The advancement of technologies like machine learning and natural language processing has created entirely new tools for customer service, marketing, and business intelligence.
One of these tools, used to improve the customer experience, is customer sentiment analysis, also known as opinion mining. It is an application of natural language processing that uses software to decode the sentiments conveyed in a text. This helps companies determine the social aspects of customer interactions with their brand.
Sentiment analysis gives companies an idea of what the customers’ sentiments or feelings are toward them by analyzing the tone of the language customers use when interacting with them. Customer sentiment analysis pulls data from many places, including web form queries, social media platforms, customer feedback surveys and portals, review site posts, interactions with chatbots, and more.
Using sentiment analysis tools, companies can quickly get a sense of how people are reacting to new products, marketing campaigns, and more. Through machine learning technology, a sentiment analysis tool scans the language used by customers and ranks their general emotions as positive, neutral, negative, and so on.
If most comments on a social media post come back with a sentiment score of angry or unhappy, the company immediately knows corrective action may be needed, even without waiting for a quantitative spreadsheet breaking down product returns.
Customer sentiment analysis is still a relatively new subfield of analytics, but there’s plenty to learn. As always, the CX Lead has everything you need to know to keep you up to date on the latest in customer sentiment analysis.
How does customer sentiment analysis work?
Now that you know what customer sentiment analysis is, let’s look at a simple example of how customer sentiment analysis works.
The word cool literally means low temperature. But when used in customer feedback or by a customer to describe a product, cool is likely a positive statement indicating excitement or customer satisfaction. A sentiment analysis tool using machine learning would rank cool as positive, and the organization would understand the customer’s sentimenttoward its product.
The machine learning models of customer sentiment analysis learn to understand natural language and what sentiments are conveyed in customer feedback aside from their literal meaning. Sentiments are then grouped into categories.
This use of sentiment classification gives organizations an understanding of whether customers are expressing positive, negative, or neutral feedback. Put simply, a program is taught that happy is a positive expression. When customers leave a review saying that they are happy with a product, the customer sentiment analysis tool ranks the statement as positive.
Customer sentiment analysis tools also use text analysis to assist in identifying when sentiment is being expressed. For example, adverbs often relate to emotions, whereas proper nouns relate to people or places. So the tool may place more emphasis on what is conveyed using adverbs when identifying overall sentiment.
Of course, just as human language is incredibly complex, so, too, is the technology that powers sentiment analysis. Most tools use advanced algorithms and deep learning to classify text based on features such as intent or context, in addition to sentiment classification.
All this information can give companies a more efficient way of understanding customers.
Contextual semantic search, for example, can classify a bulk number of tweets complaining about a price hike as relevant to price, even if the word price is never explicitly mentioned. So it can accurately group a tweet such as, “Can’t believe how much this rideshare charged for just one block! I’ll be broke for months!”
If programmed correctly, the contextual semantic search would have learned that broke is a colloquial term related to price. With customer sentiment analysis, customer opinion becomes usable data that inform companies’ strategies.
Data used for customer sentiment analysis
As a general rule, any written or verbal customer feedback can be analyzed for customer sentiment. That includes comments on social media, in written reviews on either the business’s product page or industry blogs, on surveys, or even in support ticket chats since they are likely to indicate a level of customer satisfaction. You can even buy customer sentiment analysis data sets online.
Companies can proactively collect such data by sending out customer satisfaction surveys or asking for ratings that have the addition of open-ended questions.
Sentiment analysis use cases and applications
Sentiment analysis can be used for much more than passively collecting data about customer sentiment, and it would be a mistake to think of it as just another big data tool. The machine learning many sentiment analysis tools use enables users to gather qualitative data that can, in turn, be used as a powerful tool for increasing brand awareness, enhancing reputation, and improving the customer experience.
Using customer sentiment analysis to improve customer service
One application for sentiment analysis is in assisting with customers and customer service. When a customer’s comment is flagged as negative, automation can be set up to efficiently address the issue. For example, the comment might be put in the chat queue of a customer care representative who specializes in soothing irate customers.
Moreover, if a person leaves a scathing review on social media, the natural language processing capability of a robust sentiment analysis tool can use to alert the social media team. They can then reach out directly to the customer on the platform’s messaging app immediately, adding a layer of expediency and personalization to customer care. This type of social media sentiment analysis gives particularly unique insight since people tend to treat conversations on messaging apps as far more casual and direct.
Sentiment analysis can also be used to quickly aggregate a demographic’s general sentiment, even if the comments or survey results are using casual or natural language. This type of text mining makes companies nimbler in their ability to respond to customer needs.
One of the main advantages of using tools that leverage artificial intelligence, such as sentiment analysis software, is that the more data it has to work with, the better and more accurate it becomes over time.
Using sentiment analysis to increase brand awareness and communication
The emotions people have about a business’s product or service and the language they use to express those feelings can have a huge impact on that business. People tend to listen to their friends and family when it comes to recommendations.
When a company launches a new marketing campaign, the company can use customer sentiment analysis to immediately gauge the target audience’s reactions to the campaign and to make tweaks to it or to rethink its strategy based on the analysis. The ability to identify common sentiments about a product or service can be the seed for an effective marketing campaign that responds to customer sentiment in real-time.
Sentiment analysis can also help better target content creation and communication with customers through emotion detection, which identifies a customer’s feelings about a product or situation. Blogs, calls to action, and other nudging techniques can be targeted based on what you know about your demographic’s state of mind. Knowing the emotion your communication evokes in customers can guide you in the best way to reach out, whether you’re reaching out via social media, a website landing page, a survey, or an email blast. It can also help narrow down what tone to use and where to use it.
People may respond more positively to a casual tone on social media and in a newsletter but may prefer the more formal language in a customer service setting. Customer sentiment analysis can tell you how to best address people engaging with your brand.
Benefits of customer sentiment analysis
Although customer sentiment analysis can benefit nearly any organization if used correctly, it’s currently best suited for those that offer services or products.
This is largely due to the practical mechanics of how customer sentiment analysis works. These are brands that can accrue a large amount of customer feedback, especially on social media. Though it’s true most people only take to Twitter when something has gone wrong, conversations that occur on social media platforms or review sites can offer a wealth of customer sentiment. Tracking mentions of certain words and phrases, or even complex queries across social media platforms and other web locations, is called social listening. It applies customer sentiment analysis tools to track sentiment for products and services. For example, using a method called brand mention, social listening will track anytime your brand name is mentioned and use sentiment analysis to identify trends in customer sentiment.
Two benefits of social listening are reputation management and competitor analysis. Social listening will alert you to how your brand is being discussed across the web so that you can identify and react in real-time to the health of your product’s or service’s reputation. You can also track the customer sentiment of competitors, which allows you to learn from their interactions as well.
Social listening helps make the sentiment analysis that much more robust as the machine learning model underpinning it increases its deep learning capability.
Refining marketing strategy using customer sentiment analysis
How people react emotionally to a marketing campaign directly correlates to its success or failure. It increases understanding of customer satisfaction, both what leads to it and what does not.
If a campaign engenders a negative sentiment, for example, it will be harmful to brand reputation, as well as the sales or growth of the product or service. This is where the advanced sophistication of a sentiment analysis tool is especially useful. These tools go beyond identifying that an ad is not performing well. They can identify specific sentiments toward a campaign and are often able to recognize which aspects of a campaign are evoking those sentiments. For example, a sentiment analysis tool may be able to identify if certain sentiments are targeted at a product feature or the price, among other things.
Emotional reaction to the outreach can also guide direct campaigns. Sentiment analysis can tell a company that younger people hate email and that an older demographic dislikes social media. The company can then respond by setting up an email contact for older customers and a chat option for the younger demographic.
Tools to perform sentiment analysis
There are many tools available to perform customer sentiment analysis, but they are not all alike.
Some are already integrated into social media management software. Others are voice-based and can be used to detect tone in a call center or customer service phone line setting. Still, others specifically function as sentiment analysis software that can be deployed or integrated into multiple environments.
What to look for in sentiment analysis tools
Sifting through all the available options for sentiment analysis tools can become overwhelming. There are some features you definitely want to keep in mind.
If you’re especially interested in social listening, you should ensure easy integration with social media platforms and look for reports that give insight into social media sentiment analysis.
Your company’s capabilities are important. While some tools are plug and play, others require significant coding knowledge.
Find the right sentiment analysis tool by being very clear on what data you are trying to accrue and have a sense of how you want to use it.