What is contact center sentiment analysis? A guide to how it works, its benefits, and more

As humans, we use a variety of skills to determine how someone is feeling. We listen to what they’re saying, watch their expression and body language, listen to changes in the tone of their voice, and more. It’s not so easy to do this when you’re not face-to-face. As the majority of brand/customer interactions now happen digitally, this can make things very difficult. That’s where contact center sentiment analysis comes in. 

With good sentiment analysis tools you can bridge the gap and understand how people communicating digitally are feeling just as well (if not better) than you would if you were face-to-face.

What is sentiment analysis?

Sentiment analysis is the process of identifying emotions and attitudes from phone calls and digital conversations.

Contact center sentiment analysis is usually enabled by AI-powered conversation intelligence, which can accurately analyze things like words used, tone of voice, and the direction the conversation takes to accurately determine how the speaker/writer is feeling.

Types of sentiment analysis

There are several types of sentiment analysis contact centers could find useful:

• Emotion-based sentiment analysis: This is a nuanced approach that goes deeper than simply telling you whether emotions are negative, positive, or neutral. 

Emotion-based sentiment analysis tools can tell you the precise emotions that a customer is feeling. For example, your emotion-based sentiment analysis tool might identify that a customer is beginning to get frustrated with their call through increasingly abrupt sentences and a brusque tone.

• Aspect-based sentiment analysis (ABSA): This works by pinpointing specific negatives and positives about a product or service that customers have mentioned in customer communications. 

For example, if a lot of people make comments like “The item was not true to size.” and “I need to exchange this item for a bigger size.”, your ABSA tool would tell you that a significant number of people have negative opinions on your sizing policy.

• Fine-grained sentiment analysis: This is similar to emotion-based sentiment analysis, but it gives a more fine-grained view of a customer’s opinion of specific elements of a product or service. 

A fine-grained sentiment analysis of text will determine the specific emotions that the writer is feeling (and the intensity of those emotions) based on things like word choice and sentence structure.

• Lexicon-based sentiment analysis: This analyzes word choices to determine whether the sentiment expressed is positive, negative, or neutral. 

While this approach is useful for things like survey responses, which leave little room for nuance, it’s less accurate in conversational contexts. For example, it has difficulty recognizing sarcasm.

• Intent-based sentiment analysis: This focuses on working out the intent behind a communication. For example, intent-based sentiment analysis could figure out how likely a customer is to make a purchase based on the questions they’ve asked during a sales conversation.

• Multimodal sentiment analysis: This analyzes multiple sources, such as video, recorded conversations, online reviews, social media posts, and emails to give an overview of audience sentiments. 

For example, if you want to know how people feel about a particular product you might ask your multimodal sentiment analysis tool to comb your archives and the internet for all mentions of that product and report back on the broad sentiments associated with it.

• Machine-learning sentiment analysis: This is more a method of sentiment analysis than a type, but it’s worth mentioning here. Machine learning uses sophisticated AI algorithms and automations to steadily “learn” more about the sentiments it encounters. 

The more data it’s exposed to, the better it gets at things like identifying and even predicting customer sentiment. Ultimately, machine-learning tools are capable of incredibly complex analysis yielding deep and nuanced results.

• Hybrid approach: The hybrid approach is what most good sentiment analysis tools will provide. Hybrid approaches combine a number of customer sentiment analytics and types to give a well-rounded, accurate, and nuanced picture of what your customers are thinking and feeling.

Sentiment analysis example: How it could work in your contact center

So, now we know sentiment analysis’s meaning. But how does sentiment analysis work? How can sentiment analysis be used to improve customer experience in a call center?

Let’s explore one of the many sentiment analysis examples of how you could use it to supercharge your customer interactions.

Let’s say that one of your contact center representatives is having trouble dealing with a particular customer query. The customer isn’t shouting or insulting your representative, but the issue is taking a long time to resolve, and your representative wants to keep the customer happy.

Fine-grained, emotion-based sentiment analysis will quickly be able to pick up on nuanced cues in the customers’ tone and word choice and alert the representative to rising frustration levels. The representative can then modify their own tone and approach to soothe and reassure the customer.

Similarly, sentiment analysis can be applied to call monitoring in your contact center. When your sentiment analysis tools detect calls going wrong, they can alert supervisors and managers to step in and help out before the customer gets too frustrated.

Benefits of sentiment analysis in the call center

Customer sentiment analytics bring a lot of advantages for contact centers. Here are just a few of the ways that customer sentiment analysis tools could benefit you:

• Complement sources of customer feedback
• Identify root causes of dissatisfaction
• Improve customer engagement and retention
• Support more efficient quality management (QM)
• Get a more holistic view of agent performance
• Aid accurate forecasting
• Fuel product and service improvement
• Better tailor marketing and sales efforts

 Complement sources of customer feedback

Before sophisticated sentiment analysis tools came along, it was much harder to get a read on what large numbers of customers felt about your brand. Brands were reliant on asking for customer feedback in the form of surveys, which aren’t always reliable and many customers don’t want to fill out.

With sentiment analysis, brands can get an accurate idea of how people feel about their business and products, from things like conversations, messages, social media posts, and more. This complements direct customer feedback and helps to build an accurate picture of customer opinion.

Identify root causes of dissatisfaction

Being able to quickly pinpoint customer sentiment in real time makes it a lot easier to establish the root causes of customer dissatisfaction. For example, if a customer becomes angry every time they mention a certain keyword, sentiment analysis tools will quickly pick up on this and notify the agent taking the call.

The agent will then be able to resolve the customer’s problem much faster and more easily.

Improve customer engagement and retention

Real-time customer sentiment monitoring means that your call center agents can quickly save interactions from going south. For example, supervisors can step in when needed to help out frustrated customers, and agents themselves can modify their tone or approach in response to their customers’ sentiments.

This makes for a much more satisfying customer experience, which will, in turn, boost customer engagement and retention.

Support more efficient quality management (QM)

Performing sentiment analysis on every customer interaction makes quality training and quality management much easier. Quality managers can see at a glance which interactions went badly and which went well, and they can analyze these interactions for quality insights. 

They can then use these insights to train staff in how to maintain high interaction quality and avoid negative interactions. 

Get a more holistic view of agent performance

Sentiment analysis metrics can be useful for agent evaluation. As well as quantitative metrics, like average handle time, managers can also see how customers typically respond emotionally to each agent. This gives a much more holistic and nuanced picture of agent performance. 

For example, say one agent has a lengthy average call time. However, sentiment analysis metrics also reveal that customers typically enjoy their interactions and leave satisfied. In that case, managers will understand that they’re a highly skilled agent who delivers excellent CX; something they might miss from just the call-length metric.

Aid accurate forecasting

Understanding the direction of customer sentiment can be a huge help both with forecasting and with workforce management (WFM).

For example, if sentiment analysis predicts a general downward turn in customer attitudes toward your brand, you can prepare your support staff to deal with more unhappy customers in the upcoming days. 

You can also coach them in the kinds of issues that customers may be experiencing, which will help them to provide a much better, more efficient, and more satisfactory experience for the customer.

 Fuel product and service improvement

By feeding back customer sentiment metrics to R&D, upper management, and marketing departments, your sentiment-gathering contact center automation tools can help improve everything from product quality to the way your brand does business. 

Customer sentiment analytics are great for learning what customers think about your product, services, and your brand in general. You can use sentiment analytics to take customer opinions on board and make improvements.

Better tailor marketing and sales efforts

Knowing what customers like and don’t like is very useful for tailoring sales and marketing efforts. You can use sentiment analysis insights to train sales staff and create marketing messaging that customers are likely to respond well to.

Challenges of any sentiment analysis model

No sentiment analysis model is perfect. The AI that underpins sentiment analysis models and tools has some challenges. It’s worth noting, though, that AI technology— especially machine learning models—are improving at a rapid rate. So, what’s a challenge for AI sentiment analysis today may not be a challenge in the very near future.

Some prominent sentiment analysis challenges include:

• Assessing interactions in context: While sophisticated machine-learning AI models are getting very good at complex, context-related sentiment analysis, more basic models struggle to understand some interactions within their proper context. 

For example, the word “silly” can be used to mean both “fun and lighthearted” and “foolish” depending on the context. A customer may describe an advert as “silly” in the first context, but a poorly trained sentiment analysis tool may not take that context into account and record this as a negative sentiment.

• Understanding irony, sarcasm, and idiomatic language: Similarly, some sentiment analysis models struggle to interpret irony, sarcasm, and idiomatic language. 

For example, if someone sarcastically says that they love your product, analysis tools may record this as a genuinely positive interaction. This can become a big problem when dealing with international audiences, as some nations habitually use very idiomatic language. 

• Negotiating negation and other nuances: Negation is commonly used in idiomatic speech, but some sentiment analysis models have trouble understanding it. 

For example, if someone said “I wouldn’t say I enjoyed the experience.”, what they mean is that they didn’t enjoy the experience. But the way the statement is phrased makes it hard for sentiment analysis tools to pick out a clear meaning. 

More nuanced examples like “I wouldn’t say I enjoyed the experience, but I wouldn’t say I hated it either.” can also be very confusing for sentiment analysis AIs.

Use cases for customer sentiment analysis tools in the contact center

Let’s take a look at examples of sentiment analysis in action. Here are some practical use cases for customer sentiment analysis tools in a contact center:

Improving customer experience

Sentiment analysis metrics can be used to augment your contact center metrics in real time. 

This allows you to keep a close eye on how customer interactions are going and step in or advise your agents in real time to ensure the best possible results for the customer. With a solution like RingCX, you may not even need to get involved at all. The AI Assist feature provides real-time suggestions to help agents turn calls around themselves:

Being able to easily tell how a customer is feeling at any given moment during an interaction is very useful for tailoring the interaction toward a satisfying resolution. This results in a huge boost to customer experience.

Understanding and enhancing agent performance

Sentiment analysis can be used to monitor and analyze agent performance. The metrics will quickly show if someone is having trouble connecting with customers or if they’re exceptional at making customers happy. 

You can use this information to reward good agents and give support where needed. You can also use the insights you get from sentiment analysis of customer service conversations to train your agents for better performance.

This is particularly useful if you use a cloud contact center, as it allows managers to get an accurate picture of agent performance without having to be physically present.

Achieving optimum workforce optimization

Sentiment analysis can help you optimize workforce performance continuously in real time. By monitoring customer sentiment as conversations are occurring, your agents and their managers can spot problems arising before they get serious and quickly modify the interaction to improve things. 

Sentiment analysis also provides valuable insights to customer service agents that help them to do the best possible job at all times.

Contact center sentiment analysis: One more way to optimize performance

AI-enabled technology is changing the world. You can use it to change your contact center for the better with RingCentral RingCX.

RingCentral RingCX uses conversation intelligence and AI-powered analytics, including sentiment analysis, to help you support and bolster the performance of your call center agents. 

With the insights and support provided by RingCentral RingCX, you can make huge improvements to call center performance and ensure a great experience for every customer.

To find out more, view a demo today.

Contact center sentiment analysis FAQs

What’s different about call center sentiment analysis compared to other forms of feedback?

Contact center sentiment analysis is focused on solving customer problems in real time and optimizing call center agent performance. For example, call center sentiment analysis tools may “listen in” on calls and alert agents or supervisors when sentiments are taking a turn for the worse, alerting them to the fact that action needs to be taken to rectify any issues. 

It can also pinpoint areas where agents are struggling to connect with callers and suggest ways to improve. This is in contrast to other forms of feedback, like surveys or customer reviews, which can’t be acted upon in real time.

Can you apply sentiment analysis in real time in a contact center?

Sentiment analysis works well in real time for contact centers. Sentiment analysis tools can monitor customer calls, make intelligent live suggestions to help out agents, and alert supervisors when they detect that a customer is getting frustrated.

What are some ways to improve sentiment score in your contact center?

There are many ways to improve your contact center’s sentiment score. Here are a few popular methods:

• Use sentiment analysis tools to monitor and gain insights into customer sentiments.
• Train your staff in listening and communication skills.
• Don’t be afraid to apologize to customers.
• Demonstrate that each caller has been heard, and acknowledge their issues.
• Monitor customer sentiments over time with sentiment analysis tools.

Originally published Dec 11, 2024, updated Dec 12, 2024