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If you’ve spent time working with customer feedback tools, you’ve likely come across platforms that use keywords to tag and categorize what customers are saying.
You type in, or upload, some terms you care about (“delivery delay,” “app crash,” “wait time”), and the platform automatically scans incoming feedback for those phrases. When it finds them, it tags the relevant entry.
On the surface, this feels like efficiency. You get dashboards. Trend lines. Maybe even a few alerts. But there’s a catch.
Keyword-based tagging only works well when you already know what to look for.
And that’s the core problem: these tools are built on assumptions—assumptions about what your customers are saying, how they say it, and what matters most to them. If those assumptions are off, or if your customers evolve faster than your keyword list does, your customer intelligence quickly becomes not very…intelligent.
Let’s unpack how keyword-based tools actually work, how clustering-first approaches offer a fundamentally different (and, we argue, better) model, and why the customer intelligence platform you choose should think more like a detective than a librarian.
How keyword-based feedback tools work (the technical-lite explanation)
Keyword-based tagging systems are, at their core, pattern matchers. You supply a list of words or phrases, and the system looks for literal or close-to matches in your customer feedback—product reviews, survey responses, support tickets, call transcripts, and so on.
In most cases, these tools rely on natural language processing (NLP) techniques such as:
- Tokenization: Breaking down text into words or phrases.
- Stemming and lemmatization: Reducing words to their base form (“running” becomes “run”).
- Boolean logic: Tagging entries with the operators “AND”, “OR”, and “NOT” that are used as conjunctions to determine outputs (e.g., find me feedback that mentions “Android” and “crash”).
Advanced tools might layer in sentiment analysis or word (vector) embeddings to handle synonyms or contextual similarity (more on the latter below).
This approach isn’t inherently bad. For known issues or generic customer feedback terms, keyword models can be incredibly useful. But they hit a wall when customer-speak changes (e.g., people stop saying "shipping delay" and start saying "i think my order is ghosting me"), when feedback comes in in languages other than English, or when customers describe a problem indirectly or emotionally, not literally.
It also becomes a problem when new issues emerge that aren’t on your radar yet. When that happens, keyword-based tools don’t just miss insights—they reinforce your company’s blind spots.
How clustering works in LLM-powered customer intelligence tools
Clustering, on the other hand, flips the script. Instead of asking, "Does this comment or entry match what we already know?" clustering asks, "What patterns exist in this data, even if we’ve never seen them before?"
In LLM-based platforms, clustering often uses unsupervised or semi-supervised learning to group similar pieces of feedback together. Rather than relying on fixed keywords, these systems analyze the semantic content of feedback entries to detect meaningful similarities (like, ahem, Unwrap does).
Here’s how that typically works (again we went tech-lite):
Vector embeddings:
Each feedback entry is converted into a high-dimensional vector using a language model (e.g., OpenAI’s GPT model, BERT). This vector represents the "meaning" of the entry.
This is essentially the translation stage. Your phone call with the support team at Apple gets turned into a mathematical form that an AI model can understand and extract meaning from.
Dimensionality reduction:
Since these vectors are huge (sometimes thousands of dimensions), techniques like PCA (principal component analysis) or t-SNE reduce them into a smaller dimensional space while preserving the structure of the vector. This helps models focus on the most important information—which in turn, improves their accuracy for grouping—and reduces the amount of processing time and memory needed to do all that computational work.
Think of it like folding a big, crinkly map into a neat, pocket-sized version—you lose size, but you keep the all important streets and landmarks intact. And it’s easier to manage while you drive.
Clustering:
Just as the term implies, this is where the grouping happens. Algorithms like k-means (a type of centroid-based clustering), DBSCAN (density-based spatial clustering of applications with noise), or HDBSCAN (hierarchical density-based spatial clustering of applications with noise) group similar vectors together, albeit using different approaches.
Each approach is best suited to a particular data distribution. Just like Hunter boots are best suited for clomping around the Scottish Highlands (this is no time for heels), the way the data is spread out determines which algorithm you go with.
Every cluster that results ideally represents a distinct theme, issue, or customer insight. The goal is that the algorithm does a really, really accurate job of grouping like with like.
Labeling:
Once your feedback data is grouped, some systems take things a step further.
Advanced LLMs can generate labels or feedback group names for each cluster using summarization models or statistical analysis of terms within the group. The “human-in-the-loop” approach helps QA this post-processing step, meaning someone’s there to tell the model “yes, this is correct you get a gold star” or “oops, that’s not quite right.”
TL;DR
If your eyes glazed over a bit during the above sections, here’s a super simplified explanation:
Raw, unstructured (meaning not easily readable by a machine) feedback entries are transformed into vectors or the contextual meanings of those entries, ➡️ those meanings get shrunk down so they are easier to work with, ➡️ then they are ordered into groups, ➡️ finally those resulting groups are labeled. All automatically.
You see why this is a big deal, right? With clustering, you don’t need to predict what people will say. You don’t need to create a list of possible product problems ahead of a big launch, crossing your fingers you thought of everything your customers might mention. The AI model finds patterns on its own—even if they don’t match any of your existing tags or keyword lists.
Of course, clustering isn't perfect. It can struggle with edge cases or super variable language. It can produce clusters that are too broad, too narrow, or ambiguous, requiring manual work to resolve.
But when done well, clustering is a tool for discovery—an insights engine, as we Unwrappers like to say.
The assumption gap
Let’s return to keyword-based systems and their central flaw: assumption.
If you’re using keyword tagging to mine customer feedback, you are inherently filtering that feedback through your own biases. You’re saying, “these are the problems we expect to hear” and “this is the language we think customers use to describe those problems”. Everything else? “It’s probably noise, and we don’t need it.”
But what happens when customers start describing an issue in a new way? Or when a bug causes frustration in a part of your app you didn’t think to monitor. Or when—and this is crucial—an entirely new theme emerges: a change in expectations, a new competitor reference, a shift in tone.
If your system is tethered to a static keyword list, those signals go unnoticed. You’re fishing, but not catching anything, because low and behold you’ve got the wrong bait.
And to make matters worse: keyword-based platforms can create the illusion of comprehensiveness. You can get complex dashboards and custom reporting—and it can feel like insights. But all you're seeing is customer feedback as you’ve predefined it.
Clustering-first approach = listening-first approach
A clustering-first approach looks at the customer feedback problem in a different way. It listens first, then looks for patterns without requiring you to define the patterns ahead of time. It respects the messiness and unpredictability of language, because that’s truly where the real insights live.
With a platform that relies on clustering, you can detect emerging issues without needing to constantly refine an always-on keyword list. You can gain a better understanding of the voice of the customer, learning how customers describe problems in their own words. And arguably most importantly, clustering will surface unexpected themes that may reveal new opportunities or risks to your business.
It’s not the end all be all—you still need smart people to review and act on the insights. But that’s true for any tool. The difference is that clustering opens the door to what you don’t yet know. And in fast-moving markets, that’s often the deciding factor between leading or lagging behind.
The best teams know customer intelligence isn’t predefined
It’s tempting to believe that if we just tag enough feedback with the right labels, we’ll finally "know our customers." But customers aren’t static. Neither is language.
If your customer intelligence platform is built on a keyword-based model for its foundation, you’re anchoring your understanding in yesterday’s assumptions.
The future belongs to tools that can listen without a script. That can tell you what’s changing, not just what’s recurring. That ultimately helps you discover, not just confirm.
