Customer Feedback Analysis: How to analyze and act on feedback data

December 10, 2023
Reading time:
2
minutes

All of us, as customers, have left some type of feedback.

From reviewing a book you’ve bought on Amazon to shouting about a brand on X (Twitter) to filling in a customer feedback survey when prompted to by a new app on your phone, leaving feedback is a regular occurrence in the digital era that we probably don’t think about too much.

Even offline, customer service phone calls are recorded, while bricks and mortar stores often capture feedback through handwritten comments or verbal responses to staff members.

For brands, gathering customer feedback data is not particularly new. But our ability to collect customer sentiments, comments, and opinions from a vast range of touchpoints – and a growing proportion of our customer base – has massively improved in recent years.

This development is exciting for those interested in CX and helping brands better their product features and services.

But what is arguably even more exciting is the growing opportunity we have to transform text-based feedback into actionable insights.

What is customer feedback analysis?

Customer feedback analysis is the process of systematically examining and interpreting feedback received from customers about a product, service, or overall customer experience.

It involves gathering customer feedback through various channels such as surveys, online reviews, social media comments, and customer support interactions. The collected feedback is then analysed to gain valuable insights into customer preferences, satisfaction levels, concerns, and suggestions.

The analysis of user feedback often involves several steps.

In the first place, it is being able to gather this feedback content from customers. We want honest commentary, criticisms, and reviews in their pure free text form from as diverse a range of touchpoints as possible and from all stages of the purchase funnel.

Secondly, it is about transforming this feedback into insights. For this, you need an AI-powered analysis tool like Chattermill to unify and scrutinise your customer feedback data at scale.

What proportion of your customers are expressing negative sentiment at the delivery stage of the journey? What are the popular phrases consumers use alongside mentions of your latest product? What are X users saying about your brand? Through customer feedback analysis, you can find the answers to these questions.

Then we need to take those insights from your customer feedback and act on it.

Who within the organisation is best placed to take the necessary action – your eCommerce managers, product developers, customer support team, etc.?

And how can these valuable insights be used to make changes in the short term and be embedded into strategy and process going forward?

Why is customer feedback analysis important?

Listening to your customers is essential in today’s competitive market. It’s never been easier for them to shop elsewhere or share negative reviews about your brand.

Customer feedback is a vital part in customer retention and loyalty. Any pain points your customer experiences in their interactions with your brand are areas to address in order to retain those customers long term. Placing your customer at the heart of everything your business does and employing a customer-centric approach, leads to happy customers and better engagement with your products and services.

Customer feedback analysis: challenges and their solutions

Feedback comes from multiple channels

We need to get data from various sources to understand our customers comprehensively. But when you’re receiving reviews and comments from social media, apps, websites, review sites, emails, in-store and more, it can feel overwhelming.

That is a lot of customer interactions, even for small or medium-sized businesses. And when brands are growing quickly – acquiring new customers and breaking into new markets – it is easy to see how the sheer amount of feedback that needs to be processed is far beyond the capabilities of any number of staff who are having to do so manually.

This is ultimately why we have developed our tool at Chattermill. We want to automate the process – incorporating AI and machine learning – so our clients can analyze all this feedback at scale.

Interpreting and categorising feedback

Gathering feedback from a vast range of customers across a massive number of touchpoints is already pretty complicated.

If we consider, too, the nuances and quirks of language (or languages for international brands), we can start to see how difficult it is to analyze feedback. That’s before we consider spelling mistakes, regional and demographic variations.

For the feedback to be useful, it needs to be actionable by your teams. If you export and collect all the data into one place (like an Excel sheet), it’s much more manageable to interpret. The next level is an AI-powered analysis tool like Chattermill with its ability to interpret and categorize feedback at scale.

Feedback quality varies

While the meaning of words can vary, so can the quality of feedback itself.

In short, not all customer interactions are made equal. Some is insightful and some is really not.

In the first place, customers might be more (or less) honest depending on what channel they are leaving their feedback.

They might also express a slightly different sentiment depending on where they are on their customer journey. For instance, they may be unable to remember their frustrations with your checkout process when providing feedback weeks after receiving the product they bought.

What can be gleaned from both insightful and non-insightful feedback in the sentiment - are the words used generally positive or negative?

What are the benefits of customer feedback analysis?

Understand your customers on a deeper level

Customer feedback is the only way to really understand what your customers think and feel.

The hard numbers we find when we look at website data analytics – such as traffic, referrals, and conversions – can give us some insight into what consumers want and their experiences.

But it is only part of the story.

Here at Chattermill, we want to help brands get the single source of customer truth. And to do that, we need to unify customer feedback with other available data.

Find friction points in the customer journey

With the explosion of omnichannel touchpoints and an increasingly personalized path to purchase, customer journeys are becoming more complex.

Brands have come a long way in giving customers many options to engage with and buy from them. But friction is still an issue. It costs UK eCommerce businesses around £36bn a year.

For Daryl Wilkes at Asos, customer feedback analysis from contacts is vital to understanding where those friction points are.

‘Customers get in contact because something has either gone wrong or something has not gone how they expected it to play out. It’s about understanding those contacts but understanding the sentiment behind those contacts, matching up the contact reasons with the feedback that you get from your customers so the richness of that feedback is really strong.’

From there, Wilkes and his team are in an excellent position to resolve the issue for the individual who has made that contact and get to the root cause of the friction so other customers won’t be affected by it in the future.

Enhance CX to improve customer loyalty

Understanding our customers and minimizing friction all build towards enhancing CX.

This is vital for nurturing our customer relationships and promoting customer retention.

Today's fundamental difficulty for brands is that customers are less loyal than ever.

A massive 92% of global consumers do not consider themselves brand loyal.

The opportunity here is that the probability of selling to an existing customer is around 60-70% compared to just 5-20% for a new acquisition.

In short, it is well worth building brand loyalty – and a customer experience that frequently delights those who buy your products or use your services will most likely keep them returning.

Improve your Net Promoter Score

Returning customers are great for any business today, but so too, are advocates.

Exceptional CX can help turn regular customers into evangelists for your brand.

Net Promoter Score (NPS) helps brands determine customer satisfaction and what proportion of customers are likely to shout about their experience to their friends and family positively.

Customer feedback can help you understand your own NPS. You can get to the bottom of why your promoters are so keen to promote your brand. And it can steer you towards nurturing this to help improve NPS and CX going forward.

Better products and services

When we think about CX, we think about the experiences your customers have up to purchasing a product or service.

Of course, we know that CX includes much more than that today. How satisfied is an individual once they’ve got the product home? How do they feel returning to the service long after paying for it?

Feedback analysis is fantastic for discovering how customers feel about your products and services. Keyword or aspect analysis, in particular, can help you identify the pain points here – ensuring customers are supported should any issues arise. It also helps product teams with prioritizing feature requests and new product development.

More business growth

This is the ultimate benefit.

We at Chattermill want to help businesses scale up.

A proper automated feedback analytics program can keep new and returning customers happy – growing sales, growing purchase frequency, and raising your proportion of seriously impressed customers.

Most common ways to collect customer feedback

CES surveys

Customer effort score surveys are based around the simple question, “how easy was it...” to complete a specific action. That could be to interact with a website, use a product or similar. Users feedback with a rating that’s usually between 0 and 10. This provides insightful quantitative data to work with.

NPS surveys

NPS surveys (Net Promoter Score) ask your customers the straightforward question: “How likely is it that you would recommend us to a friend on a score of 0-10?”

Detractors are customers who responded in the 0-6 range, Passives are customers who responded in the 7-8 range, and Promoters are customers who responded in the 9-10 range.

It is then possible to work out overall customer satisfaction and an overall NPS score for your brand.

CSAT surveys

Another type of customer satisfaction survey is the CSAT which asks the question: “How would you rate your overall satisfaction with the [goods/service] you received?”

Respondents will rate your goods and services 1 through 5 (with 1 being very unsatisfied etc.), and from there, brands can judge the proportion of their customers who are most likely to return.

Social media mentions

Customer feedback analysis can also include social media mentions – crawling through thousands of posts across Facebook, X, Instagram, etc., to get a measure of customer engagement and sentiment around your brand’s CX.

Social media mentions may be more difficult to analyse manually, but they can be an excellent barometer for perceptions about your brand, products, and services.

Emails and SMS

Surveys sent to your database audience via email or SMS can be very targeted, going only to those you select as appropriate. For the audience, they can answer it when they choose. They’re often used to follow up on a purchase or interaction.

Live chats

On your website, you can host a chatbot to gather real time feedback from users. You chat app can ask if the webpage was helpful and if they found what they needed. This feedback on usability can inform new features and product development.

Feedback forms

These forms are always present on your website, providing a constant way for customers to share piece of feedback. It’s a useful source of qualitative data to interpret.

Support call transcripts

Those who don’t want to leave feedback online tend to call your customer support team. These types of customer share their insights and views of their user experience, providing indispensable actionable insights.

Public reviews

According to Brightlocal, 72% of Americans have written online reviews for a small business.

There is a lot of content written on review sites. Customer feedback analysis helps you understand what users say about your brand – giving you insight into sentiment about your products and services and helping you drill down into what part of your experience customers love and needs improvement.

What is the difference between customer feedback analysis and customer review analysis?

Feedback tends to be given privately between the customer and the brand. Your customer service team can enter into a dialogue with the customer to resolve the issue (hopefully). Customer reviews are given publicly and are available for all to read online.

Both provide valuable insights into customer experience and opinion which can be used for meaningful change. But the private v public nature of the feedback may result in different levels of openness and customer sentiment.

How to analyze customer feedback and act on it?

Analyze NPS responses to understand what drives loyalty and churn

Getting an overall NPS score for your business is great. You can see how many of your customers are promoters, how many are passive, and how many are detractors. You can also see how you compare to your industry average.

Analyzing NPS responses helps you understand why customers fall into the above groups.

Are most of your promoters mentioning the product, for instance? Are your detractors referring to delivery problems?

NPS analysis helps you understand what is driving brand evangelism and, on the other hand, brand churn.

Segment customers by NPS scores and feedback

Together, NPS scores and customer feedback are helpful in helping you segment your customers.

You may want to reward your evangelists with special deals and coupons. You may want to invest more time and effort to win back a proportion of your detractors.

Customer segmentation is crucial for shaping strategy and maximizing your resources.

Use an NLP tool to conduct a qualitative feedback data analysis

Natural Language Processing (NLP) brings together linguistics and AI.

We know that language is very complex. But when it comes to feedback analysis, an NLP tool can help us identify and tag feedback to automate – and speed up – the process of transforming qualitative free text into actionable insights.

Act on feedback and implement solutions

This is the most important part of feedback analysis: getting the voice of the customer into the business and using it to make a difference.

Who within your organization is best placed to take the insight you have found in the feedback and act on it?

Perhaps your web developers need to redesign a part of your website. Or maybe your customer service team needs to reach out to specific customer groups individually.

Whatever needs to be done, the fact is that the actual value of feedback analysis can only be fully realized when those who need it have access to the insight you have found and then have the means to make the necessary changes in the real world.

Case study example

Goodiebox

Every month, Goodiebox receives a high volume of support tickets and customer feedback data, in multiple different languages. Manually tagging all of these support conversations was time-intensive and impossible to scale. Goodiebox agents had to tag support conversations manually one by one, on top of making sure they also categorised all other relevant information.

Using Chattermill tools to automate tagging, Goodiebox quickly identified the root cause behind product issues and knew exactly how many members were affected by it.

By leveraging Chattermill’s solutions, Goodiebox is now able to automatically analyse the topics of incoming support conversations and be able to help members by delivering these insights to the corresponding teams instantaneously. Find out more

How to analyze customer feedback manually

Customer feedback analysis is a time-consuming and subjective when done manually. Consequently, it’s really only advisable for small scale feedback analysis by one person.

  1. Select your methods of feedback. Don’t try to use them all. Select the most appropriate, be that social media, customer surveys, reviews or support call conversations.
  2. Create a single source of truth. Collate all of your data in one place - an Excel spreadsheet is ideal. Collect the same data on each piece of feedback and create a complete dataset. Then you can use it to get a meaningful result.

Smart tools to automate your feedback analysis

Instead of dedicating hours of your team’s time to analyze feedback manually, you can employ a far more efficient method - smart tools. There’s a plethora of smart customer feedback analysis tools available, so it’s a good idea to start by thinking about the functionality you need.

  1. What do you want to analyse? Survey responses, NPS, social media? Some tools will only read one, others are able to combine data sources.
  2. How deep do you need the insights to be? Some tools drill down to a granular level of detail to provide powerful insights. Others will only provide surface level information.
  3. How will the analysis be used? Is it for a quick and simple report? Or do your teams need continuous updates via dashboards pulling specific datasets and metrics?

When choosing the best smart tool for your business, it’s important to consider both the input of data sources and the output of data visualization.

Chattermill

Discover customer insights about your products, operations, and customer experience by analyzing customer feedback at scale using AI, and delivering meaningful intelligence that enables you to see and act on the customer reality.

No matter how much customer feedback data you collect, Chattermill integrates with all of your tools (such as a CRM or analytics platform), across all channels, to create an intelligent, single source of customer truth. Discover more

IBM Watson

The IBM tool specialises in sentiment analysis based on NLP. The analysis process focuses on customer satisfaction surveys and support tickets in particular. Learn more

Survey Monkey

As well as building forms and surveys, Survey Monkey can be used for data analysis. Collect feedback and benchmark CSAT, NPS®, customer service feedback and customer effort score (CES). Through analysis, you can transform feedback into actionable insights. Find out more

Conclusion

Find out what your customer needs, wants and feels by simply listening to them and acting on their feedback. It sounds simple, and it can be. Data-driven decision-making will make a powerful impact on your business. But you don’t need to do it alone. There are incredible, smart tools available to help you.

To learn more about how Customer Feedback Analytics tools like Chattermill can help you gather and analyse customer feedback at scale, book a demo.


See Chattermill in action

Understand the voice of your customers in realtime with Customer Feedback Analytics from Chattermill.