How to use sentiment analysis to improve customer success (2026)

NPS surveys. Post-project feedback forms. Annual review calls. The problem with all these customer success methods is they measure how customers feel retrospectively. By the time that data is in front of you, some of those customers have already made up their minds.

A survey sent once or twice a year – skewed towards whichever clients are happiest or most frustrated – tells you almost nothing about the majority quietly deciding whether to renew.

Are you tired of finding out customers are leaving when their cancellation notice hits your inbox?

Sentiment analysis software shows you the warning signs so you can do something to keep those customers around when you still have the chance.

[EMBED - Felipe Araya, TMF: Sentiment analysis as a growth lever]

What sentiment analysis actually is

Sentiment analysis uses natural language processing to analyse tone and context across client emails and support tickets as they arrive. NLP scores each interaction as positive, neutral, or negative, then builds a live picture of how clients are feeling across your entire portfolio.

A frustrated email every now and then is one thing. But declining sentiment across multiple emails from the same client? That’s churn waiting to happen. And sentiment analysis catches the pattern before things escalate – at a scale no manual process can match.

The timing is everything here. Surveys capture client frustration after it's already too late, while sentiment analysis software reads the signal while you can still do something about it.

From detection to action

The numbers back it up. Research by NiCE in 2024 found that brands excelling at customer sentiment outperform competitors in five-year stock returns by 43 percentage points. That's the return on knowing how your clients feel and acting on it before they have to tell you.

Customer experience (CX) leaders already know the gap exists. Just 9% of the 1,181 CX decision-makers surveyed in Genesys’s 2025 State of Customer Experience report see NPS as critical to business performance. That’s compared to 45% that said customer satisfaction is a critical metric – and sentiment analysis can reveal that in real time.

The manual alternative doesn't scale. Teams that have tried getting feedback from clients manually burn hours getting a partial picture of one month. They rarely go back for a second look.

When we spoke to Felipe Araya, Global Head of Operations at TMF, he explained why knowing how your clients feel is just the first step to customer success:

"We're using sentiment to be able to provide more visibility on how a client might be feeling at a department level, a market level, and a global level in real time. But visibility is just the first piece of the puzzle. If we know a client is unhappy, we need to act. That's where Enate's core orchestration platform comes in."

Detecting and resolving problems with Enate

Enate's sentiment analysis software scores every client communication in real time across your entire portfolio. When a client's sentiment starts shifting, the solution triggers a workflow automatically and tracks the issue through to resolution.

Custom sentiment analysis models can easily rack up to six figures and take months to build. Plus, they need training, integration work, and constant maintenance – and even then, accuracy is rarely where you need it to be, with some third-party machine learning models often only achieving about 70% accuracy. Not good enough to act on at scale.

If you're already running Enate for orchestration, sentiment analysis is built into the platform. That frees your teams from scanning for warning signs manually so they can spend their time on the clients who need attention, not on finding out which ones do.

TMF has deployed Enate's sentiment analysis across 5,200 of their 7,000 users, covering 30,000 clients. They benchmarked the accuracy themselves, comparing Enate's AI output against human reviewers on 1,000 emails, and hit 94%. That's what gave them the confidence to roll it out at that scale.

Felipe explains the business impact:

"It's all about the client. A happier client is more likely to pay you on time, give you more business, recommend you and be a reference to other clients. Enate's sentiment analysis AI tool is a key cornerstone of our ambition for flawless service and happy clients. It's directly impacting those three things: faster cash in the door, more growth through existing clients, and more growth through new clients."

If client success is still something you measure after the fact, you'll always be responding to problems that were visible weeks before you knew they existed.

Book a demo to see how Enate's real-time sentiment analysis software turns early warning signals into resolved issues – before they churn.

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