Is there really a way to analyse customer feedback across regional dialects, sarcasm, and slang? Discover modern approaches to understanding customer sentiment and improving CX decisions with AI-powered feedback analysis.
Customers are expressing themselves more freely than ever before across multiple channels - surveys, social media platforms, customer service chats - and understanding their feedback has become increasingly complex.
Whether it's customers saying 'canny believe it', ‘pet’, ‘that’s sick’ on social media channels or frustrated customers leaving sarcastic 'thank you sooo much 🙄' in their survey responses, if you want to analyse feedback at scale - how can you guarantee that the tools you’re using pick up on the weird and wonderful language used by your customers?
If you can't understand what all your customers are saying at scale, you're not just missing feedback - you're leaving your customers feeling ignored and misunderstood and ignoring all the ways they would want you to respond.
Remember when customer feedback was all neat multiple-choice surveys and properly punctuated complaint letters? Those days are long gone. Today's customers write exactly how they speak, whether they're filling out surveys, sending emails, or posting on social media. This shift has created a couple of challenges for customer experience teams:
The same word can mean completely different things depending on where your customer is from. Take "canny" - in Northeast England it means "lovely," while in Scotland it means "cannot." And that's just one word in one country! When you're dealing with feedback from multiple regions, the complexity multiplies exponentially.
"Thank you so much for making me wait three hours, I love standing in overcrowded gates! 😤"
Imagine losing a loyal customer because your tools couldn't detect their growing frustration hidden behind polite words. Their sarcastic 'thanks' was actually a desperate cry for help that went unnoticed.
Traditional sentiment analysis tools might read "thank you" and "love" and categorize this as positive feedback. But we all know this customer isn't happy. Sarcasm creates a deliberate gap between what's said and what's meant - and this gap can completely reverse the meaning of feedback. You need tools that understand what they really mean - and right now, you're probably missing these crucial signals.
The internet has revolutionised how people write. Abbreviated words, missing punctuation, and emoji usage aren't signs of poor literacy - they're evolving forms of expression that carry important meaning and sentiment. But they're also a nightmare for traditional text analysis methods.
Standard approaches to analysing customer feedback often stumble when faced with these challenges:
“You'll inevitably remember comments that correspond with whatever you were interested in that day. Just chucking a few comments at the end of a report you're doing, calling that Voice of the Customer is a bit superficial. Similarly, counting the words and creating a word cloud that shows people mention "Guinness", "repair", and "flat" doesn't really tell us anything.
It's really a way of trying to process all of those comments that we had in an objective way so that we can dig into things in more detail and get to topics that we might not have considered before.”
Chris Haynes - Head of Customer Insight @ Guinness Housing Partnership.
Bias is another area where we see an inherent challenge in AI-driven sentiment analysis. Large language models, which underpin many sentiment tools, often carry biases that can skew results. For example, brand names with words like "green" may receive higher sentiment scores due to associations with sustainability, while human names can trigger problematic stereotypes.
To address this, we've conducted extensive research to identify and mitigate these biases. In one experiment, we found that AI completed "Tommy plays with..." as "cars," while "Sarah plays with..." returned "dolls." More concerning, "Mohammed plays with..." resulted in "guns." This highlights how unchecked biases can perpetuate harmful assumptions.
Our solution is to strip out names and replace them with "Alex"—a name that research shows carries minimal bias across genders and cultures. While complete debiasing isn't possible, we continuously refine our models to minimise the most egregious issues.
Many off-the-shelf AI tools operate as "black boxes," providing results without explaining how classifications are made or why certain fringe cases are included or excluded. At Wordnerds, we want our AI to be as transparent and adaptable to the specific language, geography, and unique vocabularies of our clients. This ensures more accurate and trustworthy insights, tailored to your customers and communities.
The key to understanding modern customer feedback isn't just about the words used - it's about understanding how these words interact with each other and their broader context. Here's what that means in practice:
Words don't exist in isolation. "Sharp" might be positive when describing a suit, but negative for a children's toy. Modern analysis needs to understand these contextual differences.
By analysing how words relate to each other in a sentence, we can better understand true meaning and sentiment. This helps identify sarcasm through linguistic juxtaposition and contradiction.
Regional dialects and internet-speak often follow consistent patterns, even if the specific words vary. Understanding these patterns helps decode meaning across different ways of expression.
Replace outdated feedback tools, or tools that don’t give you the depth of your analysis, with modern tools that can interpret casual language, emojis, and regional expressions. Your tool should also:
Getting this right isn't just about accurate data - it's about truly understanding your customers' needs and frustrations. When you can properly interpret feedback regardless of how it's expressed, you can:
As communication continues to evolve and we experience the birth of even more feedback channels, the challenge of understanding customer feedback will only grow more complex.
But with the right approach and tools, these challenges become opportunities - opportunities to better understand your customers and improve their experience, no matter how they choose to express themselves.
Remember: your customers are telling you what they think and feel - you just need the right tools to understand them.
