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Customer sentiment analysis — What is it and why does it matter?

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Is customer sentiment analysis at the top of your customer experience plan? 

Time it was!

A better look at what your customers think (and feel) about your company lets you make better decisions when it comes to properly planning and providing a stellar experience to your consumers. 

Knowing exactly what they like or dislike in terms of your product and the services you provide means you’ll be able to:

  • Provide customized experiences
  • Offer proactive support
  • Make better choices for the future of your product and users
  • Increase the return on investment for campaigns multiple departments in your organization are running
  • Safely manage your brand reputation and avoid risk escalation

That’s exactly why next we’re taking you through every step you need to understand to get started with customer sentiment analysis. Plus, we’ve talked to a couple of customer service experts to get their insights on how to use sentiment analysis for customer feedback and service improvement.

What is customer sentiment analysis?

Customer sentiment analysis is a tool customer support team managers use to quickly and accurately assess customer feedback. 

It allows support teams to spot patterns in customer feedback and reactions, allowing them to put together more successful strategies and solutions that can improve customer satisfaction and experience. 

Generally, sentiment analysis relies on algorithms that analyze customer data such as emails, comments, reviews, surveys, and even phone calls. These resources are used to evaluate the language used and determine a customer’s feelings. 

This analysis can help to identify customer pains, preferences, and spots that require improvement. It also helps leaders find trends in customer feedback and make better decisions about how to respond to customer needs or issues.

Why is customer sentiment analysis important?

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But you can’t provide that ideal experience without understanding your customers.

That’s where customer sentiment analysis comes in.

Customer sentiment analysis is used to find out how customers feel about a particular product or service, including the customer support they received.

A core customer sentiment analysis benefit is that support teams can use it to spot issues customers are dealing with before they become a larger problem. 

By tracking customer sentiment over time, you can detect negative sentiment trends and address them on time. This is particularly important as it allows you to stay ahead of complaints and keep customer satisfaction rates at peak levels.

Once they understand how customers feel and think, support teams can also create personalized customer experiences to ensure each buyer receives the help they need. In fact, 90% of consumers will spend more with companies that personalize the customer service they offer.

How can sentiment analysis be used to improve customer experience?

We’ve talked to a couple of customer support professionals to understand just why they value customer sentiment analysis so much within their organization.

One of the most common reasons was to spot trends and patterns when it comes to customer sentiment.

Craig Stoss, Director of CX Transformation at PartnerHero, says the customer sentiment has to match, or alter, the metrics and goals you typically measure within a support context:

“For example, when we report that our customer satisfaction score (CSAT) went up, we know that CSAT is not fully accurate, as it typically captures the happiest and the angriest of customers, not those in the middle. 

“It’s completely likely that once you aggregate the sentiment from your social media presences, review comments, ticket data, or other data, you will learn something that contradicts the metrics you look at every day. That’s because you’re using a wider set of data that comes without the filters of who responds to surveys and specific pointed questions.”

For Craig, this alone is tantamount to their mission of deeply understanding the customer and partnering with them to create positive customer experiences:

“Sentiment is important to the partnership. When we think about a value conversation with our clients, we know that it isn’t the metric that is the most important, it’s the story behind the metric, what we will do based on that story to improve the metric, and the impact of those actions. Combining all of that alongside the metric gives more value to any support organization.”

Jaakko Jutila, Vice President of Customer Support at Basware, says that by applying sentiment analysis to the initial and subsequent comments made by a customer, they gain insights into the specific issues that frustrate the customer from the very beginning. This information becomes instrumental in prioritizing product development efforts, allowing them to eliminate frustrating features and enhance the overall product experience.

By analyzing customer sentiment, Jaakko’s team instructs support consultants if a particular response will exacerbate the customer’s anger, enabling agents to provide tailored and effective communication.

Jaakko further notes that one of the most evident advantages is its ability to prevent ticket escalations: 

Types of customer sentiment analysis

In the customer service space, there are four core types of consumer sentiment analysis you should know how to implement.

Aspect-based customer sentiment analysis

What it is:

Aspect-based customer sentiment analysis is a technique that can help you gain deeper insights into what customers think about specific aspects (or elements) of your products or services. This type of analysis goes beyond simply classifying customer feedback as positive, negative, or neutral as it helps you tell which particular aspects customers like or dislike. 

This gives customer support teams a more granular look at how customers think. For instance, when you receive a customer complaint, aspect-based sentiment analysis will tell you exactly which parts of a product or service the customer had an issue with by looking at the words they’re using to describe their experience. This lets you identify and fix the root issue behind a complaint before things escalate.

How to get started:

To perform aspect-based sentiment analysis, you first need to decide which are the main aspects (elements or features) of your product or service that customers care about. 

These can be factors like specific features, pricing, usability, or customer service. As a customer support manager, you’ll want to focus on the latter. This, however, doesn’t mean you should run this analysis independently. In fact, it’s a good idea to pair up with the other departments in your organization.

Once the aspects are clarified, you’ll most commonly use machine learning (ML) or natural language processing (NLP) techniques to analyze large volumes of customer feedback data. Sources for these insights include customer reviews, surveys, social media posts, and support tickets. 

Fine-grained customer sentiment analysis

What it is:

Imagine breaking down each sentence into words and classifying them into positive, neutral, or negative ones. By analyzing language cues like emoticons, punctuation, sentiment shifters and intensifiers, an algorithm based on fine-grained sentiment analysis can classify customer messages (even phone calls and audio files) based on the trained sentiment labels. 

The insights from fine-grained sentiment analysis can help customer support teams prioritize issues, improve processes, tailor FAQ answers and knowledge base articles, and provide a more targeted response to individual customer queries.

How to get started:

To perform fine-grained sentiment analysis, you’ll need a model that has been trained on a large dataset of text examples labelled with specific emotions like fear, satisfaction, hesitancy, anger, etc. You can then apply this model to customer reviews, comments, emails, transcripts, and surveys. 

The output is typically a series of on-text highlights that showcase diverse emotions. Based on these, you can prioritize responding to customers displaying strong negative emotions like anger. Or you can track emotions in time to monitor customers’ responses to a change like new feature updates or interface changes.

Analysis focused on emotion detection

What it is:

Keeping emotions in sight is perhaps one of the most popular types of customer sentiment analysis as it’s easy to apply and doesn’t require setting up complicated AI-based models.

It simply implies focusing on analyzing the tone, emotions, and attitudes expressed by customers in the way they interact with your business through channels like emails, social media, online review websites or surveys, and chat conversations. 

So, what’s your goal in this case? To understand how people truly feel about the service and experience your agents offer. For instance, looking through tickets can help you spot negative emotions and opportunities for improvement. This way, you can talk to your agents and impose better practices for improving the way agents communicate and talk to customers.

How to get started:

What matters most is understanding how to read these tickets, messages, posts, reviews, or transcripts.

For example, when customers have a negative experience with your support team, they’re often direct when it comes to expressing their frustration and disappointment, even following up several times if needed. But they can also use neutral or even positive language to express this customer dissatisfaction. That’s exactly why it’s best to do the analysis yourself and keep things in context, paying particular attention to subtle dissatisfaction and past interactions.

Remember: Don’t pay attention just to the words customers use. Context, grammar, punctuation, spacing, emojis, and even non-verbal cues can help you detect emotions correctly. 

With Kaizo, you can turn to tickets to analyze customer sentiment, see how agents respond to tickets, understand whether they were empathetic enough or not, and more. 

Kaizo’s Customer Sentiment metric is a valuable tool for assessing customer satisfaction and improving service quality. It provides insights into the overall customer experience and helps identify areas where things may have gone wrong. 

Unlike traditional measures like CSAT, this metric goes beyond surface-level ratings and uses advanced natural language processing algorithms to analyze the sentiment behind customer interactions accurately. 

By detecting negative sentiment early on, it enables proactive issue identification and swift action to address concerns and ensure customer satisfaction. The metric also highlights areas of strength and training needs for support agents, allowing for targeted coaching and improved performance.

But that’s not all.

Kaizo’s Empathy Score metric is designed to evaluate and measure the empathy levels displayed by customer support agents during interactions. Maintaining a professional, empathetic, and positive approach is crucial for representatives, even in challenging situations. 

The AI-driven Empathy Score enhances the quality of customer support by providing objective measurements of empathy, ensuring consistent evaluations across different interactions and agents. 

It generates data-driven insights by analyzing language patterns, sentiment, and tone, empowering the QA team to identify areas of strength and areas where additional training may be needed. 

Agents receive feedback based on their empathy scores, helping them improve their skills and contribute to their professional development.

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Focusing on intent analysis

What it is:

Want to know exactly what customers will do next or have a rough estimate of when they’ll be ready to make a purchase?

Enter intent analysis.

This technique helps agents understand the intent or purpose behind what a customer is saying. Customer support representatives can then provide better, more targeted responses to customer queries. Plus, it’s a great tool if you want to help your sales or product teams with extra insights.

How to get started:

You can tell intent by largely relying on the context. Customers can even use different words to express the same underlying intent. For example, a customer might ask “Can I get more pictures of the product?” or “What are your return policies?” These questions both have some form of purchase intent behind them. The only issue: Each of these customers might be at a different lifecycle stage. And that’s exactly where context comes in to help you predict the most likely intent. 

By understanding intent at each stage, support agents can improve the responses they provide to make sure they’re not losing a prospect or customer. As a manager, you can dig into past agent-customer interactions to analyze the way your agents respond and make adjustments. Small changes can help you improve first-reply resolution rates, agent productivity, and customer satisfaction rate.

Getting started with customer sentiment analysis for customer service

If there’s one thing you need to remember right now is that improving customer experience means helping your agents grow. But you can only understand what soft or hard skills need more development by looking at how they interact with customers. 

And then there’s the second part of it all: Understanding customer sentiment, needs, and pains to better tailor future interactions.

To get a good grasp of customer sentiment (as well as agent performance while you’re at it), start by analyzing past support tickets. Choose tickets that were challenging or took longer than usual to complete. It’s then up to you to decide on which type of sentiment analysis you want to use. 

You can opt to perform all four types of customer sentiment analysis, but this won’t be a good solution if you’re looking to get quick results. We recommend choosing a mix of emotion and intent-based analysis to begin with as they’re easier to implement and require resources you already have such as access to support tickets, customer reviews, or surveys.

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