What Is Customer Sentiment Analysis? Use-cases and Applications

Businesses use customer sentiment to measure a buyer's perception about their brand. If done well, it can provide answers to questions like:

• How do customers feel about your brand, products or services?
• What do they say about your company on social media?
• Are they satisfied with your customer support services?

Sentiment analysis can be leveraged to fully understand your target audience and personalize every aspect of your business, so that it revolves around your buyers.

Personalization is the key to success after all.

According to Mckinsey, brands that get it right can potentially generate 40% more revenue than those that don’t. It goes to show that building an awesome product is not enough anymore.

You also need to take into account user sentiments.

Customer sentiment analysis can help you achieve a greater level of user understanding and personalization, thereby improving customer experience.

What is customer sentiment analysis?

Customer sentiment analysis is the process of detecting and understanding the opinions, emotions, and attitudes of consumers towards a certain product, service, or company.

It lets you look into the minds of your buyers and get an idea about what users think and feel when they interact with your brand (before and after), which is really important since:

• 39% of buyers won't purchase from a brand that doesn't provide personalized experiences
• Eighty six out of a hundred customers will pay more to companies that offer a great consumer experience

Otherwise known as user sentiment analysis, it is usually performed on surveys, social media posts, feedback, and reviews to uncover valuable customer insights.

You can additionally use web queries and press releases.

The information acquired can help track the overall customer emotions and gauge user satisfaction levels, thus enabling you to enhance features and optimize marketing messages.

So how does it work?

Customer sentiment analysis uses machine learning algorithms like NLP (Natural Language Processing) to classify emotions into different polarities, such as positive, negative, or neutral.

NLP is a branch of artificial intelligence that combines computer science and linguistics to analyze and interpret the human language. It can process a large amount of customer data in a relatively short amount of time.

Usually, a simple sentiment analysis process involves these steps:

1. Collect textual data from different sources
2. Remove irrelevant information (words, special characters)
3. Tokenize the text into singular words or phrases
4. Extract relevant attributes (phrases, lexicons)
5. Group attributes into different sentiment categories

The algorithm then assigns a customer sentiment score to attributes on the basis of polarity and magnitude (intensity of the sentiment), which determines the tone and emotion expressed.

Four main types of sentiment analysis

Sentiment analysis algorithms use natural language processing to determine emotions and customer satisfaction levels.

As there is a wealth of data available in the form of customer reviews, feedback, and comments, it has now become easier to gauge the sentiment of the text (and your buyers).

That being said, let's delve into the four most popular methods of sentiment analysis.

I. Fine-grained analysis

Fine-grained sentiment analysis goes beyond positive or negative. You go a step further and divide emotions into five polarities: very positive, positive, neutral, negative, and very negative.

By using text analysis, you can assign a customer sentiment score to each review, say from 1 (very negative) to 10 (very positive).

This approach uncovers nuanced details and offers a more comprehensive perspective, which can help you pinpoint areas for improvement in your products and services.

II. Emotion detection

Forming an emotional connection really works for some brands. In fact, 71% of buyers will recommend a brand based on their emotional attachment to it.

Emotion detection models help you decipher both positive and negative sentiments, like happiness, anger, and sadness.

It’s mainly done using machine learning and/or lexicons. Lexicons are words or phrases associated with a particular topic, which makes it easier to segregate terms according to their sentiment.

However, lexicons become difficult to use when you consider the ways in which people express themselves.

Like a user might say, “your product rocks” and you won’t know where to place it. But artificial intelligence combined with machine learning technologies can solve this problem.

III. Aspect-based analysis

Aspect-based analysis is sort of like fine-grained sentiment analysis. Only difference is that it focuses on specific topics and subjects that people are talking about.

Businesses often use it to analyze consumer review data by mapping sentiments to different features of a product or service.

Here’s an example.

In a smartphone review, a brand might analyze the sentiment for camera quality, interface, and design. Suppose a user says, “the design is okayish, the camera quality is super bad.”

When this feedback is put through a sentiment analysis software, it will group “okayish” and “super bad” as negative customer sentiments.

IV. Intent analysis

Customer intent is the thought that drives your buyers to make a purchase decision.

Intent analysis tells you whether your prospects intend to endorse your brand and its products. It gives you a better idea of what your buyers want as well as the purpose behind their actions.

As in, what do they wish to do when they first visit your website? Or why do they constantly contact your customer service teams?

So when they show the intent to make a purchase, you can immediately reach out to them. This saves a significant amount of time and resources, allowing you to pursue legitimate clients.

Why is sentiment analysis important for your business?

Businesses that want to increase their customer base and see a positive growth in revenue must make it a point to use sentiment analysis.

And why not?

Knowing your buyer’s sentiments towards your brand and your products not only helps smooth out customer interactions but also increases customer satisfaction and loyalty.

With this in mind, let’s go ahead and take a deeper look into the many benefits of customer sentiment analysis.

#1 Personalize customer experiences

When you center yourself around the preferences and wants of your buyers, you start meeting customer expectations and consequently improve the customer experience.

It is a no brainer really.

You can effectively measure emotions via sentiment analysis. By assigning quantitative scores to qualitative data, it becomes effortless to get insights that work.

We know that personalization is a tough job.

You have to consider a bunch of factors besides the myriad of emotions that buyers go through before they make a purchase.

All the while identifying and addressing issues to make things simple.

The insights gathered from sentiment analysis can make your job easier and tell you a lot about customer needs and pain points, making personalization only a slightly difficult process.

#2 Reduce customer churn rates

Churn rate refers to the percentage of customers you lose in a given period of time, like in a month or a year. Although it looks bad in your annual reports, it is a normal part of any business.

This doesn’t mean that you should ignore it and let the numbers go wild.

Customer sentiment analysis helps keep this percentage down and improves your customer lifetime value (CLV). Sentiment scores enable you to spot the types of emotions along with the magnitude of what your buyers are feeling.

Are they happy, frustrated, excited, or dissatisfied? And if they are, to what extent?

You can prioritize complaints, offer assistance, and solve problems. Addressing sentiments at the right time can boost customer retention and brand advocacy.

#3 Manage brand reputation

Customers love it when your business has a great brand personality. It doesn’t have to be something outrageous, like Old Spice, to capture attention or build a huge fan following.

If your brand is consistent and trustworthy, then it’s enough.

Positive sentiments boost brand equity and encourage user engagement (customer feedback, reviews, transactions). Negative feelings on the hand can lead to a company’s worst nightmare.

Remember, it takes just one comment from an unhappy customer to severely damage your reputation.

Brand monitoring with sentiment analysis enables you to manage your brand reputation both online and offline. You can use social media trackers to capture mentions and comments related to your brand.

Some sentiment analysis tools allow you to analyze review sites, forums, and online communities.

#4 Track user sentiment over time

Human emotions are volatile. A person might like something one moment and hate it the next moment.

It’s the same with customer opinions and sentiments.

They are constantly changing and won’t be the same forever. So making plans that ensure that your brand values and messages don’t offend anybody just once is not going to work.

You need to tweak it over time.

Customer sentiment analysis can be immensely valuable when it comes to tracking shifts in consumer behavior. You can keep a watchful eye on customer reactions and adjust your business strategies.

AI-assisted trackers can further monitor sentiments in real time, letting you customize your marketing campaigns and products accordingly.

How to collect data to measure customer sentiment?

You can collect data via surveys, NPS ratings, social media monitoring tools, reviews, support chats, and Voice of the Customer (VoC) programs.

This qualitative approach will assist you in making data-driven decisions to improve your products, marketing, and customer services.

1. Surveys

Consumer surveys are the best way to gather accurate customer data for sentiment analysis.

Create surveys with questions designed to explain user emotions at each stage of the customer journey. They can be distributed to prospects via email, social media, or hosted directly on your website.

You can use online survey tools like SurveyMonkey, Zoho, or Google Forms for more efficiency.

2. NPS ratings

NPS stands for the Net Promoter Score. It is a marketing metric that measures the likelihood of your customers suggesting your product or services to others.

Brands usually ask this question: “"On a scale of 0 to 10, how likely are you to recommend [product, service, or company] to a friend or family member?”

NPS ratings help you distinguish between unhappy customers, indifferent customers, and highly enthusiastic buyers who would readily refer you to others.

3. Social media monitoring

Nowadays, it has become a necessity to keep track of the social media sentiments in relation to your brand.

You have to process people’s reactions (likes and dislikes), comments, shares, reshares, and brand mentions to analyze customer sentiments and tone of voice.

Social media listening tools like Audiense and Meltwater permit you to monitor reactions on different social media platforms in real-time.

4. In-app and website reviews

Product and website reviews like the one given below offer a quick way to gather customer data and insights, for general as well as specific questions related to your products or services.

You can collect product reviews from sites like G2, Capterra, Amazon, or Tripadvisor.

Additionally, try and encourage users to leave feedback within your apps or websites. This will allow you to automatically gather the data you need with web scraping tools.

5. Customer support

Customer support interactions, like chat logs, support tickets, emails, and transcripts, are highly valuable sources of information.

Both the structured and unstructured data can be processed with the help of customer support software to group people based on the sentiments they express.

6. Voice of the customer (VoC) data

VoC programs collect information from surveys, reviews, and feedback forms to measure customer loyalty and get directions for improvements.

Customer sentiment analysis tools like Monkeylearn and Userpilot use NLP along with a set of other machine learning algorithms to understand the text-based data.

Analyzing customer feedback helps them to identify and group the sentiments related to your product or brand in a usable format.

Use cases of customer sentiment analysis

You have read about the different types of sentiment analysis models, their benefits, and the sources from which you can collect customer data.

Now it’s time to learn about its applications in the real world. Commonly speaking, customer sentiment analysis helps companies:

• build better products and services
• enhance marketing and communication
• improve customer service

Here’s a detailed explanation of how insights generated from sentiment analysis technologies can benefit you in each of these areas.

#1 Build better products and services

All products and services begin with a simple idea.

For instance, Steve Jobs founded Apple to change the way in which people looked at computers.

In an age where computers were too big and chunky for personal use, he wanted to make them small enough to be used at home. Jobs knew that his idea would lead to commercial success.

The same can be said about Airbnb.

Both brands gave buyers what they were looking for. By tapping into user needs and emotions, they ensured the success of their venture.

Sentiment analysis can be employed in all stages of product development, from ideation to commercialization. You can perform it on product feedback, ratings, reviews, and comments to:

• Anticipate industry trends and spot niche markets
• Prioritize product updates by analyzing common issues
• Facilitate pricing changes and product launches
• Prompt development teams to fix recurring bugs
• Discover areas for improvement, assess product performance, and boost adoption rates

#2 Enhance marketing and communication

You cannot design the perfect product and wait for buyers to come to you. You need to approach them with hyper-personalized marketing campaigns and messages.

Still, how do you know who they are? How do they feel? Or if they will respond to you at all?

Thorough market research allows you to answer the first question. In addition, customer satisfaction surveys give you an overview of your target audience.

But customer sentiment analysis in combination with marketing will enable you to scrutinize sentiments in response to your marketing campaigns.

Furthermore, you can:

• Segment customers on the basis of different sentiments
• Use emotion analysis to guide direct campaigns
• Create content that caters to specific user sentiments
• Focus on areas or topics that evoke positive reactions
• Customize tone, style, and language for different marketing channels (like email, social media, organic, paid, etc)

It will allow you to create a positive customer experience on all platforms and push prospects down the marketing funnel. You need not just stick to your own buyers, look at your competitors’ as well.

Their weaknesses and strengths will help you create informed marketing campaigns.

#3 Improve customer service

Did you know that a good customer service experience increases the chances of repurchase by 82%?

It makes sense since a buyer will always gravitate towards a brand that offers a positive customer experience. Even if a product has some defects, on-time service can tide things over.

It is essential if you want to prevent churn rates and boost profitability.

That said, sentiment analysis can help your customer support teams in the following ways:

• Spot the tone and language that people find off-putting when communicating with support staff
• Optimize team trainings with analysis insights
• Automate repetitive tasks and reduce long response times
• Resolve customer support tickets faster, decrease backlog
• Alert team members when customers are distressed
• Enhance performance, reduce stress and boost employee retention

Customer support is one of the toughest jobs out there. You have to listen to millions of customers on a daily basis without losing your patience.

There’s not much you can do.

Nonetheless, analyzing sentiments can help you respond to customer queries more efficiently. Spot the most common problems and moods and train yourself to tackle them better.

Additionally, you can monitor online conversations using customer service teams especially trained to handle social media.

Wrapping up

Customer sentiment analysis has its own pros and cons. While it can help process a substantial amount of data, it might struggle to deal with complex language like sarcasm.

A simple statement like, "That's not so bad" can be challenging to interpret, since it can be positive or negative depending on context.

Even so, sentiment analysis tools can be powerful agents when it comes to capturing the subtleties of consumer expression. You can actively use the insights generated to further your business aspirations.