Empathy Revolution: How AI Reads Minds (and Hearts) to Personalize Customer Experiences

AI-powered Sentiment Analysis

The future of customer experience lies in harnessing artificial intelligence to decode emotions, predict pain points, and personalize every touchpoint. As Qwary founder Manoj Rana explains in our recent Software Spotlight podcast interview, “If you want to survive today in the market, you really need to understand your users.”

This exclusive interview with Rana and podcast host Michael Bernzweig offers an insider perspective on how AI-powered sentiment analysis unlocks a new level of customer empathy and engagement.

AI-powered sentiment analysis: decoding emotions, predicting pain points, and personalizing touchpoints.
Harness the power of AI to decode emotions, predict pain points, and personalize touchpoints.

Decoding the Data Behind Emotions

What if you could look inside the minds and hearts of your customers to truly grasp their feelings? Emerging emotion AI makes this possible by analyzing unstructured feedback to uncover sentiment, intent, and drivers.

“Back in my Comcast days, we used to look into numbers like NPS numbers,” recalls Rana. “But over a period of time, the trust in the NPS and other metrics and the quantity of data have gone down because the leadership has somehow misused that metric for self-promotions.”

The solution? Combining qualitative data with AI for actionable insights. As Rana explains:

“With AI, you can break that data down into the sentiments, you can create themes and create themes. You can also monitor what product features have led into that experience and you can improve your product for it.”

Inside the “Empathy Engine” of Qwary

Founded by brothers Manoj and Vishal Rana, Qwary helps over 7,000 brands capture and analyze customer feedback. Their robust platform integrates seamlessly with existing tools while providing proprietary features like:

  • Video interviews to collect authentic emotional responses
  • In-product surveys targeting specific customer journeys
  • Sentiment analysis revealing attitudes and emotions
  • Session recordings showing how users navigate products
  • Custom reporting dashboards with data visualizations

As Rana notes, Qwary is essentially an “empathy engine” – continuously listening, learning, and providing insights to enhance experiences.

Qwary AI in Action: Case Studies

Still skeptical about AI's ability to drive empathy? Here are real-world examples of Qwary in action that we discussed in our interview:

Skelgo Spa

This Italian grocery chain uses surveys to compare customer satisfaction across its brick-and-mortar outlets. By analyzing feedback store-by-store, issues can be quickly identified and addressed through additional staff training.


A delivery provider in the Netherlands, Porterbuddy leverages Qwary to measure and optimize the quality of delivery experiences. As a driver-centric platform, empathy for couriers equals empathy for customers.

Moment House

This live entertainment startup uses post-event surveys to pinpoint technical problems impacting digital shows. Identifying pain points through emotional feedback is key to enhancing their platform.

AI Empathy Set to Disrupt CX

While AI automates basic customer service queries, the next level is infusing emotional intelligence into every interaction. As Rana predicts:

“We are seeing a lot of mid-market companies also, the smaller companies that are looking for this feature. And that is a good thing.”

For companies still relying on superficial surveys and lagging indicators like NPS, integrating AI-powered sentiment analysis is no longer a “nice-to-have” but a competitive necessity.

5 Ways Brands Can Start Building AI Empathy

Ready to tap into the power of emotion AI? Here are 5 tips to begin uncovering actionable insights:

1. Install tracking to capture more signals: Integrate solutions like Qwary to gather behavioral, conversational, and survey data passively at scale.

2. Ask open-ended questions: Seek qualitative feedback through video interviews to collect authentic emotional responses.

3. Analyze unstructured data with AI: Uncover themes, trends, and drivers through sentiment analysis and natural language processing.

4. Personalize in real-time: Use session recordings and user analytics to target surveys and offers based on observed behaviors.

5. Close the loop: Follow up on negative experiences immediately and use feedback to refine products, services, and interactions continuously.

The future of CX is empathetic and predictive. With AI as your co-pilot, unlock unprecedented levels of customer understanding at scale.

Be sure to listen to our exclusive Software Spotlight podcast interview with Manoj Rana, founder of customer experience platform Qwary. In the episode, he discusses the company's rapid growth to over 7,000 brands and 2.7 million surveys. Qwary captures customer feedback and turns data into actionable insights to elevate satisfaction, improve adoption, and build loyalty.


What is sentiment analysis in CX?

Sentiment analysis uses AI to detect emotions, attitudes, and opinions within customer feedback and interactions. This qualitative data offers insights that traditional CX metrics often miss.

How can emotion AI improve customer service?

By analyzing unstructured conversations, AI can flag frustrated customers instantly so agents can intervene with empathy. It can also suggest appropriate responses based on sentiment.

What AI capabilities drive empathy?

Capabilities like natural language processing allow AI to interpret emotions and meaning from text and speech. Machine learning algorithms also uncover patterns predictive of satisfaction, churn risk, etc.

How does Qwary analyze emotions?

Qwary uses proprietary natural language processing and machine learning models to extract sentiment signals from surveys, session recordings, product usage data, and more. These insights help brands enhance experiences.

Why is qualitative data better than quantitative data alone?

While metrics like NPS provide high-level trends, qualitative feedback reveals the underlying “why” behind satisfaction and intent. AI makes analyzing open-ended responses at scale possible.

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