Are you looking for ways to measure customer experience and see where improvements can be made?
You're not alone.
More and more businesses are taking interest in how customers feel about their brand, products, and services. However, measuring sentiment can be difficult—especially if you don't have the right knowledge and tools to measure and act on it.
In this article, you’ll learn what customer sentiment analysis is and why it’s important, as well as some of the primary uses of customer sentiment analysis today.
What is customer sentiment analysis?
Customer sentiment analysis is the process of measuring customer sentiment toward a brand. It can be performed on social media, online surveys, reviews, interviews, and more—anywhere your customer shares their thoughts on your brand.
The goal is to track the general feelings or emotions people experience when interacting with a brand, product, or advertisement.
Sentiment analysis gauges customer satisfaction. It goes beyond categorizing just negative or positive sentiment; sentiment analysis can also be used to identify exact feelings, such as sad, happy, excited, scared, and so on.
Customer sentiment analysis collects and analyzes feedback and opinions about a business from current and potential customers. This information lets businesses identify patterns in customer complaints or issues and improve their products and services to increase customer loyalty and satisfaction.
Types of sentiment analysis
Sentiment analysis varies depending on the context or data analyzed. Here are some of the most common types.
Sentiment analysis uses natural language processing (NLP) to automatically identify and extract sentiments from text to provide a richer, more contextual view of content. It’s the process of separating subjective, opinionated information from objective information.
Text analysis is one of the most popular ways to detect sentiment in customer reviews, comments, feedback, and more. Automated sentiment analysis uses Machine Learning (ML) and Artificial Intelligence (AI) to extract meaningful insights into the underlying sentiment of the text.
Categorizing customer sentiment semantically links similar sentiments and finds out what kind of emotion a particular group of words conveys. It goes a step further than detecting sentiment by analyzing text and predicting what sentiment is being expressed—happy, sad, worried, angry, and more.
Sentiment detection is widely used in Twitter sentiment analysis, customer service, marketing, news, and many other areas to uncover customers' emotions and thoughts about different facets of a business.
Clause-level analysis is key when you’ve got more than one sentiment in a piece of text. This type of sentiment analysis looks at the clauses—groups of words—within the text in question. It’s able to weigh up the sentiments to provide a clause-level sentiment score that takes both negative and positive statements into account.
For example, say you’re looking to analyze customer reviews left online. One reads:
‘The solution is effective but the support staff is rude.’
There’s both positive and negative sentiment here, and clause-level analysis weighs both up to provide an accurate prediction. With other types of sentiment analysis, these conflicting sentiments could cancel out and be classified as ‘neutral’, when that’s not the case.
Drawbacks of sentiment analysis
Although sentiment analysis is an effective tool for measuring customer sentiment, it comes with some limiting factors.
Needs a lot of data
In order to get accurate results with sentiment analysis, you need a lot of data. Gathering that much data can take a company years, and using ready-to-use datasets means training your model with generic data. You want the model to understand how your users sound, not how everyone else’s sound.
You’ll likely require extensive use of data analytics tools for collecting, labeling, and analyzing data. This increases the cost of implementing sentiment analysis. Additionally, the data must be labeled in an organized and structured manner to be analyzed efficiently. Labeling that much data and ensuring data privacy can be another headache.
Some tools—such as Levity—enable you to combine a ready-to-use dataset with your own data, allowing you to train AI models with a mix of business-specific and generic data. This cuts down the amount of time needed to collect training data whilst also ensuring the model is more accurate to your business needs.
It can struggle with complex language
Words and phrases can have different meanings depending on the context in which they’re used. This can make it difficult for AI to clearly identify the intended sentiment behind a message.
For example, words like "good" and "amazing" have positive connotations but can have different connotations depending on the context. An algorithm might recognize these words as positive, but the sentence using them might be negative.
Even if an algorithm is programmed to account for these differences in meaning, it will still have trouble discerning the true meaning. For example, AI might analyze the sentiment behind a phrase “an amazing failure” as positive based on the word “amazing” alone.
Why is customer sentiment analysis important?
With customer sentiment analysis, you can track and monitor how your customers feel about your product or service, as well as your brand in general. It’s useful for a number of different reasons.
Improve customer service
Analyzing customer sentiment is paramount to improving customer service. Knowing what your customers feel or say about your business is essential and enables you to identify areas to improve the customer experience.
For example, by analyzing social media conversations, customer service teams can identify and address problems before they become PR crises.
Suppose you launch a new product, but the early reviews about it are negative. With sentiment analysis, you can monitor the reaction to the new product on social media channels and address concerns quickly.
This will help you prevent the spread of negative reviews and improve the business areas customers feel negatively about.
Customize marketing strategies and campaigns
Sentiment analysis allows you to identify and target customers with highly positive or negative feelings about your brand or product. Extreme feedback can be useful for creating targeted marketing strategies and campaigns.
Sentiment analysis is also useful for market research and developing more personalized marketing strategies, including email marketing, social media advertising, and search engine marketing.
For example, if feedback on your new product or feature unveils that users love a specific aspect of it—let’s say the interface—you can focus marketing efforts on highlighting this.
Monitor and manage brand reputation
Positive sentiment toward your brand improves brand equity and increases brand value. It encourages customers to engage with your brand in a variety of ways, including re-purchasing, recommending it to others, and leaving positive reviews.
Online brand monitoring via sentiment analysis tracks and analyzes customer opinions about your business—both good and bad.
AI-powered sentiment analysis tools usually automate this process and work best for brand monitoring. They allow you to automatically capture brand mentions, practice social listening, and extract meaningful insights quite effectively.
You can monitor what people are talking about your products and services and business in general to offer them the right solutions.
Improve product and services
Customer sentiment analysis helps you collect customer feedback on products and services from ratings, reviews, comments, and posts on social media and forums to help make your offerings more appealing.
For example, an ecommerce company can analyze reviews to identify common issues with your product. This enables the product development team to prioritize certain aspects over others in order to deliver the most pressing updates first.
By collecting and analyzing customer feedback, you can find out what customers think of your product or service, what they want, or are unhappy with, and make necessary changes.