The six most common methods of feedback analysis used by CX and insights teams, and why they might be holding you back from driving change for your customers…
Every day, customer insights professionals face the same challenge: mountains of unstructured text from surveys, social media, and staff feedback, all waiting to be transformed into meaningful action plans. But here's the problem - the very methods you're using to analyze this data might be preventing you from discovering data-driven insights that drive real change.
But how do you tackle this mountain of text analysis effectively?
Before you invest more time analysing thousands of customer comments, let's examine why the most common text analysis methods might be holding you back from discovering insights that drive real change…
While human analysis is feasible for small datasets, it quickly becomes problematic when you need to manage multiple datasets from a range of sources. Beyond the obvious challenges of time, cost, and team morale (nobody wants to spend weeks tagging comments), manual tagging introduces:
While selecting powerful quotes helps illustrate your findings, building your entire analysis on individual comments is like trying to understand an ocean by looking at a single drop of water. You're left with:
In a world where big bosses need numbers to back up their decision, a one-off post from hotguy2739 will only get you so far.
Major survey and social listening platforms excel at numbers, but their text analysis is like trying to understand Shakespeare through a calculator. Their algorithms:
This is why many organizations use specialised text analytics tools alongside their existing platforms.
Creating an in-house text analysis solution sounds appealing until you realise what it actually requires:
We’ve got years of experience with linguistics and AI, we’ve made all kinds of mistakes and misfires on our way to building a platform that can read and understand text, isn’t biased and provides insights out of the box.
If you decide this is for you, then do reach out to us, we’d be delighted to help. But for most organisations, homemade NLP isn’t worth the stress.
Though visually appealing, word clouds strip away all context - and context is everything in language analysis. The same word can mean completely different things:
Word clouds strip away all meaning from customer feedback, miss critical phrases and word combinations and they don’t get to the actual crux of the problem customers describe, providing no way to gain actionable insights. Simply counting word frequency won't deliver meaningful insights, word clouds should not be a key part of your CX strategy in 2025.
Surprisingly, this is also a common approach. Many organizations let valuable text data gather dust, missing opportunities to:
If you're reading this, you're already ahead of many organizations in recognising the value of your text data. But are you getting everything you could from your qualitative text feedback?
Relying on manual feedback analysis can feel like running uphill—slow, exhausting, and often leading to frustratingly incomplete insights.
It barely scratches the surface of what's possible, which is why customer-centric businesses are turning to AI-powered tools to uncover deeper insights in their text data that would be impossible through manual analysis. To truly enhance your customer experience you have to have a clear way of turning your qualitative text data into actionable insights.
Want to discover what these options are? You can download our buyers guide for free which walks you through the various AI-powered feedback tools and how to pick the right one for your current situation.
