Sentiment analysis in call centres: what it is, how it works and why it improves customer service

Over a decade ago, with the arrival of speech analytics, call centres were able to reinvent themselves and move beyond traditional metrics. But today, the real breakthrough comes with sentiment analysis in the call centre, a technology that not only reveals what customers say when they call, but also helps understand how they feel throughout the conversation.

All of this is made possible thanks to artificial intelligence for the call centre and, more specifically, to speech analytics, which extracts valuable insights to improve customer service. As a result, calls stop being simple recordings kept “just in case” and become an extremely valuable source of information.

WHAT IS SENTIMENT ANALYSIS IN A CALL CENTRE

Sentiment analysis in a call centre involves identifying the emotional tone behind a customer conversation: satisfaction, anger, stress, impatience, doubt, or frustration.

Thanks to advances in natural language processing (NLP) and its integration with increasingly sophisticated voice analysis technologies, it is no longer just possible to classify a call as positive or negative, but also to capture nuances such as anger, impatience, stress, doubt, or satisfaction.

Applied to a call centre, this represents a major shift. There is no longer any need to manually review a small sample of calls to assess service quality; instead, it is now possible to analyse every conversation, without exception, and to do so automatically.

The result is a far more complete, objective, and realistic view of what is happening in the day-to-day operation of the call centre.

HOW SENTIMENT ANALYSIS WORKS IN A CALL CENTRE

Although it may seem complex from the outside, the way sentiment analysis works is actually quite intuitive:

1. THE CALL IS RECORDED AND TRANSCRIBED

Everything starts with the conversation between the customer and the agent. That call is recorded and automatically converted from speech to text using increasingly accurate speech recognition systems.

2. NLP ANALYSES CONTENT AND CONTEXT

From there, natural language processing comes into play, analysing the content of the conversation while taking into account not only the words themselves, but also their context. The system can identify whether an expression is neutral, ironic, negative, or positive, and interpret the true meaning behind the message.

3. THE VOICE IS ANALYSED: INTONATION, PACE, VOLUME AND PAUSES

But the analysis does not stop at the text. The technology also examines how the customer speaks: intonation, pace, voice volume, and pauses. These elements provide a significant amount of emotional insight that is often not explicitly expressed.

4. A SENTIMENT SCORE IS ASSIGNED

With all this data, the system assigns a sentiment score to the call and classifies it according to its emotional tone.

This information is presented in a clear and actionable way for supervisors, quality managers, and executives, without the need for human intervention. In addition, this artificial intelligence for the call centre not only automates the analysis, but also learns from every interaction, improving its accuracy over time.

WHAT BENEFITS DOES IT BRING TO THE CALL CENTRE

The main advantage of sentiment analysis is that it allows you to truly understand what happens during a call. It is not just about knowing whether an issue was resolved, but about understanding how the customer experienced it.

This insight is extremely valuable for call centre supervisors and managers, as it helps them identify patterns, pinpoint friction points, and take action before negative situations occur again. It also supports the early detection of dissatisfied customers or those at risk of churn.

Sentiment analysis delivers the following direct benefits:

• Identifying friction points before they become recurring issues
• Spotting dissatisfied customers or those at risk of churn
• Improving service quality through real, data-driven feedback
• Understanding which scripts or processes create tension
• Automatically prioritising critical calls

In addition, sentiment analysis is not only applied to customers; it can also be used to assess the experience of agents themselves. By generating automatic sentiment scores by agent, team, or campaign, it becomes possible to correlate these insights with key performance indicators:

By generating automatic scores per agent, team, or campaign, you can:

• Identify training needs
• Detect emotional overload or stress
• Correlate sentiment with performance KPIs

A TECHNOLOGY DESIGNED FOR PEOPLE

Although we talk about artificial intelligence, data, and automation, the ultimate goal of sentiment analysis is deeply human: to better understand people. To understand what concerns them, what frustrates them, what they value, and what they expect from a brand when they pick up the phone.

This advanced speech analytics technology plays a key role in advancing customer service. It is one of the most attractive call centre solutions on the market, because ultimately, the difference between good service and excellent service lies not only in solving problems, but in doing so while understanding how the person on the other end of the line feels.

A SMARTER, MORE CONNECTED CALL CENTRE

If you want to take customer service to the next level, discover how the contact centre software Omnichannel Contact Center unifies all interactions into a single intelligent environment, integrating speech analytics to deliver a fast and consistent service across all channels.

To learn how to implement these solutions in your call centre, call 900 670 750 or fill in the form below, and one of our experts will contact you to provide personalised advice.

REQUEST INFORMATION ABOUT SENTIMENT ANALYSIS FOR THE CALL CENTRE