfaction naturally increases. Combining these with feedback-driven improvements ensures service aligns with customer expectations and delivers a smoother experience.- How can AI improve customer service metrics?
AI improves customer service metrics by automating repetitive tasks, enabling faster responses, and ensuring consistent service quality. It can handle high volumes of simple queries, route customers to the right agents, and generate insights from interactions. This reduces wait times, improves resolution rates, and allows human agents to focus on complex issues, ultimately enhancing overall customer satisfaction.
See how much your business could save by switching to AI-powered voice agents.
Total Human Agent Cost
AI Agent Cost
Estimated Savings
A Demo Phone Number From Retell Clinic Office
Start building smarter conversations today.
