How to Improve CSAT Scores in a Call Center

One of the biggest jobs for any support organization is delivering a good customer experience. And one of the clearest signals of how you're doing is your customer satisfaction (CSAT) score. It's also one of the hardest metrics to move.

To improve CSAT scores in a call center, start by measuring satisfaction on every call instead of relying on the few customers who answer surveys. Then find your detractors, figure out why those calls went wrong, and fix the most common drivers first. From there, give agents real-time support so more issues get resolved on the first contact, and let AI agents handle routine requests, as long as the full context carries over when a customer needs a person.

Below, I'll walk through how we approach each of those steps in our own Support org at Dialpad, including the parts that used to take my team hours every week.

Why call center CSAT is so hard to move

You can't improve a score you can't see clearly, and many call centers only see a sliver of theirs. Traditionally, CSAT comes from post-call surveys. The problem is that only a small fraction of customers stay on the line to answer them.

The ones who do tend to be the extremes. They either had a great experience and want to praise the agent, or they had a frustrating one and want to be heard. Customers who had a perfectly average call usually hang up.

That leaves you with a score built on a small, skewed sample. It can tell you something is wrong, but it rarely tells you how widespread the problem is or what caused it. So before you try to raise CSAT, you need a more complete way to measure it.

How to measure CSAT after every phone call

The first step is getting an accurate read on where you stand. That means combining traditional surveys with a way to measure the calls customers never rate.

Start with surveys, but don't rely on them alone

Post-call surveys are still worth running. Dialpad lets you send customer satisfaction surveys by phone, where callers rate the interaction on their keypad, or by text right after the call ends. You can also invite callers to explain their rating in their own words, which Dialpad AI records and transcribes.

Most teams calculate CSAT as the percentage of satisfied responses (usually 4s and 5s on a five-point scale) out of all responses. Our CSAT overview breaks down the formula in more detail. The math is simple. The hard part is that survey responses only cover a small slice of your calls.

Use AI to score the calls customers don't rate

This is where Dialpad AI CSAT changed how my team works. Instead of waiting on surveys, it predicts customer satisfaction from the conversation itself. Dialpad AI transcribes each call, then analyzes the transcript for signals like long holds, interruptions, whether the issue was resolved, and how the customer's sentiment shifted from start to finish.

AI CSAT evaluates both inbound and outbound calls and assigns eligible English-language calls a score from 1 to 5. It skips calls that don't make sense to grade, like voicemails or calls where the agent still owes the customer a callback. Your survey results still matter here. They help ground the AI's predictions, so it pays to keep collecting them even when response rates are low.

The result is a much larger and more representative sample than surveys alone. The AI CSAT dashboard shows your average score over time, how it compares to the previous period, and how many calls the AI graded alongside your survey response rate.

The team at FinditParts saw this firsthand. "AI CSAT helps us score all our calls, and has increased our scoring footprint fourfold," says Joel Kassay, Director of Customer Success at FinditParts.

7 strategies to improve CSAT scores in a call center

Once you can see satisfaction across all your calls, improving it becomes a process instead of a guessing game. These are the steps my team follows, in roughly the order we follow them.

1. Find your detractors first

Negative feedback is painful, but it's where you learn the most. Your detractors show you exactly where the experience is breaking down, so start there before you try to optimize anything else.

Historically, finding detractors meant reading individual survey comments one by one. With AI CSAT, low-scoring calls are already flagged, so you can filter to them by agent, team, or date range and start reviewing right away.

2. Get to the "why" behind every low score

Say your CSAT score is 80%. You can't win back the 20% of customers who left unhappy unless you understand what went wrong on those calls. Was it a long wait, an unresolved issue, a gap in agent knowledge, or something else?

Call recording is table stakes here. Transcripts are even better, because you can skim to the key moments instead of listening to a call from start to finish. But for years, the step I really wanted was AI that could tell me why a customer was dissatisfied without anyone having to review the call. That now exists.

With AI CSAT Explanations, every scored call is evaluated across seven categories: agent communication, agent empathy and professionalism, agent knowledge, agent speed and efficiency, completeness of resolution, customer sentiment, and operations. In the Coaching Hub, supervisors can filter by category, see which agents' calls triggered it and the reasons behind each one, then play the call or add it to a playlist for review. Pair that with sentiment analysis and you get a clear picture of how customers felt and what drove it.

3. Fix your biggest CSAT drivers before anything else

Once you know why calls go wrong, group the reasons. My QA team does this every week. They bucket the causes of low scores into categories and chart them, so we can see which issues show up most often. Those are your biggest opportunities.

This used to be a heavily manual process: reviewing transcripts, listening to calls when needed, and tracking everything in a spreadsheet. AI Scorecards take much of that work off the team by grading conversations against your own QA criteria, so reviewers can focus on coaching instead of data entry.

Scorecards and CSAT also work better together. "AI Scorecards have been really helpful to give us more context behind a particular agent's CSAT scores," says Jude Tungul, Business Operations and Intelligence Manager at Westward360.

4. Shorten the wait and route customers to the right agent

Long holds and repeated transfers are some of the most common reasons customers leave unhappy. Routing callers to an agent with the right skills the first time reduces both.

It also helps to watch queue health as it happens. Dialpad's Queue Health report tracks wait times, call volumes, and SLA achievement in real time, and its leaderboard view makes bottlenecks easier to spot before they turn into a wave of low scores. When queues do back up, in-queue callback lets customers request a return call without losing their place in line. Contact center analytics then help you see how those changes affect satisfaction over time.

5. Give agents answers during the call, not after

Many detractor calls come down to an agent who didn't have the right answer in the moment. Real-time guidance closes that gap.

AI Live Coach cards surface relevant help content and suggested responses based on what the customer is saying, while the call is still happening. That can help newer agents resolve more issues on the first contact and keep answers consistent across the team.

Superior, a treasury solutions provider that handles long, complex calls, used Dialpad AI to raise its CSAT score from a 75% baseline. "It took us about four months to get to a CSAT score of 94% with Dialpad AI, and we've been maintaining these scores for the last six months," the Superior team shared.

6. Let AI agents handle routine requests, with full context on handoff

Not every call needs a person. Order status checks, appointment scheduling, and similar routine requests can often be handled by Dialpad AI Agents across voice and digital channels, so customers get answers without waiting in a queue and your human agents have more time for complex conversations.

The handoff is where these interactions are won or lost. When a customer asks for a person, or a request falls outside what the AI agent is set up to handle, it escalates into your contact center. The human agent can see context from the conversation so far, which can include the reason for the contact, why it escalated, the steps already attempted, and the customer's sentiment. Customers are less likely to have to repeat themselves.

If you're not sure where to start, Skill Mining analyzes transcripts from your human-handled contact center calls and recommends AI agent skills, along with the share of calls each could handle. One caution from experience: judge AI-handled conversations by whether customers were satisfied and their issue was resolved, not by deflection rate alone. A deflected call that leaves a customer frustrated still hurts your CSAT.

7. Coach from patterns and recognize what's working

CSAT data is most useful when it shapes coaching. The AI CSAT agent leaderboard shows each agent's average score, how many calls were graded, and how that compares to the previous 30 days. Weekly trend emails keep supervisors on top of changes without digging through dashboards.

Conversation metrics add another layer. Leaderboard columns for agent talk time, listen time, and silence help you spot calls where an agent dominated the conversation or left customers waiting in dead air. Those patterns point to specific, coachable behaviors.

Don't forget the other end of the leaderboard. Your top performers show what good looks like, and recognizing them matters. Stephen Schleeper, Senior VoIP Engineer at Sunstate Equipment, put it simply: "AI CSAT has been a game-changer for us." As he adds, "Our customers notice the difference."

How CSAT connects to churn and retention

CSAT isn't just a support metric. A customer who leaves a call unhappy is often a customer at risk, and a low score can be one of the earliest warning signs of customer churn you'll get.

That's why speed matters. When AI CSAT scores calls within seconds of the call ending, you can follow up with detractors while the issue is still fresh, rather than weeks later when a survey trickles in. FinditParts has seen that pay off. "These CSAT scores are also helping us win back customers, which is huge for our team," says Joel Kassay.

A few practices help turn CSAT data into retention:

• Close the loop with detractors quickly. A follow-up call from a senior agent or supervisor can turn a bad experience around.

• Watch for churn signals in the conversation. Cancellation requests, repeated contacts about the same issue, and frustration about wait times all show up in transcripts before they show up in your renewal numbers.

• Track CSAT alongside NPS. CSAT tells you how a specific interaction went, while NPS reflects the overall relationship. Comparing CSAT vs. NPS over time can show whether service issues are starting to affect loyalty.

• Share recurring drivers beyond the support team. If the same product issue keeps pulling scores down, product and operations teams need to see it too.

How AI changes the way support teams improve CSAT

The biggest change AI brings to CSAT isn't a single feature. It's the shift from sampling a few conversations to understanding all of them.

When I started in support, improving CSAT meant working from a handful of survey responses and whatever calls a QA team had time to review. Today, AI can score satisfaction on every eligible call, explain what drove each score, coach agents while the customer is still on the line, and handle routine requests before they ever reach a queue. Support leaders get a much deeper understanding of their customers, and they can act on it faster.

EF Education First is a good example. Using Dialpad AI CSAT to infer satisfaction across its calls gave the team full visibility into service quality. "Over the last three months with Dialpad, we've been getting over 97% in CSAT, from about 89% previously," says Mike Monteiro, Senior Manager of People Operations at EF Education First.

Where that data lives matters, too. Dialpad Support for contact centers runs on the same foundation as Dialpad AI Agents, so conversations handled by AI and by people are captured in one place. That's what makes Dialpad's AI platform for customer experience useful for CSAT: every interaction becomes operational insight your team can use to make better decisions, instead of data scattered across disconnected tools.

Where to start

You don't need to do all seven of these at once. If I were starting over, I'd begin by getting an accurate read on CSAT across every call, then pick one team and one or two of the most common detractor drivers to fix. Measure where you are before you change anything, check again a few weeks later, and expand from there.

CSAT can be slow to move, but it does move when you understand what's behind it and act on what you find.