Sentiment analysis can be a key indicator of customer satisfaction and customer experience. Customer experience is the key to business success. In fact, after a positive experience, more than 85% of customers purchase more. Twenty-five percent, however, drop a product or brand after just one bad experience. That proves that poor customer experiences can be costly.
As a business owner, rather than assuming you know how your customers experience your business, is there a way to really understand what makes for great customer experiences? If there were a way to analyze customer feedback, could it be used to retain more of your customers? (I hope you know where we’re going with this.)
The answer lies in analyzing review sentiment! Gathering customer feedback in the form of online reviews is pointless if the business isn’t using that customer feedback to strategically promote change. Keep reading as we reveal how sentiment analysis is more than a buzzword and how businesses can benefit.
What is sentiment analysis?
Sentiment analysis is an automatic process that uses artificial intelligence to assign a value from +1 (extremely positive) to -1 (extremely negative) to bits of text. Sentiment analysis inspects the text and identifies emotional opinion within it to determine if the writer’s opinion is positive, neutral, or negative. The greatest benefit is its ability to identify the words used to describe the business, such as ‘smelly rooms’ or ‘rude staff’. Sentiment analysis can tell that the phrase, “The burgers were burnt” is a negative sentiment.
Some statements sentiment analysis can identify could be:
- Recognizing the positive word in, “I enjoy the atmosphere.”
- Noting the difference between, “this place is great” and “this place is not great.”
- Identifying modifying words in “The hotel rooms are too small.”
Why star ratings are not enough
But can’t star ratings tell you enough about the feelings of a reviewer? Not always! Stars matter, but content matters more. Star ratings paint reviews with too broad a brush, missing a lot of details of how the customer really feels. Having a high star rating looks impressive and you’d assume the content to follow is positive, but that’s not always the case. That’s where the value of sentiment analysis comes in. For example, a five-star review might mention aspects the reviewer didn’t like, despite liking the experience as a whole.
Through sentiment analysis, business owners can capture the emotion behind a star rating and begin to understand what attributes contributed to or detracted from a positive experience at the business. For example, a review can still be rated five stars and mention that the burgers were burnt.
How can sentiment analysis be used?
Does every business owner spend hoards of time sifting through their customer reviews? Of course not! Sentiment analysis technology can do it for them. Think of it like a miner working around the clock, digging for those little nuggets of consumer-feedback-gold.
Sentiment analysis can also track recurring themes in feedback, like ‘wait time was too long’ or ‘not enough towels in the rooms’. Finding these themes is like hitting a goldmine of actionable insight.
Below are some use cases for review sentiment analysis that businesses can value from:
Targeting customers to better their experience
Sentiment analysis allows you to understand the nuances in customer reviews and pinpoint where an issue (or positive experience) stems from. Rather than responding the same way to every negative review, sentiment analysis provides insights that enable you to craft a better response. You’ll also be able to surface issues to the right department who can benefit from the feedback to make improvements.
Companies can also use sentiment analysis to put certain reviews at the top of the priority list. When sentiment analysis is used strategically, companies are better equipped to address negative feedback quickly and in a way that affects change. For example, if the most negative sentiment is from people who don’t like your delivery process, you can use that to make an operational change.
Discover trends over time
If your current review management tactic reveals that people are happy with your business, that’s great! But, that can lead to complacency. Sentiment analysis helps to combat complacency by showing how customer sentiment trends over time. For example, you can see when customer sentiment increases and attribute it to a change made at the business, such as a new menu item or change of management.
Sentiment analysis can also reveal how seasonality affects your online reviews and use that to your advantage. For example, if ‘staff’ peaks in negativity in the summer months due to slow service, you might consider hiring more help.
Recognize differences in market segments
And lastly, if you manage a brand with many locations, sentiment analysis is nearly impossible without a tool. Reading through hundreds of reviews for important keywords is a waste of time and money.
Using a sentiment analysis tool, however, brand managers can see which locations are viewed more positively than others. This can lead to a greater understanding of the market segments each location serves and the operational processes at each location. For example, if the location in Phoenix rates very negatively for ‘return policy’ and the location in Miami is very positive, the manager can see how returns are handled at the Miami location and implement a change to the location that is lacking.
Try it for yourself
Duly’s Reputation Management product now features powerful sentiment analysis functionality, called Insights.
Using natural language processing, Insights uses AI to identify and extract keywords and phrases from customer reviews to show what drives the online conversation. The result is a collective understanding of whether a business is succeeding or failing in the eyes of its customers. Use the information to uncover customer trends, understand how customers in certain geographic areas feel about specific business locations, and determine how operational adjustments affect the brand over time.