Now Detecting Empathy: Sentiment Analysis for Customer Support That Captures the Human Side

In customer support, technical accuracy and quick resolution will always matter. But what makes the real difference—what your customers actually remember, is how your team made them feel. A moment of empathy can turn a tense conversation into a positive one.

With our latest update to EdgeTier, we’re excited to announce that we now detect empathy in agent responses, giving team leaders and QA managers the ability to see, measure, and coach the most human skill of all.

Why empathy matters in customer support

Empathy is more than being nice. It’s the ability to recognise what a customer is feeling and respond in a way that shows you’re aware of their frustration, confusion, or stress. It doesn’t replace solving the problem, but it makes the customer feel heard while you do. And in high-pressure moments, that can change the entire tone of an interaction.

For support leaders, empathy is:

• A sign of emotional intelligence in action
  
• A differentiator in customer satisfaction and retention
  
• A skill that can and should be coached and recognised, not left to chance
  

The challenge? Empathy has always been difficult to track at scale. Now, with EdgeTier, it’s part of your customer support sentiment analysis toolkit.

What’s new in EdgeTier

We’ve added empathy detection to EdgeTier’s existing emotion analytics capabilities. Using real support conversations, we taught our models what empathy looks like, making it possible to spot empathetic replies like:

“I completely understand why that’s frustrating.”
“I can see why this has been irritating. Let me walk you through it step by step.”
“I know you’ve had to repeat yourself, and I really appreciate your patience.”

Empathy is detected at the individual message level, just like the customer emotions we track—so you can:

• Highlight where agents show empathy naturally 
• Spot missed opportunities to be more human
• Use real examples to coach teams on tone and phrasing

Why this matters now

Contact centre leaders today are under pressure: higher volumes and tighter margins. 

AI is transforming customer service, enabling teams to automate more, respond faster, and reduce costs. But as efficiency rises, human skills like empathy, are becoming more important, not less.

Customers want speed, but they still expect human connection, especially when things go wrong. When emotions are high, like during delays, outages, or cancellations, empathy can be the difference between a complaint and a thank-you.

With EdgeTier, empathy becomes measurable, not anecdotal—and part of how you build a truly customer-centred support experience.

Coaching for Empathy with EdgeTier

EdgeTier gives you the tools to act on empathy data right away. Here’s how:

• Spot differences across teams: Compare how empathetic different teams are, or identify where empathy is missing across brands or for specific contact reasons.
  
• No manual tagging: Empathy is detected automatically across all interactions, saving time and ensuring consistency.

• Give better feedback, faster: Use the Coach module to highlight real examples, whether calling out strong responses or correcting missed opportunities to provide more human service.
  

EdgeTier’s full approach to emotion detection

EdgeTier’s emotion and sentiment analysis software is designed specifically for customer support teams. Our models are trained on real-world service interactions, not generic data, so insights are accurate and actionable.

We currently detect:

• Praise → Surface moments of happy customers
  
• Gratitude → Highlight appreciation and strong service
  
• Frustration → Identify dissatisfaction before it escalates
  
• Confusion → Spot where customers need more clarity, or where internal improvements can be made
  
• Empathy → Recognise when agents show genuine understanding, and where to improve
  

See empathy in action

Empathy is hard to coach when it’s invisible. With EdgeTier, now you can see where empathy happens, measure it across teams, and give targeted feedback based on real interactions.