Customer Experience

Get More Out of Customer Feedback Using Text Analytics and Sentiment Analysis

June 15, 2016

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How deep do you dive into your online reviews and customer feedback?

Are you just looking at your star ratings and most helpful reviews, or are you looking beyond the surface level and diving into the entire anatomy of feedback?

Whether you’re running a small- or medium-sized business or managing an enterprise-level organization with hundreds or thousands of locations, it’s likely that customers are sharing useful information for your business.

Text analytics can allow you to find the signal in all that noise and summarize trends across millions of customer feedback data points.

Making Sense of Massive Amounts of Feedback

They aren’t simply rating their experiences with 1 star or 5 stars. They’re also expressing their thoughts, feelings, expectations in free-form text. They’re trying to let you know that the morning shift waitress needs more training, that Bank Teller 3 is the best and most helpful of the bunch, that the breakfast buffet is really good, even if a bit expensive. They’re posting comments and generating conversations on sites like Yelp and TripAdvisor; on social media, via status updates, check-ins, and tweets; and on other feedback channels, where customers are making their voices heard and expecting your company to respond.

This can be challenging to handle, especially if your company is getting a lot of feedback. When you have tens or even hundreds of thousands of comments and reviews to read and manage, how do you even begin to make sense of it all?

This is where text analytics and sentiment analysis come in.

  • Text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure textual information — in this case, text from reviews and feedback — for business intelligence, research, or investigation.
  • Sentiment analysis is usually part of a larger text analytics effort, and involves analyzing subjective material and extracting various forms of attitudinal information, such as sentiment, opinion, mood, and emotion.

An increasing number of data-driven companies are embracing text analytics and sentiment analysis techniques in order to get more out of customer feedback, as well as to find more effective ways of measuring and improving the customer experience.

Reveal Hidden Trends and Patterns

According to IBM, 80 percent of all data today is unstructured.

This includes data from online reviews, social media posts, enterprise system data, “additional comments” in surveys, E-mails, recorded phone calls, YouTube video reviews and comments, what-have-you.

Unstructured data is typically difficult to analyze, for a number of reasons: sheer quantity, human error (grammatical mistakes and misspellings), presence of multiple unrelated ideas, unclear sentiment.

Using text analytics, however, you can bring customer feedback to life by finding hidden trends and patterns from unstructured data and textual information.

A quick glance at your collection of 500 Yelp reviews, for example, can tell you that customers have been rating your business with an average of 4.5 to 5 stars. Without text analytics, however, it may remain undiscovered by your business that 50 percent of customers — regardless of the rating score they gave — raised the issue of “long wait times” or “impatient staff.”

Trending keywords

By empowering companies to dive into the entire anatomy of feedback, text analytics can facilitate the discovery of customer trends and patterns that are not readily accessible via traditional or simplistic approaches in online review monitoring and feedback management.

You can also do some nifty things with all this data (remember it’s 80 percent of the data that’s out there). For example, at ReviewTrackers, we looked at 130,000 baseball stadiums reviews and compared the way fans talk about hotdogs at each stadium. The result: a new way to rank which baseball stadium has the best hotdogs.

If this sort of thing whets your appetite, read our Getting Started guide to Text Analytics.

Understand Customer Sentiment

Another challenge that a typical modern brand or company faces is how to gain a better understanding of the voice of the customer. What do they think, how do they feel, what are their needs and expectations, and how can you respond to these needs and expectations?

With sentiment analysis, your company can extract additional information from free-form customer feedback, including sentiment data and emotion (which aren’t always easy to pin down). It also eliminates blind spots and biases that may not be readily apparent when simply collecting data.

For example: a TripAdvisor user may describe the bed in a hotel as being “soft”, but a range of adverbs and modifiers can instantly change the entire sentiment of the user’s review. A bed described as “incredibly soft” will obviously not be a cause for concern for a hospitality manager, but a bed that’s “too soft” will.

Understanding customer sentiment allows your organization to:

  • Look beyond the surface level of ratings and feedback data and achieve a more accurate, complete, and unified view of the customer;
  • Respond to reviews more strategically and effectively;
  • Identify mixed-sentiment reviews and feedback that require management response;
  • Understand topics and issues that customers mention organically, without prompting;
  • Discover new ways to increase customer lifetime value;
  • Make timely and correct adjustments to the customer experience, and accelerate operational improvements and breakthroughs for your company.

Develop a Brand that Resonates With Your Audience

Analyzing unstructured data from reviews and feedback can also be useful in driving the engagement levels that people have with your brand.

Specifically, insights from text analytics and sentiment analysis initiatives can help tailor your messaging and communications so that the voice of your brand speaks the language of your customers.

Here’s a great example from Search Engine Journal: in the past, fashion brands insisted on calling hoodies “hooded sweatshirts.” An analysis of reviews and customer feedback left on Amazon, however, showed that shoppers preferred to type “hoodies” — either when they were writing a review of an item they had already bought or just searching online for a product in that category.

It made sense that retailers switched to “hoodies” in their product descriptions and catalogs. Not only did this allow for greater brand resonance among targeted audiences; the switch also fostered improvements in search relevance and ranking.



Online reviews and feedback offer more value and information than just star ratings and satisfaction scores. By using text analytics and sentiment analysis, your company can make the most out of customer feedback and gain actionable insights that are critical to marketing, sales, operations, and resource allocation.  

At ReviewTrackers, we are helping companies unlock actionable customer intelligence from online reviews and feedback with a new feature called Trending Topics.

Powered by advanced natural language processing and machine learning technology, Trending Topics facilitates the discovery of high-impact customer trends and brings multi-layered insights to our leading customer feedback platform. As a result, companies can easily identify underlying opportunities and issues with the customer experience — and make better business decisions backed by data.

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