Customer experience has become a new battlefield. Data shows that 89% of businesses compete primarily based on customer experience. People buy products because they receive a great experience, and they stop purchasing after getting a bad one. To gain and retain customers, brands have to do more than just hone their products to perfection. They must also offer a memorable experience.
But conquering customers’ hearts requires precise knowledge. Companies must learn about customers’ needs, desires, and feelings in order to address them. Quantitative feedback like net promoter score, online surveys, or ratings can give good insights into your overall performance. However, they won’t give you a detailed picture like the analysis of customer sentiments will.
What is sentiment analysis?
Sentiment analysis, also called opinion mining or emotion analysis, is a branch of computer science. It uses machine learning and natural language processing to measure the sentiment and tone of the text, spoken language, images, and even emojis. It’s a useful tool that helps businesses to look far beyond the number of likes or shares. Through qualitative feedback, sentiment analysis helps to find out whether consumer attitudes towards products, services, and brand activities are positive, negative, or neutral.
Why is sentiment analysis important in business?
Gathering customer opinions has never been easier. Live chat, chatbot conversations, online surveys, social media posts, forums, and blogs are all loaded with useful customer feedback.
The problem is that being able to collect and analyze all of the user-generated content that appears online borders on the impossible. Interpreting that feedback well is also another thing. Customers express feelings differently. Many times, they apply both negative and positive statements in one comment and use sarcasm.
The dinner was great, the waiter was terrible.
Great service, I was waiting 2 hours for the receipt!
To fully understand customers and the issues they have, you need to meticulously analyze and categorize huge amounts of feedback. That’s where sentiment analysis tools come in handy. They help to automate collecting data and analyze it at scale.
Sentiment analysis tools such as Talkwaker can detect sarcasm, too. Because of that, they help to determine what customers say and what they actually mean. Those insights help to fix issues and improve business processes, which could translate into a better customer experience.
Popular sentiment analysis use cases
Social networks and the popularization of smartphones have opened a new dimension in terms of sharing and consuming data. Consumers write whatever they want online, and their voices are heard globally. That's why monitoring customer sentiment matters more than ever. Positive customer reviews build trust and boost conversion. Negative ones may ruin your reputation for months.
More importantly, data shows that 95% of customers read online reviews before making a purchase. Consequently, you must do your best to get as many positive opinions as possible. Of course, that’s not always possible. There’s always someone who won’t like your products or service, and they’ll be more than happy to share that online. When they do, you should know about it.
Sentiment analysis tools like Sotrender or Brand24 can help you keep your ear to the ground and monitor customer sentiment around the clock. So, if anything harmful to your brand appears online, you’ll be informed about it. With this functionality, you can handle issues immediately by countering fake news and issuing apologies to angry customers. By getting ahead of it, you’ll prevent unfavorable content from going viral.
Since sentiment analysis lets you monitor customers’ emotions in real-time and across channels, it’s a perfect tool to keep an eye on the health of your services. It’ll allow you to learn about the weak points in your customer support and help troubled customers quickly.
Businesses also apply sentiment analysis to discover what customers feel when they contact their customer support teams. This helps to find out which agents succeed in delivering efficient service and which need to improve their performance to boost customer satisfaction. These insights help to determine what makes customers satisfied and to better train new customer service representatives.
On top of that, sentiment analysis helps companies that deliver support using call center software or chatbots to prioritize customer issues. Virtual agents can transfer unhappy customers to human specialists so that agents can take care of customers’ problems before they escalate. The same applies to help desk systems. Sentiment analysis algorithms can detect concern and anger in emails and mark them as cases with the highest priority. This helps customer service agents, who manage a massive number of requests, decide which issues need immediate attention.
Sentiment analysis provides marketing intel and allows you to measure the performance of your promotional campaigns and events. Using it, you can find out whether your marketing activities generated any reactions online and then check their sentiment. This helps to rethink your strategies, if needed, and better personalize your messages in the future.
A good example of using sentiment analysis in marketing is the case of Expedia Canada, a travel company, that launched its “escape winter” campaign in 2014. The campaign created quite a stir online. However, when the company took a closer look at the sentiments of the comments, it turned out that many of them were negative. The brand found out that people hated the violin music used in their commercial. Because they were monitoring social sentiment and not just the number of mentions or shares, the brand corrected their course quickly.
Soon after that commercial aired, the brand released a spin-off video that shows a character from the first commercial breaking the violin. They hit the jackpot with that approach. The company showed that they have a sense of humor and listen to their customers, and that helped to improve the social sentiment.
Sentiment analysis can help you to boost your online presence, too. If you pick up on positive comments quickly, you can immediately respond to them and fuel the discussion with customers who have just become your brand’s advocates. This also helps to build better relationships and lets you find good examples for case studies.
Customers’ opinions about products are influenced because of many different factors. They also change on the fly like feature improvements, price increases, and marketing campaigns. All of these factors stir all sorts of emotions up with users.
Product managers need to constantly monitor how users respond to products and the changes they undergo. Sentiment analysis is useful in this area, too.
Sentiment analysis tools such as HubSpot’s Service Hub let you transform lots of qualitative data into quantitative insights on products and features. This helps to find out more about how customers perceive your solutions, how they perform on the market, and what should be done to improve their usage and reception.
Sentiment analysis isn’t the most important tool for market research. However, it can consolidate data and help overcome the deficiencies of typical research methods, such as surveys or focus groups.
By applying sentiment analysis to research techniques, brands can systematically collect data from a large number of customers across multiple communication platforms. People share tons of experiences online every day and hour. There are more than 330 million active users on Twitter alone, and they send over 500 million tweets each day. They post about various things, including their experiences with brands. Having all this data analyzed and categorized lets marketers get a broader picture of the market and their competitors. This, in turn, helps to better understand customers' desires and create unique value propositions.
Stock market prediction
Stock prices are difficult to predict. They change out of the blue and depend on multiple economic and political factors. Curiously, the data shows that there is also a strong correlation between stock prices and social media sentiments. Articles, news, reviews, even a single tweet can influence brand reputation and impact stock performance.
In 2018, when an American celebrity, Kylie Jenner, tweeted her displeasure with Snapchat, the company’s stock prices went down by 7%, losing over $1.3 billion of market value.
The same year, one of Elon Musk’s tweets affected the stock prices of Tesla.
The relationship between social sentiment and the stock market encourages investors to make use of sentiment analysis and AI algorithms when considering investing decisions. By using them investors can discover the public viewpoint of particular brands and get hints on stock trends and movements.
The challenges of sentiment analysis
Sentiment analysis is being applied to countless numbers of business cases where it has proven to be useful. Still, sentiment analysis models pose quite a challenge for researchers. The range of vocabulary we use is evolving constantly. New words appear every day and existing ones get new meanings. Researchers have to constantly train sentiment analysis tools to deliver more accurate results by detecting sarcasm or word ambiguity. Only by constantly perfecting sentiment analysis will it make a difference in helping brands deliver better products and successful customer experiences.