More and more of our day-to-day activities occur online.
Growing numbers of us are shopping via digital channels. We stream media. We use banking apps, book holidays on travel comparison sites, and chat with friends on social media.
And, increasingly, we share our experiences there too.
From online reviews to emails sent to customer service teams, we are writing a whole lot of content about our experiences with products and services.
For brands, that’s a hell of a lot of potential insight.
Feedback analytics helps you make sense of all that. It separates the signal from the noise, turning all of that free text into actionable CX insights.
Why is it important to analyse customer feedback?
Chattermill’s Dave Ascott sums up the importance of analysing customer feedback succinctly:
“It’s how you understand what your customers think and feel.”
At Chattermill, we want to help you have a comprehensive view of the customer experience.
To get this single source of customer truth, you need to unify hard numbers with what’s traditionally been more difficult to quantify: customer sentiment from customer feedback.
By analysing this feedback, you can understand the pain points on the journey your customers take from discovery to purchase and beyond.
You can also uncover the accurate perception of your brand and service among your high-value customers and – crucially – those whose loyalty might be on the wane.
The ROI of feedback analysis
We know the importance of seeing the financial return of your time and money on CX.
Brands across the board want to see their money go further.
We also know that linking customer experience to ROI is notoriously difficult.
For example, identifying the many reasons customers have for buying a specific product or returning to buy more products may not be immediately clear-cut.
But if feedback analysis is done well, it can be.
First, the necessary feedback channels must be open for customers to leave feedback. Additionally, the right analytics tools must be used to analyse and highlight actionable insights from your customer feedback.
That takes a little more investment than manually reading through NPS responses – but once those things are in place, the improvements and returns are striking.
If we focus on reducing customer churn, we can identify a relatively simple way of linking ROI with feedback analysis.
If you can work out why customers are leaving and then address this to convince them to stick with you and shop again, it’s easy to see the real-world value of listening to your customers.
Beyond this, if we can identify what would nurture those customers to place orders more frequently, buy higher-value items, or become evangelists for your brand, then you can begin to see the real-world value of feedback analytics in the long term.
The risks of not implementing feedback analysis
Today, customer experience is the key battleground on which businesses compete.
Name brands – from Amazon to Boots – invest near limitless amounts of money into understanding how customers behave and what they say. Market disruptors know, too, that CX is an area where they can compete with established businesses.
Uber is a particular case in that it has moved from disruptor to market leader in relatively recent memory. Maisie Lam, Head of Customer Experience for Uber in Australia and New Zealand, talks to Forbes about their drive towards frictionless CX:
‘A negative peak or an end to an experience can completely bias a customer’s judgement,’ she says. ‘It means you need to know where in your users’ experience there is negative friction, or even positive peaks and resolutions.
In short, if you are in the dark about what your customers think, feel and say, you are already losing out to the competition.
Those brands on the front foot when it comes to listening to consumer feedback and building that comprehensive view of the customer – the single source of truth – will be improving their CX and keeping their customers loyal.
Where to find the customer feedback for feedback analysis
We know something about the why and how of customer feedback. But there is also a question of where we should focus our attention when knowing what shoppers say about their experiences.
The best practice approach is to find out how your customers feel in relation to your whole organisation. This will likely include the checkout process, delivery and fulfilment, as well as post-purchase support.
Sources might include social media, review sites, and direct invitations to submit feedback at different points along the path to conversion.
Unfiltered responses direct from customers are vital, but so is having the necessary channels open to ensure your frontline staff and customer service teams can share what shoppers say.
In our CX leaders roundtable, How to solve friction in eCommerce, Figs’ Michael Bair speaks about the value of feedback across the whole customer journey to get the complete picture.
‘All forms of data and customer feedback is important,’ he says. ‘But you really have to wait for the feedback you’re getting from different channels.’
‘Certain channels, for example, Facebook, are more public, but they’re probably a really low portion and can get lost very quickly. CX interactions are your biggest source, but their feedback is very one-to-one.’
As Bair highlights, there can be subtle differences in what people share depending on where they are on their journey.
This is reinforced again by Chattermill’s Dave Ascott, who reminds me that customers rarely comment on the fulfilment or delivery of a product unless they leave feedback immediately after having that part of the experience.
For offline and omnichannel sellers, in-person feedback can offer another perspective too. There must also be opportunities to hear from customers in bricks and mortar stores.
Challenges to customer feedback analysis
As we’ve touched upon above, best practice in customer feedback data analysis calls for freeform text from a massive variety of sources which need to be analysed.
Here at Chattermill, the critical challenge of unifying and analysing all of this data at scale is what drives us.
We call it Unified Customer Intelligence. Using machine learning and AI to glean insight from free text across many touchpoints.
Of course, analysing feedback before we came along wasn't impossible. But without automation and the means of presenting this insight in one centralised place, it was a massively slow, manual, and expensive process.
Unified Customer Intelligence means you can analyse your customer feedback at scale, gather all this insight in one place, and take action off the back of it.
Customer feedback analysis methods
There are three key feedback analysis methods. They are:
1. Sentiment analysis
Sentiment analysis identifies the emotional tone behind what your customers say about your brand, product or service. Do they love it? Or do they hate it? It’s good to know either way.
2. Keyword or aspect analysis
Keyword and aspect analysis helps to identify the important non-sentiment words that come up frequently in feedback. From parts of the product to parts of the payments process – this helps pinpoint the elements of CX that might need attention.
3. Topic analysis
Topic analysis uses machine learning to detect and assign topics or tags to free text feedback. You might have thousands of emails about delivery – topic analysis can help you dig out all the emails about slow delivery, late delivery, or missed delivery windows, for example.
Which text analysis method is best used for analysing customer feedback?
All of the above types of feedback analysis have their place.
But the ultimate goal is not to find what you want in the data but to understand your customers accurately.
In short, you need to unify the data to understand customer feedback – with all its language nuances.
Driving action from customer feedback analytics
So you have all of this feedback. You’ve even begun to notice trends among sentiment and key topics your customers are alluding to. But you still need to be able to quantify it.
Feedback analysis software such as Chattermill can help convert this free text feedback into insights.
H&M’s Ross MacFarlane has spoken about this for our CX leaders roundtable, Transforming CX in fashion & retail.
‘There’s a big challenge for us in that the data’s so scattered,’ he says. ‘There’s so much data available.’
‘Chattermill’s really helped us a lot to be able to unlock some of that unstructured feedback and identify drivers of customer experience positive and negative.’
The key here is being able to assign quantitative (or numerical) tags to qualitative (text) feedback at scale to help build your Voice of the Customer (VoC).
Once the VoC is in the business, the next step is being able to distribute this data to those who can take action.
These might be product managers, customer support leads, or operational teams. But the key is ensuring that everyone within the business has access to this data and the VoC – and that everyone is working off that single source of truth.
The result is not only a unified view of the customer, but also a unified business.
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