Customer Sentiment Analysis: Everything You Need to Know

Gaining a better understanding of how your customers feel about your brand can have a huge impact on your confidence for future strategies. Here are all the main things to know about customer sentiment analysis…

Customers will experience a variety of emotions while interacting with a brand, whether good or bad. But being able to understand and analyse these emotions from a business perspective can sometimes be a challenge. Here, we’ll discuss what customer sentiment is and how businesses can measure it before explaining how to handle your own analysis.

What is customer sentiment?

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Customer sentiment refers to the emotions your customers experience while engaging with various aspects of your brand. One of the best examples to illustrate this is that they could be happy while scrolling through your products, but may become frustrated by your purchasing pages.

Analysing customer sentiment can be complex, but once this has been achieved, it can provide an invaluable indication of the emotions your customers are experiencing. Tangible data can be collected using tools like AI to collect information on habits and behaviours from groups of customers. Emotional data can also be measured and collected using natural language processing which analyses emotions and categorises them. Using this data in a similar way to how you would use CSAT and NPS, your business can evaluate these in context and gain a better understanding of changes that might need to be made.

Happier customers are more likely to return or go through with a purchase, while an unhappy customer isn’t likely to bring repeat business. If many of your customers seem to be unhappy when interacting with certain elements of your brand, this could be a sign that things need to change.

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How does customer sentiment analysis work?

The analysis of customer sentiment simply means that you can work out how your customers are feeling. This can be done in a few different ways, including the automatic detection of emotions during customer engagement.

Natural Language Processing, or NLP, is one of the most common methods used for customer sentiment analysis. The algorithms tend to use polarity and magnitude to analyse customer emotions and separate them neatly into categories.

See below for the definitions of these terms.

Polarity: Whether the emotion is negative or positive

Magnitude: Indicates the strength of the emotion

How to measure customer sentiment

Measuring emotions can be tricky – the key is to collect the contextual data around customer feelings so that you can carry out insightful structural analysis. Below are a few key examples:

Map any issues across your site:

Put out fires before they get any worse by grouping emotions to the area of your app or site that they’re related to. This will help you fix any common problems much more efficiently.

Sort data by customer type:

Breaking customers down into demographics, the length of time they’ve been engaging with your brand or whether they’ve purchased from you can help you target anything in your strategy that isn’t working and where you’re meeting expectations.

Analyse the data over time:

Do negative emotions spike around a certain time of year, such as the retail peak season? This could indicate issues such as long average handling times and low first contact resolution. Find out where the peaks and troughs are for your customers’ emotions.

Use artificial intelligence software:

AI can help you automatically collect, group and analyse emotional data shared by your customers. The longer it’s used for, the better it can be at showing you the data you need at the right times.

How to do customer sentiment analysis

So how can your business get started with customer sentiment analysis? Follow our steps below…

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Use NLP for analysing customer interactions

Natural language processing is one of the main tools that can assist with analysing customer sentiment. This can be implemented through AI and allows your technology to separate the language used in customer service interactions based on the predetermined categories. Your business can then collect the data on magnitude and polarity and look at this alongside contextual factors like mapped issues and data over time.

Gather customer feedback

Feedback can be incredibly valuable for businesses in all types of industries. From brick-and-mortar retailers to e-commerce sites, you should never underestimate the importance of gathering feedback from a wide variety of your customers at as many points as possible.

Whether it’s after purchase or when a customer abandons their cart, contacting them asking for feedback can give you valuable insights into the emotions they felt at different points and ultimately why they did or didn’t purchase from you. The feedback can then be collated and analysed to look at the most common thoughts, feelings and emotions at different points of the customer journey.

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Use social media

Social media is a very powerful tool, and it’s only going to get stronger. A negative social media presence could do serious damage to your brand reputation if you’re not careful.

Monitor the main social media sites using your brand name to see what people are saying about you. By analysing the data on social media, you can find out the polarity and magnitude of your customer’s opinions.

Monitor service and product reviews and ratings

We’ve all been faced with rating buttons asking us how we felt about our experiences, usually ranging from zero to five or showing angry to happy emotions. Whether you’re shopping with an online retailer or you’re in the airport, these buttons are everywhere, and there’s a good reason for why.

These buttons help businesses monitor customer emotions towards their brand quickly and easily, with little to no effort required from the customer.

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Create personal connections and interactions with your customers

The more you get to know your customers, the easier it should be to understand how they’re feeling about your brand. The tricky thing here, though, is that customers are usually only likely to initiate these types of interactions when they’re at extremes of emotions. Whether they’re really happy with their service or they’re incredibly disappointed, you’ll probably hear from them if your communication lines are open. And while these interactions can be valuable and show you where you’re doing things right or where you’re going in the wrong direction, it doesn’t tell you anything about those that feel the emotions in between the extremes.

The solution to this could be adding exit surveys after customers engage with your representatives or chatbots to find out what they thought. It could also be good to add in more opportunities throughout the user experience where customers can voice their opinions. Of course, you don’t want to bombard them with too many pop-ups, so keep things minimal and non-invasive so that their user experience isn’t interrupted by these things.

The more personal interactions you can have, the better your understanding of customer sentiment will become.

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