
How to build a world-class AI customer service team
Templates and guidance on building a customer service team that uses both AI and human agents to their fullest potential.
Learn MorePicture this: A customer has a simple question, so they open a support chat expecting a quick answer. Instead, they get stuck in an endless loop of unhelpful responses from a scripted chatbot. No real answers. No way forward.
Frustration builds. They leave, search for a competitor, or worse—head to social media to share their experience. Meanwhile, your support team has no idea this is happening. There’s no data on failed interactions, no visibility into customer effort, and no insights into how automation is performing.
That’s what happens when you don’t track AI-first customer service metrics. And it’s all too common.
But there’s good news—tracking these metrics isn’t just possible, it can be automated with AI customer service . Even better, AI doesn’t run out of time or patience. It’s available 24/7, never multitasking, never rushing to wrap up the conversations. That means the potential for high-quality, high-consistency service is far greater than anything human teams can deliver alone.
And when the quality of service goes up, every customer service metric—from CSAT to FCR—gets a lift.
Ready to learn more? We thought so. In this guide, we break down the 10 most critical customer service metrics, and how AI customer service strategies can optimize them—so you can fix what’s broken, double down on what’s working, and deliver seamless, automated customer experiences.
Without tracking the right customer service metrics, you’re flying blind. You might think your customer service strategy is working—until satisfaction scores dip, customers start dropping off, and your team is left guessing why.
Metrics aren’t just about measuring performance; they power better automation. The right AI customer service metrics tell you:
To get a true picture of performance, AI agents should be measured using the same scorecard as human agents. Whether it’s CSAT, CES, FCR, or other KPIs, consistency in evaluation ensures fair comparison, highlights strengths and gaps, and drives meaningful improvements across both AI and human-powered support.
This guide helps you prove the impact of the AI agent, align reporting with leadership priorities, and set your career up for success.
Get the guideBottom line? AI customer service is only as good as the insights driving it. The right metrics don’t just help you track performance—they help you optimize, automate, and deliver smarter, frictionless customer experiences. Now let’s explore the 10 most commonly used customer service metrics.
Nobody wants to reach out for help with an issue and leave without a fix, nor do they want to reach out a second time after being led to believe it was resolved the first time around. FCR measures how often a customer’s issue is not only resolved—it measures whether the issue is resolved on the first attempt. No follow-ups or repeated explanations required.
The higher the FCR, the better (generally speaking)—it typically means low effort for customers and a low cost to serve.
Sound simple? One nuance to consider when trying to optimize FCR is that not all resolutions should be instant. If agents rush to close cases to keep FCR high, you might end up with customers reopening tickets when their problem isn’t actually fixed. Of course, this doesn’t apply if you use an AI agent. AI doesn’t have to rush because they are available 24/7 and can help infinite customers at once.
FCR = (Total Cases Resolved on First Contact / Total Cases Received) x 100
Customer satisfaction is the heartbeat of your support experience, and CSAT (Customer Satisfaction Score) is one of the most widely used ways to measure it. Typically, CSAT is gathered through a post-interaction survey asking customers a simple question: “How satisfied are you with your experience?”
CSAT = (Number of Satisfied Customers / Total Responses) x 100
Customer loyalty is an important measure of a successful support experience.
Net Promoter Score (NPS) is a simple but powerful way to gauge how likely your customers are to recommend your business to others. It’s typically measured through a single-question survey: “How likely are you to recommend us to a friend or colleague?”
Customers respond on a 0-10 scale, categorizing them into:
NPS = [(Promoters - Detractors) / Total Responses] x 100
No customer wants to contact support. When they do, they expect a smooth, effortless experience. Customer Effort Score (CES) measures exactly that—how easy (or frustrating) it is for customers to get their issue resolved.
Instead of asking customers if they were satisfied, CES asks: “How easy was it to get the help you needed?” Customers respond on a scale from “very easy” to “very difficult.” Lower effort = happier, more loyal customers.
CES complements resolution rate. Think of it like this—a customer might have had their issue resolved, but if they had to jump through hoops to get help, they’ll likely remember the frustration more than the resolution.
CES = (Number of “Easy” Ratings / Total Responses) x 100
Customer service is essential—but it can also be expensive. Cost Per Contact (CPC) tracks exactly how much each customer interaction costs your business.
For traditional support teams, high CPC means increasing headcount, longer response times, and escalating operational costs. Excessive costs are never good, but cutting costs aggressively has a major impact on customer experience. Monitoring CPC is key to this balancing act.
CPC = Total Support Costs / Total Interactions Handled
Not all AI customer service is created equal. Some chatbots simply deflect customers—offering vague answers, suggesting help center links, and getting stuck in painful loops. But true AI automation doesn’t just contain conversations—it resolves them.
That’s where Automated Resolution Rate (AR%) comes in. This metric measures the percentage of customer inquiries that are fully resolved by AI, without human intervention. It’s a composite metric that multiples containment rate by the resolution rate for contained conversations.
AR% = (Automatically Resolved Conversations / Total Conversations) x 100
If you want a deeper understanding of this metric, check out how we currently assess whether a conversation was automatically resolved.
A poor customer service experience isn’t just frustrating—it’s expensive. Churn Rate measures how many customers stop doing business with you over a specific period.
Acquiring new customers is often 5 to 7 times the cost of retaining an existing customer, so reducing churn should be a top priority for every business. And since bad service is a leading cause of churn, improving your customer support experience is one of the fastest ways to keep customers.
Churn Rate = (Customers Lost / Total Customers at Start of Period) x 100
Customers expect immediate responses—especially in today’s always-on digital world. First Response Time (FRT) measures how long it takes for a customer to receive an initial reply after reaching out for support. The longer customers have to wait, the more likely they are to abandon the conversation, grow more frustrated, or churn entirely.
Improving FRT can be difficult as your company expands. As incoming queries grow, you have two options:
31% of customer service professionals believe that FRT will see the greatest improvement with AI, knowing customers are increasingly valuing faster resolutions that only AI can provide at scale.
Get the full reportFRT = (Time of First Response – Time of Request) / Total Responses
Average Handle Time (AHT) measures the total time spent resolving a customer inquiry, including talk time, hold time, and any post-interaction work.
It’s no surprise why AHT is a critical metric. Remember the last time you had to listen to hold music while a customer rep put you on hold for 20 minutes? It’s infuriating. In fact, 60% of modern customers feel that waiting on hold for just one minute is too long.
Your obvious goal should be to minimize AHT, but here’s some more context:
AHT = (Total Talk Time + Hold Time + After-Call Work) / Total Calls Handled
Call Abandonment Rate measures the percentage of customers who hang up before reaching an agent. If this number is high, your support experience could be driving customers away rather than helping them.
A high call abandonment rate leads to more serious problems, such as frustration and lost business. Customers don’t like to wait; if they abandon a call, they might also abandon your brand.
If long wait times or unnecessarily complex IVR menus are driving your call abandonment rate high, an AI agent can help . It eliminates wait time entirely and allows customers to communicate their problem in natural language instead of navigating through IVR.
Call Abandonment Rate = [(Total Incoming Calls – Calls Answered) / Total Incoming Calls] x 100
Tracking customer service metrics is only the beginning. The real magic happens when AI turns those insights into action.
Remember: no single metric tells the whole story. To truly understand and improve your customer service, you need a complete, data-driven view—tracking multiple performance indicators across every interaction, channel, and resolution type.
The right metrics help you spot problems before they escalate and identify opportunities to automate, personalize, and improve. But the real differentiator? AI doesn’t just measure better—it performs better.
Unlike human agents, AI doesn’t get tired, distracted, or overwhelmed. It has all the time in the world to deliver helpful, thoughtful support—no matter the hour, the channel, or the volume. And that consistency fuels stronger performance across all 10 metrics in this guide.
Instead of relying on disconnected data sources and manual analysis, AI can track key performance indicators, analyze trends, and optimize customer interactions in real time—turning every conversation into an opportunity for improvement.
This guide gives you the framework to prove the ROI of your AI investments, drive bigger impact, and position AI Customer Experience (ACX) as a long-term growth driver for your company.
Get the guide