13 Customer Service Metrics to Improve in 2025

How you treat and interact with your customers can make or mar your business. It’s that simple.

Exceptional customer service can be the difference between running a business that’s just getting by and running one that’s absolutely thriving. In some cases, it’s the thin line between being a market leader and slowly fading into irrelevance.

Your product might be good, your pricing might be fair, and your marketing might even be sharp. But if your customer service is sloppy, unresponsive, or frustrating, people will remember. And they won’t be coming back.

The opposite is also true. Businesses that obsess over customer service often find themselves with loyal customers, repeat sales, and glowing reviews that practically do their marketing for them.

So, how do you actually know if your customer service is performing at the level it should? You measure it. You track it. You assess it. And that’s where customer service metrics come in.

What Are Customer Service Metrics?

Customer service metrics are measurable indicators that show how effective your customer support team is at handling customer needs, solving problems, and creating positive experiences.

Think of them as a scoreboard for your customer service department, a way to keep track of what’s working and what’s quietly sabotaging your growth.

They cover a range of aspects, from speed and efficiency to quality and satisfaction. And no, it’s not about drowning in random numbers. It’s about tracking the right numbers that actually tell you something meaningful about the customer experience.

When you monitor these metrics consistently, you:

• Spot weaknesses before they become serious issues.
• Identify strengths you can build on.
• Align service goals with your overall business objectives.
• Make data-driven improvements instead of just guessing.

In short, customer service metrics give you a clear picture of how well (or poorly) you’re treating the people who pay the bills.

1. Customer Satisfaction (CSAT)

What it is: CSAT measures how happy customers are with a specific interaction, product, or service. It’s basically asking, "So… how did we do?" and getting an honest answer.

How it works:Customers rate their satisfaction on a simple scale, often 1 to 5, where:

• 1 = Very dissatisfied
• 5 = Very satisfied

You then take all the responses, focus on the ones that are “satisfied” (usually 4 and 5), and calculate the percentage.

Formula: CSAT % = (Number of satisfied responses ÷ Total responses) × 100

Example: If 70 customers out of 100 gave you a 4 or 5, CSAT % = (70 ÷ 100) × 100 = 70%

How to collect the data:

• Email surveys right after a support ticket closes
• On-screen pop-ups after live chat
• A quick one-question poll in receipts or follow-up emails

Data to focus on:

• Only count valid responses (no blanks)
• Track trends over time, one bad week might be a fluke, three months is a problem
• Break down by agent or department to find where the love or frustration is coming from

2. Customer Effort Score (CES)

What it is: CES measures how easy (or painfully hard) it was for a customer to get their problem solved or achieve their goal. In other words: "Was this a walk in the park or an obstacle course?"

How it works: Customers rate the ease of their experience, often on a scale like:

• 1 = Very difficult
• 5 or 7 = Very easy

Formula: CES = Total score sum ÷ Number of responses

Example: If 200 customers gave a total combined score of 1,000, CES = 1,000 ÷ 200 = 5

How to collect the data:

• Ask right after an interaction (checkout, ticket resolution, onboarding)
• Keep it short, one question works best
• Use web forms, in-app prompts, or email follow-ups

Data to focus on:

• Watch for drop-offs, if CES drops after a new process, something broke
• Compare CES by channel, maybe phone is smooth but chat is frustrating
• Check CES alongside CSAT, sometimes customers are happy but still felt it took too much work

3. Net Promoter Score (NPS)

What it is: NPS measures customer loyalty by asking: "On a scale of 0 to 10, how likely are you to recommend us to a friend or colleague?"

Responses fall into:

• Promoters (9–10): Loyal fans who recommend you
• Passives (7–8): Neutral, not actively promoting
• Detractors (0–6): Unhappy customers who may share negative feedback

Formula: NPS = % Promoters – % Detractors

Example: Out of 500 customers:

• Promoters: 300 (60%)
• Passives: 100 (20%)
• Detractors: 100 (20%)

NPS = 60% – 20% = 40

How to collect the data:

• Email surveys every quarter or half-year
• Post-purchase or after key milestones
• In-app for SaaS or digital products

Data to focus on:

• Track your NPS trend over time
• Segment results, premium customers may score higher than free users
• Read follow-up “Why?” answers to understand the reasons behind scores

4. First Response Time (FRT)

What it is: FRT measures how long it takes your team to send the first reply to a customer after they reach out. This is that crucial first impression moment — slow responses can make customers feel ignored, even if you solve their issue later.

How it works: You track the time from when a customer’s request arrives (email, chat, social media, etc.) to when your first human or automated response is sent.

Formula: FRT = Total time taken to send first responses ÷ Number of tickets

Example: If your team handled 50 tickets and the combined time before the first reply was 500 minutes, FRT = 500 ÷ 50 = 10 minutes average first response time

How to collect the data:

• Use helpdesk or live chat software that timestamps incoming requests and replies
• Measure separately for each channel (chat vs. email vs. social media)

Data to focus on:

• Median FRT is often more useful than average (outliers can skew results)
• Compare peak vs. off-peak hours
• Identify agents or shifts with consistently slower FRT and fix bottlenecks

5. Average Handle Time (AHT)

What it is: AHT tracks the average total time it takes to resolve a customer’s request — including talk/chat time, hold time, and any follow-up work. Too long, and customers get impatient. Too short, and you might be rushing and missing details.

How it works: Measure the total time spent on each ticket or call from start to finish, then find the average.

Formula: AHT = (Total talk time + Total hold time + Total follow-up time) ÷ Number of cases handled

Example: If your team spent 1,000 minutes total on 200 tickets, AHT = 1,000 ÷ 200 = 5 minutes average handle time

How to collect the data:

• Track call/chat durations and follow-up time through your support software
• Log after-call work like sending recap emails or updating records

Data to focus on:

• Don’t aim for “shortest possible,” balance speed with quality
• Watch for spikes after new product launches (could indicate training gaps)
• Segment by case type; complex issues will naturally take longer

6. First Contact Resolution (FCR)

What it is: FCR measures the percentage of customer issues solved in a single interaction, without the customer having to follow up. This is a huge trust-builder — nothing feels better to a customer than getting their problem fixed right away.

How it works: You track cases where the first reply or conversation solved the issue completely, then calculate the percentage of those cases out of total cases handled.

Formula: FCR % = (Number of cases resolved on first contact ÷ Total cases) × 100

Example: If 300 out of 400 cases were resolved without follow-up, FCR % = (300 ÷ 400) × 100 = 75%

How to collect the data:

• Use support software with a “resolved” status and track ticket reopen rates
• Follow up with a one-question survey asking if the issue was solved in the first attempt

Data to focus on:

• Low FCR can mean knowledge gaps, unclear documentation, or rushed troubleshooting
• Compare FCR by agent; some may need extra training
• Check FCR alongside CSAT, a high FCR but low satisfaction may mean issues are “closed” too quickly

7. Customer Retention Rate (CRR)

What it is: CRR measures the percentage of customers who stay with you over a given period. High retention means you’re keeping people happy; low retention means they’re quietly walking away to competitors.

How it works: You track how many customers you have at the start and end of a period, adjusting for any new customers gained in that time.

Formula: CRR % = ((Number of customers at end of period – New customers gained) ÷ Number of customers at start of period) × 100

Example: You start the quarter with 500 customers, end with 550, and 100 of those are brand new: CRR % = ((550 – 100) ÷ 500) × 100 = (450 ÷ 500) × 100 = 90%

How to collect the data:

• Use CRM or billing data to track active customers over time
• Define what “retained” means for your business (active subscription, repeat purchase, etc.)

Data to focus on:

• Track by customer type or plan level — your most profitable segment might be leaving faster than the rest
• Compare retention rates before and after major changes (pricing updates, new features)

8. Ticket Volume

What it is:Ticket volume is the total number of support requests your team receives over a given time frame. It’s a simple but powerful way to see workload trends and spot issues early.

How it works: Count every new ticket, email, chat, or call logged in your system during the period.

Formula: Ticket Volume = Total number of support requests in the period

Example: If your helpdesk logged 1,200 tickets last month, your ticket volume is 1,200 for that month.

How to collect the data:

• Pull reports from your helpdesk or CRM
• Break down volume by channel to see where customers prefer to reach you

Data to focus on:

• Watch for sudden spikes, could be a product issue or seasonal demand
• Compare ticket volume with staffing levels to avoid burnout
• Track the percentage of recurring vs. new issues to spot knowledge base gaps

9. Average Resolution Time (ART)

What it is: ART measures the average time it takes to completely solve a customer’s problem from when it’s reported to when it’s marked resolved. It’s a good indicator of efficiency and customer experience.

How it works: Track the time from ticket creation to resolution for each case, then find the average.

Formula: ART = Total resolution time for all cases ÷ Number of cases

Example: If 300 cases took a combined 1,500 hours to resolve, ART = 1,500 ÷ 300 = 5 hours average resolution time

How to collect the data:

• Use ticketing software with timestamps for open and closed statuses
• Separate by priority level; urgent tickets should have much lower ART than low-priority ones

Data to focus on:

• Watch for trends by issue type; complex tech problems may need more resources
• Compare ART to CSAT, fast resolution isn’t good if quality suffers

10. Customer Churn Rate

What it is:Churn rate is the percentage of customers who stop doing business with you in a given period. It’s the flip side of retention and one of the most important metrics for long-term growth.

How it works: Track how many customers you lose over a period compared to how many you started with.

Formula: Churn Rate % = (Number of customers lost during period ÷ Number of customers at start of period) × 100

Example: If you start with 500 customers and lose 50 by the end of the month, Churn Rate % = (50 ÷ 500) × 100 = 10%

How to collect the data:

• Use subscription cancellation logs or CRM records
• Define “lost” — inactive, unsubscribed, or no purchase for a set time frame

Data to focus on:

• Segment by customer type; high-value customers leaving is more damaging
• Look for patterns before churn; long response times, poor CES, or declining NPS could be warning signs

11. Cost Per Resolution (CPR)

What it is: CPR measures how much it costs your business, on average, to resolve a single customer issue. This helps you understand efficiency and resource allocation — solving problems shouldn’t break the bank.

How it works: You total the costs associated with your support team (salaries, software, overhead) and divide by the number of issues resolved.

Formula: Cost Per Resolution = Total support costs ÷ Number of issues resolved

Example: If your monthly support cost is $20,000 and your team resolves 4,000 tickets, CPR = 20,000 ÷ 4,000 = $5 per resolution

How to collect the data:

• Track salaries, software subscriptions, training, and other support overheads
• Count only resolved cases, not open tickets

Data to focus on:

• Compare CPR across channels; phone support may be more expensive than chat or self-service
• Watch trends over time; rising CPR could indicate inefficiency or understaffing

12. Unresolved Query Backlog

What it is: This metric shows the number of customer issues still open and unresolved. A high backlog signals slow service, potential customer frustration, and bottlenecks in your process.

How it works: Simply count the tickets that remain open past a reasonable resolution window.

Formula: Unresolved Query Backlog = Total number of open tickets at a given time

Example: If your system has 150 open tickets at the end of the week, your backlog = 150

How to collect the data:

• Pull reports from your ticketing system filtered by unresolved or open status
• Break down by priority to see which issues need immediate attention

Data to focus on:

• Watch trends; a steadily growing backlog is a red flag
• Compare by agent or department to identify bottlenecks
• Use this alongside ART and FCR to see if unresolved cases are affecting satisfaction

13. Number of Escalation Requests

What it is: This tracks how many customer issues are escalated to higher support levels or management. Frequent escalations may indicate training gaps, unclear processes, or complex problems that aren’t handled efficiently at the first level.

How it works: Count every ticket marked as “escalated” within your system.

Formula: Number of Escalations = Total escalated tickets during a period

Example: If 60 out of 500 tickets are escalated in a month, Number of Escalations = 60

How to collect the data:

• Use your support system to flag escalations automatically
• Include escalation reason codes for better insights

Data to focus on:

• Track by agent, some may need extra training or resources
• Look for patterns, repeated escalation reasons highlight process gaps
• Compare escalations with CSAT and FCR, high escalation but low dissatisfaction could still indicate unnecessary effort

How chatbase Can Improve Your Customer Service Metrics

Customer service metrics are more than just numbers — they’re the pulse of your business. Tracking them effectively lets you understand performance, spot trends, and take action that actually improves the customer experience.

With chatbase, you can directly enhance key customer service outcomes:

• Average Response Time: chatbase responds almost immediately. By learning your business, it can instantly answer FAQs and, when needed, execute actions without delay.
• Customer Churn Rate: chatbase helps reduce churn by keeping customers engaged with timely support, resolving issues before they escalate.
• Customer Retention Rate: improve retention as chatbase ensures customers get consistent, personalized assistance that keeps them coming back.
• First Contact Resolution: resolve more issues on the first interaction as chatbase guides responses and automates solutions where possible.
• Average Handle Time / Time-to-Resolution: streamline problem solving since chatbase provides instant suggestions and automates routine tasks.
• Ticket Interactions: manage and prioritize tickets efficiently, ensuring no request gets lost or delayed.
• Unresolved Query Backlog: automatically flag and address pending tickets to keep your support queue under control.
• Number of Escalation Requests: reduce unnecessary escalations because chatbase empowers frontline agents with AI guidance to solve issues effectively.

By using chatbase, your team can respond faster, resolve issues smarter, and deliver a seamless customer experience that improves satisfaction, loyalty, and overall performance.

Take action today: Implement Chatbase to elevate your customer support and transform your metrics into measurable success.

→ Sign up for Chatbase now!