If you’re writing your website copy without customer input, you’re going to end up with messaging that doesn’t resonate with your audience.
Message mining is how we turn customer language into copy you can actually deploy. We create structured sets of phrases you can map to headlines, objection handling, and calls to action.
Copy that ignores customer language
When a landing page underperforms, the default response is more opinions. More stakeholder input. More internal debate about “brand voice”. More revisions that change wording without changing meaning.
The team feels productive because something is happening, but most of the time it’s just more of the same, just in different words.
It means we miss what’s important to customers.
- Your headline names a feature, but customers talk about a feeling of relief.
- Your proof section lists credentials, but customers talk about responsiveness and being kept informed.
- Your CTA offers a “quote”, but customers describe the moment they finally stopped worrying.
Message mining changes the input. Instead of asking a room to choose the message, we start with the messages customers already chose when they described outcomes, fears, and trust, in their own words.
The goal is not “inspiration”. The goal is a set of words and phrases you can deploy across pages and campaigns, and reuse the next time you write in the same category.
The message-mining framework
Approaching this as a creative exercise is the wrong way. We don’t want to copy a few reviews, highlight some emotional phrases, and call it “Voice of Customer”
That produces anecdotes. Anecdotes can help, but they don’t scale across dozens of pages, multiple audiences, or a full-funnel campaign.
Our workflow is designed to build a customer focused messaging structure into the foundations of the website.
First we export your Google reviews and those of your competitors into a CSV.
Next we run a cleaning step that extracts review text and removes unnecessary items such as usernames, UI elements, star ratings, etc. The output of this stage is a separate CSV of clean review text. This gives us a consistent Google Reviews dataset.
Then we run our language analysis to find the phrases customers use when they’re describing outcomes and If we need quick directional language, we run a local phrase analysis that focuses on repeated phrases and patterns.
The outputs are plain and simple. We want the words we can paste into a doc and work from.
- A cleaned review dataset in CSV form
- A basic phrase-frequency report
- A full AI-powered message-mining report
This turns reviews into structured outputs you can use in copy across marketing materials.
Creating useful outputs from message mining
The AI analysis in our workflow extracts four specific categories:
- Emotionally loaded outcome language: These are the raw ingredients for benefit framing and “after” states.
- Nightmare phrases: The stakes and stress before the purchase. These belong in problem framing, urgency, and objection handling.
- Why us triggers: The selection criteria customers cite when they explain why they trusted the business. These belong in reassurance, proof blocks, and “how it works” sections.
- Unconventional observations: Surprises that suggest positioning, not minor tweaks.
Once you have the review language categorised, you can map them to your copy:
- Headlines and subheads: start with Emotionally loaded outcome language, then rewrite for clarity.
- Proof and reassurance: start with Why us triggers, then turn themes into specific proof blocks.
- Objection handling and FAQ: start with Nightmare phrases, then write the answer that neutralises the fear without overpromising.
- Calls to action: use the reader’s desired outcome language (Swiped) and their risk language (Nightmare) to shape what the CTA promises.
This is the key difference between “we looked at reviews” and a repeatable system. The output is structured in a way that tells you where it belongs.
Test this on your own site:
Take one existing landing page and do a simple audit.
- Highlight every claim in the page.
- Tag each claim as Emotional, Nightmare, Why us, or Unconventional.
- Anything you can’t tag is probably internal language.
You don’t need to rewrite the whole page to learn something. You’re looking for a mismatch: where the page’s language is about you, while the reviews are about the customer’s state of mind.
Boundary conditions, ethics, and failure modes
Review mining is not magic, and it’s not a substitute for talking to customers. It’s a way to reduce internal bias by grounding copy decisions in observed language.
The first boundary condition is representativeness. Reviews are a specific slice of customers, written under specific conditions. Treat outputs as directional inputs you test, not as universal truth.
The second boundary condition is category dynamics. The emotional profile of reviews changes with urgency, risk, and the type of problem being solved. Your taxonomy still works, but the distribution across buckets will shift.
The third boundary condition is overfitting. The point is not to copy phrases blindly. The point is to preserve intent while staying accurate, compliant, and aligned with the actual offer.
Q: Is using Google Reviews for copywriting reliable?
A: It’s reliable as a source of customer language patterns when you first make the input usable as text (cleaning) and then force outputs into a deployable taxonomy.
It becomes unreliable when you treat a handful of quotes as representative, or when you copy wording without preserving intent and staying accurate.
The framework in one sentence (and what to do next)
If you can’t describe your message-mining output as Emotional phrases, Nightmare phrases, Why us triggers, and unconventional observations, you probably don’t have robust messaging yet.
Run one batch, then do a blunt comparison: for each section of your current page, ask which bucket it maps to. The gaps show you what’s missing, and the mis-maps show you what’s currently driven by internal language instead of customer language.
Summary passage (place this at the end of this section): Message mining turns messy review exports into structured customer language you can use in copy. Our workflow cleans the input into usable text, then extracts four categories of insights: Swiped phrases (outcomes), Nightmare phrases (fears), Why us triggers (trust factors), and unconventional observations (positioning patterns). The result is a set of copy ingredients you can map directly into headlines, proof blocks, objection handling, and CTAs.