Breakthrough technology like machine learning and natural language processing has created some seriously impressive 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. Essentially, it’s a type of data science that uses software to decode the sentiments conveyed in a text—whether it’s positive, negative, neutral, or a potential source of new insights. The data you’ll gain from this kind of analysis can help inform so many aspects of business and marketing, so it’s definitely a tool worth exploring.
The basic function of customer sentiment analysis is assessing the tone of language used by customers and attributing a sentiment score, or polarity, to the data. You can pull data from loads of places:
- Web form queries
- Social media platforms
- Survey responses
- Customer feedback surveys and portals
- Customer reviews
- News articles
- Interactions with chatbots
- and more!
Customer sentiment analysis is still a relatively new subfield of analytics, but there’s plenty to learn. Let's dive in.
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. In brief, the technology uses a categorizing system attributed to different words in a given text to get a read on the overall sentiment of customer feedback.
For example, let’s say the sentiment analysis system is programmed to recognize that happy is a positive word. When customers leave a review saying that they are happy with a product, the customer sentiment analysis tool ranks the statement using a polarity rating, which is represented as a numerical value between -1 (very negative sentiment), 0 (neutral sentiment), and +1 (very positive sentiment). This numerical value is also called a sentiment score. Since it’s a tool that leverages artificial intelligence, sentiment analysis software becomes better and more accurate over time as it gains more data.
If most comments on a social media post or product review come back with negative words, like angry or unhappy, the company can take corrective action ASAP instead of waiting around for a quantitative spreadsheet breaking down product returns.
Customer sentiment analysis tools also use text analysis to identify when sentiment is being expressed. For example, adverbs often relate to emotions, whereas proper nouns relate to people or places.
Natural Language Processing (NLP)
Now, here’s where it gets more complex—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. The machine learning models of customer sentiment analysis function to understand natural language—that is, the real emotion and intent behind customer feedback aside from the literal meaning. A sentiment analysis tool using machine learning would rank cool as positive.
Human language is pretty complicated, so it’s no surprise that the technology we use to analyze it is as well. Most tools use advanced machine learning algorithms and deep learning to classify text based on additional features like intent or context, not just sentiment. For companies looking to better understand their customers, all this data is worth its weight in gold. 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 mentioned in plain English. So, say someone posts a status update that says, “Can’t believe how much this rideshare charged for just one block! I’ll be broke for months!” Using NLP, your customer sentiment analysis tools would have learned that broke is a colloquial term in the common lexicon that is related to price, so it will still recognize the relevant data.
Data used for customer sentiment analysis
As a general rule, you can use any written or verbal customer feedback for sentiment analysis. That includes comments on social media, on forums, in online reviews on either the business’s product page or industry blogs, on surveys, or in support ticket chats. You can even buy customer sentiment analysis data sets online.
If you want to take the proactive approach, collect such data by sending out customer satisfaction surveys or asking for ratings that have the addition of open-ended questions.
Social Media Monitoring (or Social “Listening”)
Brands 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.
Social listening helps make the sentiment analysis that much more robust as the machine learning model underpinning it increases its deep learning capability.
Sentiment analysis use cases and applications
You can use sentiment analysis for much more than passively collecting data about customer sentiment—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 conjunction with quantitative metrics, be used as a powerful tool for countless areas of business and marketing.
Using customer sentiment analysis to improve customer service
One common application of sentiment analysis is assisting with customers and customer service. When a customer’s comment is flagged as negative, automations can be set up to efficiently address the issue. For example, the comment might be put in the chat queue of a customer support representative who specializes in soothing irate customers.
You can also use sentiment analysis to quickly aggregate the general sentiment of an entire demographic. This type of text mining makes companies nimbler in their ability to respond to customer needs.
If a person leaves a scathing review on social media, some sentiment analysis tools can use that to alert the social media team. From there, someone can then quickly reach out to the customer on the platform’s messaging app, adding a layer of expediency and personalization to customer care. This type of social media sentiment analysis brings plenty of fresh perspectives, since people are typically more casual and direct in conversations on messaging apps.
Using sentiment analysis to increase brand awareness and communication
The emotions people have about a 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.
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 and 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.
These tools 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 more formal language in a customer service setting. Customer sentiment analysis can tell you how to best address people engaging with your brand across all sorts of communication channels.
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.
When a company launches a new marketing campaign, the company can use customer sentiment analysis to perform market research and gauge the target audience’s reactions to the campaign. Then, they can tweak or rethink the marketing strategy based on their analysis. Identifying common sentiments about a product or service can be the seed for an effective marketing campaign that responds to customer sentiment in real time.
If a campaign evokes a negative sentiment, it will be harmful to both brand reputation and 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 recognize the particular aspects that evoke those sentiments.
Emotional reaction to 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.
Performing a competitor analysis
You aren’t just limited to studying your own customers’ sentiments—you can analyze the sentiments of your competitors, too! This can be incredibly useful for seeing how you shape up against the other heavy hitters in your industry. Who has the best reputation? Who’s got the most dissatisfied customers in their comment threads? There’s so much insight to gain from assessing other companies’ successes and flops!
Improving products or services
When people consistently post about common issues and negative themes regarding a specific product or service, you can assess the data to see what needs to change. Learning what your customers really want is so valuable—it can help you create an even better offering!
Longitudinal sentiment research
Like I mentioned before, AI gets smarter as it gains more information over time. One of the major benefits of continued, long-term data analysis is the ability to monitor changes over time. How does your brand reputation compare to its previous state five years ago? Analyze the trends to evaluate what changed and why.
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. Others specifically function as sentiment analysis platforms that can be deployed or integrated into multiple environments via API, as is the case with the Natural Language Toolkit (NLTK), a Python-based suite of tools for English natural language processing.
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.
The most popular tools for customer sentiment analysis
There’s no shortage of mind-blowing tools available for assessing the sentiments of your customers! Here are some noteworthy sentiment analysis tools available for businesses looking to take their customer experience strategies to the next level.
Interested in learning more about sentiment analysis? Here are some excellent follow-up reads to build on what you've learned:
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