How Sentiment Analysis Saves Your Customer Support Team Time & Effort

How Sentiment Analysis Saves Your Customer Support Team Time & Effort

In today’s competitive landscape, customer service has become the key differentiator between brands. When users connect to a business online, they expect the brand to be easy to reach out to for a personalized experience. Unfortunately, this expectation can be tough to meet. With so many consumers to help and respond to when issues arise, small customer support teams can easily become overwhelmed thanks to sentiment analysis.

Thankfully, improvements in artificial intelligence can be of help. With sophisticated sentiment analysis, customer support teams can lighten their workload by categorizing tickets and respond to them much more quickly. 

By granting support bots emotional intelligence, they can further save time by letting the bot deal with simple tasks and escalate others. Click To Tweet

What is Sentiment Analysis, and How Does it Work?

Simply put, sentiment analysis is detecting the feeling or sentiment behind a user message (positive, negative, neutral) and how strong that sentiment is. The practice is also known as opinion mining. This kind of text analysis enhances social listening. It helps brands measure overall public opinion. Also brands can segment results to better understand key demographics’ needs. Sentiment analysis is not new, but it’s vastly superior now with the help of machine learning and natural language processing.

Before the use of machine learning, lexicographic text analysis was typical. By this method, teams of humans would tag and rank words based on their emotional meaning. Then develop an algorithm to apply a score to statements containing those words. The problem with this method is that it doesn’t detect the nuances of language (or sarcasm). An f-bomb, for example, can be just as positive as it is negative. “F— yeah!” is generally exclaimed by someone who’s enthusiastic and satisfied with a situation. With an abstract understanding of language trained by a team of humans, natural language processing applications can detect such nuances.

Using Artificial Intelligence-Based Sentiment Analysis for Product Iteration

With the help of artificial intelligence and text analysis, brands can track and measure customer responses in real time or over a given period. Click To Tweet

This is most important in product design (whether that be assessing new product features or even a political platform) and public relations.

Let’s say you’re a politician giving a speech. Or perhaps you’re giving a keynote presentation about your company’s newest product. With AI based sentiment analysis, you can track the audience’s response in real time. This helps you identify trends (what points are people reacting to the most?) and segment the results. (Which messages react to content of the presentation? Which messages are focused on the presenter’s body language and overall presentation style?). These responses can help you revise a design or platform to better meet audience expectations. By better accommodating to their needs now, you prevent complaints and frustrations later.

How Sentiment Analysis and Machine Learning Ease Customer Support Workload

Good customer service is essential to the health of a business. Especially in today’s social-centric, competitive world. Small businesses, though, may have trouble providing swift customer service. Sentiment analysis powered by AI and machine learning can help businesses and brands that struggle under ever-mounting customer support tickets.

Online British supermarket Ocado became overwhelmed with a high volume of customer support tickets. After that the company decided to invest in sentiment analysis to organize tickets and respond to them more quickly. The company used a dataset built from thousands of customer support tickets. The retailer trained a machine learning algorithm to tag tickets as positive, negative and neutral to help the customer support team decide how to prioritize them.

During peak times like holidays, the team is now better suited to help customers who need it most. In the future, Ocado may extend their technology to organize social media comments. This could further help identify common issues and keep customers happy, especially since most dissatisfied customers don’t reach out.

Results from sentiment analysis and natural language processing can help you prep your support representatives in how to respond to a growing trend in complaints about a certain topic or feature. They also help customer support teams respond faster to high priority tickets. This also helps identify a scandal early on. When your support knows exactly what issues to expect, they’re better equipped to handle it quickly.

Text analysis company Aylien used sentiment analysis over a week-long period to measure how well Ryanair airline responded to a PR disaster. Aylien measured the amount of positive-versus-negative engagement on social media (and segmenting the content of those posts) over time. They determined  that Ryanair made the right decision in delivering their statement just as complaints began to rise. Likewise, any business can use this analysis to learn from past mistakes and figure out how to deal with dissatisfied customers better in the future.

Working to Improve Customer Support with Artificial Intelligence

Finally, text analysis powered by artificial intelligence can measure effectiveness of customer support representatives. Is the user frequently using anger-charged statements when talking to the customer representative? Which responses, specifically, seem to cause annoyance or anger? By making these discoveries, you can better train a team on how to prioritize and respond to tickets based on opinion and attitude.

You can do the same for iterating a customer support chatbot. By providing a self service chatbot, you further ease the strain of problems your team will need to deal with. Without the need to devote time to simple issues, your team can solve complex problems much faster. With a chatbot that has the ability to determine sentiment and escalate high priority issues, your overall customer support system becomes better optimized. Click To Tweet