5 practical use cases of customer sentiment analysis for NPS

In our article about why customer sentiment analysis matters, we introduced you to Maya Angelou's famous quote: "People will forget what you said, people will forget what you did, but people will never forget how you made them feel.”

We even gave you PwC's findings that 32% of customers would stop doing business with a brand they love after just one bad experience. So, you are now probably wondering if sentiment analysis could work in your business. We'll talk about use cases here so you can better understand how you can take advantage of sentiment analysis, especially for your NPS.

Take note that while NPS (Net Promoter Score) measures how likely customers are to recommend a brand, sentiment analysis helps explain why. Simply tracking an NPS score without digging into customer emotions, frustrations, and expectations leaves businesses with unanswered questions.

So, let's start exploring five practical ways companies can use sentiment analysis to improve NPS, turn detractors into promoters, and create better customer experiences.

Isn’t NPS enough?

Raw metrics like NPS provide some level of awareness of customer sentiment. However, research suggests that NPS is too broad a brush, missing a lot of useful detail on how customers feel. Even before looking at the specific reasons a customer may be unhappy, a lot of detractors can still talk positively about elements of the service they receive. Promoters can detail elements they don’t like, despite liking the service as a whole.

By analyzing the sentiment more accurately, and in particular finding the things people are really unhappy about, you can focus more on what will make a difference. Someone who is a detractor but expresses positively is unlikely to tell the whole world about their issues. Someone who is vehement will.

Advanced algorithms that use machine learning can capture nuances, such as the fact that ‘bloody’ may actually be a positive indicator if used in the phrase ‘bloody excellent’

Using the same principle, both dictionary and machine learning based approaches can also determine more specific emotions such as frustration, anger, or joy.

Accuracy depends on how closely the test data set resembles the dataset used to develop the dictionary or the machine learning model. Both can perform poorly if the datasets have little in common.

Why is customer sentiment analysis different?

Applying sentiment analysis to what customers are saying is a little more difficult for two reasons: how much they say, and how they talk.

The first difference is easy to understand: reviews and NPS comments are normally short snippets of text. This means that each piece of text provides only a little information when deciding its classification. Most algorithms are designed to handle large pieces of text like news articles. They might not work on customer reviews.

The second is that how a customer writes a support ticket is different to how they will respond to a survey, or write a Facebook post to their friends. In the case of a support ticket, they will (hopefully) be more factual and a Facebook post will be informal and less specific. If the same analysis is used then the sentiment assigned to each will be less accurate. It is important to tailor the analysis to the medium.

Why aspects of customer sentiment matter

Customer sentiment isn’t often useful on its own because it does not describe why customers feel that way. What are customers specifically happy or unhappy about? In order to find out, businesses need to identify aspects that customers comment on. For example, in a hotel, the aspects may be ‘customer service’, ‘room size’, or ‘cleanliness’.

A deeper analysis can also find specific recurrent themes. For example, ‘not enough bread at breakfast’ or ‘room service is too slow’. These are important because they are easier to understand and act upon. Determining these themes is the holy grail of actionable insight. It will improve customer service effectively in a targeted manner.

The difficulty with both aspects and themes extraction lies in identifying the many different ways people may refer to the same thing. For example, when talking about ‘cleanliness of hotel rooms’ customers may be using words like ‘clean’, ‘tidy’, ‘dirty’, ‘dusty’ and phrases like ‘looked like a dump’, ‘could eat off the floor’. An effective customer sentiment analysis exercise should be capturing not just the sentiment, but also the aspects and the themes in each piece of customer feedback the business receives.

What are some use cases of customer sentiment analysis?

So you are interested in collecting sentiment what can you do with it? Here are 5 use cases that will enhance your customer experience strategy.

1. Target individuals to improve their service

By capturing customers who feel strongly negative towards your product or service, customer service can deal with their issues specifically. Imagine the fury of a customer who leaves a comment that’s -0.95 negative. Whether it is through personal contact or through prioritizing their tickets, action can help defuse the situation. If Dick’s Sporting Goods can identify those with real problems in the returns process, they can make sure customer service prioritizes them.

2. Track customer sentiment over time

Tracking customer sentiment attached to specific aspects of the business is more effective than tracking just the NPS. Analysis can explain why the NPS score has changed; or if the score hasn’t changed, what may have changed in the aspects. In the Dick’s Sporting Goods example above, the specific aspects worth tracking are ‘customer service’ and ‘return policy’.

3. Determine if particular customer segments feel more strongly about your company

When paired with demographic and other quantitative data it is possible to segment the customer base and look at their sentiment in isolation. For example, do customers who spend less feel more negatively (and therefore it is a barrier to them spending more) or are the return policy issues from customers in Miami and not those in New York?

4. Track how a change in product or service affects how customers feel

As the business changes so does the customer sentiment. Publishing a marketing campaign or press release, changing your product’s interface or price structure can have an effect. Tracking customer sentiment can measure this.

A change in score can indicate if a change has resonated with the customers emotionally and was successful. Tracking the sentiment will help you fix any blunders quickly.

If you know aspects and themes in each response, you can also answer questions like: For how long do people react negatively to a product price increase, or do they really love the new feature added? If Dick’s Sporting Goods releases a campaign focusing on competitive prices they would clearly see if people react well.

5. Determine your key promoters and detractors

Customers may be commenting on many aspects of your business, but which areas are affecting your NPS score? A little bit of data science will help you answer this question. By correlating aspects with promoters and detractors it is possible to show which influence the raw NPS score more.

Convinced? Here is how you can get started

Hopefully, by now, you are asking how you can apply sentiment analysis to your own customer feedback. The first and most important thing is to be collecting data. While there are a lot of companies that specialize in collecting customer feedback, even a simple thing like running surveys through Survey Monkey or using the built-in capabilities of customer service software like Zendesk can yield a lot of interesting data.

Next, comes the analysis: assigning sentiment scores and determining key aspects for each comment. You can do this by either by reading and coding the comments manually or by using software or products that focus on customer sentiment analysis.

Finally, use the results of the analysis to implement our five suggestions on how to improve your customer experience. Dashboards and reports can be used to get this new information in front of the people who need to see it.

Curious how sentiment analysis will work on your data? Try Thematic now!