All of us have left customer feedback in some shape or form.
From reviewing a book you’ve bought on Amazon to shouting about a brand on Twitter to filling in a 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 these 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 products 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 the 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 analyzed to gain valuable insights into customer preferences, satisfaction levels, concerns, and suggestions.
The analysis of customer 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 tool like Chattermill to unify and analyze 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 Twitter users saying about your brand? Through customer feedback analysis, you can find the answers to these questions.
Then we need to take that insights from your customer feedback and act on it.
Who within the organization 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 analyzing customer feedback difficult?
There is a lot of it
We need to get data from various sources to understand our customers comprehensively.
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.
People use complex language
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.
Semantic differences – size and fit, for example – can sometimes be hard to differentiate.
Polysemy, where words have multiple meanings, can pose a challenge without a certain degree of context.
Add spelling mistakes or regional and demographic variations, and it’s easy to see why AI is becoming increasingly valuable when processing text-based feedback in all its diverse glory.
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.
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 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 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.
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
NPS surveys
NPS surveys 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
CSAT (or customer satisfaction) surveys ask 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, Twitter, Instagram, etc., to get a measure of sentiment around your brand’s CX.
Social media mentions may be more difficult to analyse manually t, but they can be an excellent barometer for perceptions about your brand, products, and services.
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.
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.
