Discover the power of online reviews and learn how AI-powered analysis helps CX teams uncover customer insights, benchmark competitors, and drive smarter decisions.
CX is your brand's most powerful differentiator, of companies that focus on CX, there’s an 80% increase in revenue (Zippia). Your online reviews are your most public form of feedback data and can be some of the richest, most untapped sources of insight into what your customers really think. But most brands still don't know how to dig out the good stuff.
In this blog, we’ll explore:
Customers leave reviews when they're passionate, whether positive or negative, which makes them a goldmine of insights. Yet, many businesses only look at star ratings, missing the real value in the words customers use.
85% of customers want businesses to anticipate their needs. Understanding their feedback helps you meet those expectations.
As Pete Daykin, CEO of Wordnerds, explained during our webinar:
"Online reviews are a figurative gold mine of lots of amazing data that you can use to your advantage in a bunch of different ways. But most companies, most brands that we talk to fail to utilise them to their max."
The challenge for many organisations isn't collecting reviews, most have more reviews than they know what to do with, it's extracting meaningful, actionable intelligence from them. Without proper analysis, valuable customer feedback remains buried in all that text data, where it's about as useful as a chocolate teapot when it comes to informing strategic decisions.
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While many brands actively manage their online reputation by responding to reviews, significantly fewer analyse review content to understand what customers are truly saying about their experience.
"A lot of the brands that we're talking to, some of them proactively manage their reputation. They do the kind of responding to reviews. But remarkably few of them do the analysing reviews and understanding the content of them."
This represents a missed opportunity, as reviews often reveal insights that aren't captured in traditional customer surveys or complaints data.
There might have once been a time when you did manual tagging of your customer feedback — but manual tagging is time-consuming, reactive, and can be very biased. This is why many CX teams are turning to AI to help them analyse their feedback. It brings about a whole host of benefits, AI-powered review analysis can help CX teams:
Instead of just tracking star ratings, you uncover hidden pain points, shifting customer needs, and strategic opportunities.
For companies looking to level up their review analysis game, Pete outlined a clear maturity model for text analytics tools:
We have an in depth guide which explains all the potential options for feedback analysis, and the pros and cons, in our AI Feedback Analysis Comparison Guide.
When discussing AI for feedback analysis, it's important to be specific. Most people immediately think of general-purpose LLMs like ChatGPT and Copilot. While these platforms could potentially analyse your reviews, they lack the specialised accuracy and reliability needed for proper customer feedback analysis…
We explore this in our blog “Can I use Copilot or ChatGPT to analyse my Voice of Customer feedback?” where we compare results from Copilot versus a specialist tool. We compared both approaches, and guess what? The results were pretty clear. General AI tools just don't catch the nuances that specialised tools do.
The best AI tools don’t just summarise reviews — they give you the ability to understand them.A platform like Wordnerds combines NLP, linguistic rules and machine learning to help you cluster customer language into themes and emotions.
Unlike basic sentiment analysis that just sorts "positive" from "negative," with Wordnerds you train your own model in 15 minutes and it can detect when a customer is frustrated about delivery but loves your product, or when they're using regional slang to describe a feature. Instead of sifting through comments manually, the final product you get is:
The result? You'll be able to make strategic decisions based on making your review data quantifiable and evidence based, rather than skimming verbatim and relying on gut feel. And by focusing your resources on the areas where competitors are weakest, you can create compelling differentiators that win over their customers and protect your market share.
Wordnerds recommends a simple but powerful approach to getting more from review data:
Here’s where things get really powerful. By analysing your competitors’ reviews alongside your own, you can benchmark how you’re performing in the market, not just internally.
Competitor benchmarking allows you to:
Want to see how competitor benchmarking using review data works in action? In our latest Webinar ‘Use AI to analyse reviews, understand your customers and benchmark against competitors’ resident Wordnerds analyst, Stella, rolled up her sleeves and conducted a proper data analysis comparing Trustpilot reviews for three UK supermarkets: Waitrose, Morrisons, and Aldi.
The analysis revealed several interesting findings:
Morrisons showed a significant increase in sentiment over the past year, which correlated with their high level of engagement with reviews:
"Morrison's 96% of the negative reviews are replied to typically within 24 hours. If we compare this to Waitrose, 62% of their negative reviews are replied to typically within one week. And then finally, we compare it to Aldi, they don't reply to their reviews at all."
This suggests that simply responding to customer feedback—particularly negative feedback—can positively impact customer perception.
The analysis identified key themes driving customer sentiment across all three supermarkets, and some might surprise you:
"Emotional effort, which is things like anxiety or stress or any emotional feelings towards their shopping journey... is the top mentioned thing for customers, followed by praise for the in-store, monetary themes, personal interaction, staff interaction."
(Yes, "store vibes" was actually one of our themes. That’s what you get when you put Gen Z’ers in charge of naming themes). But we’re so here for it.
Using a retail customer journey framework, Stella mapped customer sentiment across different stages of interaction with each supermarket:
"Here with the categories of the themes that we've trained, we can see the volume and the sentiment at each stage in the customer journey."
This revealed that Morrisons led in most categories, particularly around in-store environment, positive staff interactions, and surprisingly, value for money—even outperforming Aldi on the latter, despite Aldi's reputation as a discount supermarket. As Stella put it, "That wasn't what I was expecting!"
The analysis examined what drove customer loyalty by looking at reviews where customers self-identified as loyal:
"What are people talking about when people self-identify as being a loyal customer? This can see what really drives people's loyalty... positive staff interaction, in-store environment, store vibes, promotions and offers, navigating the store, value for money."
Interestingly, 15% of Morrisons reviews mentioned loyal customers, compared to just 7-8% for Aldi and Waitrose.
For each supermarket, the analysis identified specific areas driving negative sentiment:
"For Morrisons... contact center experience, delivery, collection, communication, negative staff interaction. These are the things that we would focus on if we were giving recommendations to Morrisons."
The analysis showed that Morrisons' issues were primarily external (contact center, delivery), while Waitrose faced more in-store challenges like payment experiences and returns.
The value of this analysis extends well beyond just making customers a bit happier. As Pete explained, these insights can directly impact your bottom line:
"We can take any kind of quant data as well and look at what the drivers are compared to those metrics. So what things make spend go up, what things make spend go down... The holy grail is building predictive models."
Here at Wordnerds we’ve worked with customers on predictive models that typically start at 80-85% accuracy and get better the more data you chuck at them. This helps businesses anticipate customer behaviour and—most importantly—revenue impacts before they happen.
As AI continues to make its mark in the world of e-commerce and customer interactions (as Ross brilliantly outlined), understanding what your customers are actually saying becomes more important than ever.
Organisations that embrace proper text analytics tools and methodologies will absolutely smoke their competition through deeper customer understanding and more targeted experience improvements.
By transforming all those review data goldmines from an underutilised resource into actionable business intelligence, you can make smarter decisions, fix customer pain points before they become massive issues, and ultimately deliver better experiences that drive loyalty and growth.
If you're interested in learning more about the power of review data, you can watch the full webinar 'Use AI to analyse your reviews, understand your customers and benchmark against competitors'.
