Customer Sentiment Analysis | Definition, DIY Template, & More

Ben Goodey

Customer Service Researcher

Understand your customer’s problems and get actionable insights

‍Most medium to large businesses are sitting on vast amounts of customer feedback, but they’re unable to make sense of it all.

But that doesn't have to be the case. In 2024, analyzing and interpreting feedback can be simple, fast, and accurate—without any human involvement.

The answer, of course, is customer sentiment analysis. This modern technology has evolved so far that it can deliver clear, actionable insights regardless of how nuanced and complex your customer feedback.

In this article, we’ll take a look at the applications of sentiment analysis for customer insights and how businesses can benefit from it.

Expect to read:

What is customer sentiment analysis exactly?
Two examples of sentiment analysis in action.
How customer sentiment analysis works (with step-by-step and template to run your own manual analysis).

Let's dive in.

What is Customer Sentiment Analysis? Meaning.

Customer sentiment analysis is a data processing technique that evaluates and interprets customers' opinions, emotions, and attitudes from their written or spoken feedback.

This analysis transforms subjective “customer sentiment” into actionable insights, allowing businesses to understand and respond to customer perceptions effectively.

Types of Customer Sentiment Analysis

Most customer sentiment analysis tools categorizes feedback as positive, negative, or neutral. However, there are more advanced types that identify emotions, urgency, and other intentions from customer text data.

The most important type of customer sentiment analysis for you to be aware of is "aspect-based sentiment analysis":

"Aspect-based sentiment analysis is a more granular approach to understanding customer sentiment. Instead of assessing an entire piece of text as positive, negative, or neutral, it breaks down the text into smaller units called aspects (aka topics and subtopics) related to specific features of a product or service."

For each aspect, it evaluates the sentiment expressed. This method allows businesses to pinpoint exactly what customers like or dislike, providing more detailed insights into customer preferences and areas for improvement.

Let's see some examples of that in action.

Examples of Customer Sentiment Analysis In Action

The goal of a customer sentiment analysis is to understand large volumes of natural language data (e.g. support chats, surveys, or customer review feedback). The insights help companies:

Understand consumer needs better
Improve operations, products or services accordingly
Enhance customer satisfaction and experience

Let’s look at that in action. Starting with sentiment analysis for customer support teams:

1. Example #1: A customer service sentiment analysis

Companies often have thousands (if not hundreds of thousands) of customer support interactions with customers every day.

At that scale, categorizing and analyzing conversations becomes impossible for a single person to do by hand—and as such, nobody has full visibility into the issues customers are facing with your brand.

companies have millions of tickets per year - feedback analysis is tough

So, how can sentiment analysis help? With it integrated into your contact center, you can leverage the magic of “aspect-based sentiment analysis” to extract topics and their sentiment from every conversation.

For example, in the image below, our sentiment analysis AI is integrated directly into the Zendesk of a video game company.

All their customer conversation are now categorized with topics and subtopics ("aspects"), and each of those topics is analzed to understand its sentiment.

The result?

The gaming company now understands issues causing their customers to reach out (and the impact of them).

They now know that Game Froze has driven 46,000 customers to contact customer service and only 2% of those were labeled positive.

This data can be used to:

Encourage other teams to act with more urgency.
Identify projects to prioritize.
Auto-route tickets causing particular negative sentiments to an “urgent” team.

Read our full guide to customer service sentiment analysis here.

2. Example #2: A customer review sentiment analysis

Another of our clients, a large international airport, leverages customer sentiment analysis to interpret their customer reviews.

For example, out of the 100s of reviews left by visitors to the airport in the past month, the AI tells us that cleanliness was a hot topic—and that 86% of the mentions were positive.

We could interpret this as “cleanliness” is such an important topic that it causes people to leave reviews.

How to Analyse Customer Sentiment Manually (Step-By-Step)

This guide to manual sentiment analysis is meant to illustrate the basic machinery behind sentiment analysis.

If you have more than a handful of customer data to analyze, this will be time intensive. But take a small sample to get familiar with the process.

Not interested in doing this manually? Check out our guide to the best customer sentiment analysis tools here.

Without further ado, here is our guide to manual sentiment analysis:

Manually Analysing Customer Sentiment in Excel

Our guide breaks down into 5 easy-to-follow steps:

Download the template
Choose feedback channels
Collect feedback
Tag feedback by sentiment
Report results: present analysis and drive insights

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Step 1: Download the Customer Sentiment Analysis Template

Before we get into our rundown of the best sentiment analysis tools out there, we’ve provided a link to the template we used for the above section for you to play around with.

Click below 👇 to download the Google Sheet template and mould it to your team's needs.

→ Free Download (No Email Required)‍

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Step 2: Choose Feedback Channel

The 3 most common feedback channels are Support Conversations, Customer Reviews, and NPS and CSAT Surveys.

Here’s some background on each:

A. Support Conversations are an incredibly underutilised feedback channel, which is a shame considering they easily the most useful source of customer feedback.

Support ticket logs (emails, calls, live chats) contain unbiased, qualitative feedback and we've written extensively about why support is the most valuable source of insights.

B. Customer Reviews are a core driver of sales as most customers look there before making a purchase. This makes understanding the reasons behind negative or positive review sentiment important for business growth.

British Airways currently uses our sentiment analytics tool for their customer reviews, check out this case study to help their NPS score take off ✈️

C. Surveys - NPS and CSAT surveys have long been industry standards. However, with questions being asked about potential bias in NPS scores, you’ll need a discerning customer sentiment analysis tool to validate your survey campaigns.

Step 3: Collect Feedback

Next, you’ll want to collect all your raw data in one place. For our purposes here, we’re using Google Sheets, but Excel or other graphing software can work just as well. Going forward, we’ll use this simple example template:

As you can see, we qualified feedback with 3 taxonomies - Channel, Positive Topic(s), and Relevant Department.

To come up with these taxonomies you can, and should, mould your taxonomical classification to your specific needs.

This is called ‘taxonomical tagging’, meaning creating new labels for your data based on your brand’s specific needs. This process is an art unto itself – it requires careful fine-tuning and attention as its results directly affect the quality of your output.

Both the nature and the complexity of the data you gather is up to you. Think carefully about how you’re going to categorise the data - what’s best for another business might not suit yours. So, ask what groups/ types of customers interest you, then target those channels.

Complexity-wise, if your process can handle starting off with more information, go for it. For instance, you might consider gathering more aspect-based sentiment information - you remember the four types from earlier – Polarity, Urgency, Intent, Emotion – to unlock more advanced results downstream.

The best tagging approaches also invent novel taxonomies to suit their own needs:

Tagging Tip 🎯– One favoured tag by our customers is the “ChurnRisk” tag which helps us to identify in real-time when a customer is experiencing a problem or emotion that will likely lead them to cancel their account or leave for a competition – utilising this tag makes prioritising those customers faster and simpler.

No matter what you do, take the time you need to create your own best practice – remember, ‘Quality in = Quality out’. Looking for more guidance? We got you with our extensive taxonomy best practices guide.

Sounds exhausting? Luckily, automation offers a more accurate, less overwhelming shortcut.

The data we presented above probably seem basic– because it is. Machine learning offers an easier and more advanced approach to sentiment classification.

Sentisum’s automated sentiment tool, for example, uses complex automated labelling practices to speed up the process, empowers it with automation (it comes up with potential groupings for you!), and ensures its insights are both granular (deeper and more detailed) and actionable - continue reading about how it accomplishes this.

Step 4: Categorise Feedback by Sentiment

We've pair our classified feedback with a numeric score - focus on the 'Sentiment' pillar on the far right from our previous table:

See those 1’s and 5’s on the far right? We’ve recorded the negative and positive sentiments detected, but also paired them with a sentiment score on a 1-5 scale. Here’s how we measured those numbers:

More complicated for a reason 😏: Customer experience experts will notice the similarity to NPS scoring. Our ratings here are determined using a similar scale but are uniquely useful in this case as they complicate our data in a useful manner.

Here’s how: These ratings allow us to determine the best approach for each variety of sentiment. A ‘1’ rating, meaning Happy feedback, should be noted for its success and applied to future business practices. A ‘5’ rating, meaning ‘Angry’ feedback, on the other hand, indicates an issue that requires an immediate fix.

By doing this, we’ve taken our categorised feedback – the feedback we broke into groups by sentiment – and further understood it, breaking it into micro-groups, by type of sentiment.

At this point, you’ve collected our data, classified it, and ranked it by sentiment. You’re ready, at long last, react to use complex customer sentiment analysis to distil your data for results 😎

Step 5: Report Results: Present Analysis and Drive Insights

If you use the right taxonomies to rediscover your data, then rank their sentiment, you’re ready to take the final step and uncover new, previously-unknown nuggets of knowledge - aka insights. A good rule of thumb for reporting sentiment analysis data is that:

Accessible insights are actionable insights 🙌

The final step of the process, ‘reporting’ your insights, means making them understandable to everyone that might need them, from highly technical employees to those without much knowledge in the area. Your results need to be clear and trustworthy.

How can you test for this? We will get into some examples, given that reporting techniques are one of our specialties at SentiSum, we’d be remiss not to set you up with a few in-depth resources first 👀:

For experts looking for deep knowledge, we’ve compiled an ebook on presenting analysis results internally.

Or, for those looking for a quicker fix, here’s our outline of our survey report examples.

Check either for explainers on everything customer sentiment reporting - your teams and your brand will thank you.

On to our example 😤:

4a. Time-Series Report: ‍

Take a look at a 'Time-Series Report' - this report tracks the percentage change of tags over a given period of time - here's a sample report generated by our sentiment analysis tool:

Courtesy of SentiSum Support Analytics Tool

Time-series change is a great place to start because it offers clear data that should spark direct action - or that’s the hope.

If an untrained eye looked at this graph the Absolute Change +/- and the graphs that accompany it jump off the page. It’s clear that Ease of Booking is doing great and that Staff Conduct and Covid Rules Following are drawing complaints.

Using this chart, any level of employee would be able to conclude that the Conduct and Covid Rules Following need improvement and should be able to start putting a plan into action, treating the new pain point uncovered by your analysis 🩹 - a superb example of accessibility transforming insight into actionable insight.

Automated Sentiment Analysis Solutions

If you have more than a handful of monthly customer feedback and support conversations, automation is critical. It makes sentiment analysis coherent and accurate—allowing your company to trust the insights to make better decisions.

We wrote a guide to the top 6 customer sentiment analysis tools here—if you're interested in a thorough review of tools on the market, please take a look there.

In this guide, I'll walk you through our own customer sentiment analysis tool, SentiSum.

SentiSum - Customer Sentiment Analysis for Customer Service Teams

SentiSum is an AI-powered platform that consumes, understands, and gives detailed insights on your customer feedback (any channel—from call centre conversations to surveys).

We’ve built the platform to be simple to use and customizable, so that everyone in your organization can benefit from customer experience insights.

If you’re in a mid-market and enterprise business, the number of chat messages, calls, emails, and sentiment surveys that your support teams receive can add up to hundreds of thousands of interactions a month.

Your customer support/experience team likely doesn't have the capacity to handle this volume of support tickets. Instead, you might try a different strategy to understand what customers are saying:

You could extrapolate insights from just a sample of sentiment data. But this way, you won’t really have confidence in those insights. Or,
You could periodically undertake a complete analysis of customer support sentiment. Yet, by the time you complete that, the insights are usually out of date.

SentiSum solves these problems by bringing all your customer support conversations and survey responses into one easy-to-use platform.

This way, you can access deep, timely insights on what customers are saying, to drive improvements you can feel confident about.

Book a demo with us to see the tool in action.

Related Reads:

Best Customer Feedback Analysis Tools in 2024

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Customer Sentiment Analysis FAQs

Why is Customer Sentiment Analysis Important?

Customer Sentiment Analysis is crucial for businesses because it enables them to understand the emotions and opinions of their customers towards their products or services.

This understanding helps in identifying customer needs, improving product quality, enhancing customer service, and tailoring marketing strategies.

By analyzing customer sentiment, businesses can also monitor brand reputation, detect shifts in customer preferences, and address issues proactively, ultimately leading to increased customer satisfaction and loyalty, as well as better business decisions and growth.

How is customer sentiment analysis useful? Five customer sentiment analysis use cases

Here are 5 ways sentiment analysis is useful in customer service:

Prioritize customer issues: Sentiment analysis can help businesses quickly identify and prioritize customer issues based on the emotional tone of their messages. This can enable customer service agents to respond promptly to unhappy customers and resolve issues before they escalate.

‍Personalize customer interactions: By detecting the emotional tone of a customer's message, sentiment analysis can help businesses tailor their responses to the customer's needs. For example, if a customer is expressing frustration, a customer service agent can respond with empathy and offer a solution to address the issue.

‍Improve customer experience: By providing personalized and efficient customer service, sentiment analysis can help improve the overall customer experience. Customers who receive prompt and effective solutions to their issues are more likely to remain loyal to a business and recommend it to others.

‍Analyze customer feedback: Sentiment analysis can be used to analyze large volumes of customer feedback to identify trends and patterns. This can help businesses identify areas for improvement and make data-driven decisions to improve their products and services.

‍Monitor brand reputation: Sentiment analysis can be used to monitor online mentions of a brand or product to detect negative sentiment and address issues before they become a larger problem. This can help businesses protect their brand reputation and maintain customer loyalty.

What is real time sentiment analysis in customer service?

Real-time sentiment analysis in customer service refers to the process of analyzing the emotional tone of customer messages or conversations as they are happening, in real-time. This enables businesses to quickly identify and respond to customer issues, prioritize certain conversations, and personalize interactions based on the customer's emotional state.Here are some examples and analogies to help understand real-time sentiment analysis in customer service:

‍Real-time monitoring: Real-time sentiment analysis involves monitoring customer messages or conversations as they are happening, in real-time. This is similar to a security guard monitoring a building in real-time for any signs of danger or security threats. Just as the security guard can quickly respond to any threats they detect, businesses can quickly respond to customer issues as they are identified.

‍Prompt customer service: Real-time sentiment analysis allows businesses to quickly identify and respond to customer issues before they become larger problems. For example, if a customer is expressing frustration about a product issue, real-time sentiment analysis can alert customer service agents to prioritize that customer's message for a quick response. This can help the business resolve the issue before it leads to a negative online review or loss of customers.

‍Personalized interactions: Real-time sentiment analysis can help businesses personalize their interactions with customers based on their emotional state. For example, if a customer is expressing happiness about a recent purchase, a customer service agent can respond with enthusiasm and congratulations. Conversely, if a customer is expressing frustration or anger, a customer service agent can respond with empathy and an apology. This personalized approach can help businesses build stronger relationships with their customers.

‍Improved customer experience: Real-time sentiment analysis can help improve the overall customer experience by providing prompt and effective customer service. Customers who receive quick and effective solutions to their issues are more likely to remain loyal to a business and recommend it to others.

‍Continuous monitoring: Real-time sentiment analysis can be used to continuously monitor customer messages or conversations, providing businesses with a wealth of data that can be used to improve their products and services. For example, if customers are expressing negative sentiment about a particular product feature, a business can use that information to make improvements and better meet the needs of its customers.

Overall, real-time sentiment analysis is a valuable tool in customer service that can help businesses quickly respond to customer issues, personalize interactions, and improve the overall customer experience.

What type of information do companies analyze when conducting sentiment analysis?

Here are the two overarching areas of customer information you can include in your sentiment analysis:

‍Text data: Sentiment analysis of text data is like analyzing a written letter to detect the writer's emotional tone. By detecting the emotional tone of customer feedback, customer service chats, reviews, or social media posts, companies can gain valuable insights into how their customers feel about their products or services.

‍Voice data: Sentiment analysis of voice data is like interpreting a person's tone of voice during a conversation to detect their emotional state. By analyzing phone calls or video chats with customers, companies can detect the emotional cues in a customer's tone of voice, such as frustration or anger, and provide a more personalized response.

What are the main goals of sentiment analysis?

The main goals of sentiment analysis are to gain insights into customer emotions and opinions, and to use these insights to improve customer satisfaction and loyalty. Here are some examples of the main goals of sentiment analysis:

‍Understand customer feedback: One of the main goals of sentiment analysis is to understand customer feedback and opinions about a product, service, or brand. By analyzing the emotional tone of customer feedback, companies can gain insights into what customers like and dislike about their products or services, and make improvements accordingly.

‍Improve customer experience: Another goal of sentiment analysis is to improve the overall customer experience. By understanding customer emotions and opinions, companies can address any issues or pain points and provide a better customer experience. For example, if sentiment analysis reveals that customers are frequently complaining about long wait times, the company can take steps to reduce the wait times and improve the customer experience.

‍Enhance customer engagement: Sentiment analysis can also be used to enhance customer engagement by identifying opportunities for positive interactions with customers. For example, if sentiment analysis reveals that customers are expressing positive emotions towards a new product or service, the company can engage with those customers to learn more about what they like and how they can improve the product or service even further.

‍Prevent negative customer experiences: Another goal of sentiment analysis is to prevent negative customer experiences by identifying potential issues and addressing them proactively. For example, if sentiment analysis reveals that customers are frequently complaining about a specific product feature, the company can address the issue before it becomes a bigger problem and affects customer satisfaction.

‍Monitor brand reputation: Sentiment analysis can also be used to monitor brand reputation by tracking what customers are saying about a brand, product or service on social media, review sites, and other online platforms. This information can be used to prevent a potential PR crisis and maintain a positive brand reputation.

Want to learn more about how SentiSum automates your customer sentiment analysis? Book a meeting with our team here.

How does customer sentiment analysis integrate with existing customer service tools and workflows?

Customer sentiment analysis seamlessly integrates with existing customer service tools through APIs, allowing organizations to enrich their CRM systems, support ticketing platforms, and social media monitoring tools with sentiment insights.

This integration facilitates automated tagging and prioritization of customer interactions, enabling personalized responses and proactive service.

Read our complete guide to customer sentiment analysis tools here.

Can customer sentiment analysis predict customer behavior or trends?

By analyzing trends and patterns in customer sentiment over time, businesses can predict future customer behaviors, such as the likelihood of repeat purchases or churn.

This predictive insight helps companies to tailor their marketing strategies, improve product offerings, and enhance customer experience based on anticipated needs and preferences.

What are the challenges and limitations of implementing customer sentiment analysis in different industries?

Implementing customer sentiment analysis across different industries faces challenges like accurately interpreting context, sarcasm, and idioms in text.

Each industry also has specific jargon and expressions that require customized sentiment analysis models. Additionally, the volume of data and maintaining accuracy across multiple languages and platforms can be challenging.

At SentiSum, we help you overcome industry-specific challenges with human-assisted AI training. In the first few weeks of working together, we make sure nuances are understood and the automated analytics technology takes over from there.

How does customer sentiment analysis comply with data privacy and protection regulations?

Customer sentiment analysis must adhere to data privacy and protection regulations such as GDPR and CCPA.

This involves obtaining consent for data collection, ensuring data anonymization, and providing transparency about how customer data is used. Businesses must also implement secure data storage and processing practices to protect customer information.

Having worked with some many large enterprises, this is all standard practice for us. Reach out and we'll give you happily guide you.

What are the future developments or trends in customer sentiment analysis technology?

The future of customer sentiment analysis lies in advancing AI and machine learning algorithms to improve accuracy in detecting nuances in sentiment.

Development focuses on real-time analysis capabilities, cross-language and cultural sentiment understanding, and integrating sentiment analysis with other data sources for a holistic view of customer experience.

Emerging technologies like natural language understanding (NLU) and emotional AI will further enhance the depth of insights.

Frequently asked questions

Is your AI accurate, or am I getting sold snake oil?

The accuracy of every NLP software depends on the context. Some industries and organisations have very complex issues, some are easier to understand.
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Our technology surfaces more granular insights and is very accurate compared to (1) customer service agents, (2) built-in keyword tagging tools, (3) other providers who use more generic AI models or ask you to build a taxonomy yourself.

We build you a customised taxonomy and maintain it continuously with the help of our dedicated data scientists. That means the accuracy of your tags are not dependent on the work you put in.

Either way, we recommend you start a free trial. Included in the trial is historical analysis of your data—more than enough for you to prove it works.

Do you integrate with my systems? How long is that going to take?

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What size company do you usually work with? Is this valuable for me?

What is your term of the contract?

How do you keep my data private?