Reviews are an input.
Why this mattersMost owners think reviews are an output — something customers leave behind after a transaction. Reviews are also an input. The richest customer language source your business will ever produce. You already have it. You are sitting on it. You are not using it. This lesson is where we extract it.
Read this once. Sit with it before you answer.
What is sitting in your existing review archive right now that could fuel your next six months of content if you bothered to look at it?
Where you stand right now.
By the end of this lesson, you will be able to:
- 1Pull 10 to 20 of your most recent reviews into a single document.
- 2Run the AI Review Mining Prompt against those reviews inside your Claude Project.
- 3Save the seven structured outputs in your workbook and your Claude Project knowledge files.
- 4Set a 90-day recurring reminder to re-run the prompt with newer reviews.
The whole lesson in a few points.
- 01Reviews are an input, not just an output. Past reviews already contain the language, problems, fears, and outcomes your market uses.
- 02The AI Review Mining Prompt extracts seven structured outputs from 10 to 20 reviews in about 10 minutes.
- 03Save the output in your workbook and as a knowledge file in your Claude Project. Modules 10, 11, and 13 pull from it automatically.
- 04Customer language inside reviews is keyword research already done for you by people who paid you.
- 05Re-run every 90 days. Customer language shifts. New reviews catch new patterns.
What your reviews actually contain.
A typical review has more strategic content per word than any other source you have.
The customer's problem in their words. Before they hired you, they had a leak, a broken cabinet, a backed-up sink, a divorce. They describe it in the language they used when they searched. That language is what you should be using on your website and in your content.
The fear they had before hiring. "I was worried it would take weeks." "I had been quoted twice this much." "I was nervous about letting strangers into the house." These are the objections every other prospect has. You can address them before they are even voiced.
The outcome they remember. Not what you did — what they got. "My kitchen feels new again." "We got back to work the same day." "I finally have peace of mind." This is the language that converts.
The specific service detail. Customers describe the exact service they bought in the words they used to find it. That is your keyword research, done by people who already paid you.
What the AI Review Mining prompt does.
The prompt takes your raw review text and extracts seven outputs.
One. Top 5 problems customers mention having before they hired you.
Two. Top 5 outcomes or results they describe after working with you.
Three. Top 3 fears or hesitations they mention.
Four. The specific words and phrases they use most often.
Five. Any service mentioned repeatedly that you may be under-featuring in your content.
Six. Three social proof post topics pulled from patterns.
Seven. One education post topic that addresses the most common pre-hire concern.
Paste in 10 to 20 reviews. Get seven structured outputs. Ten minutes of work. Output good for the next six months of content.
Prompt 4 — AI Review Mining Prompt
Copy this prompt into your Claude Project. Paste your reviews where the prompt asks for them.
I am going to paste a collection of my Google reviews. I need you to analyze them and deliver the following: 1. The top 5 problems customers mention having BEFORE they hired us, in their exact language. 2. The top 5 outcomes or results they describe AFTER working with us. 3. The top 3 fears or hesitations they mention, even if indirectly. 4. The specific words and phrases they use most often that I should be using in my own content. 5. Any service that gets mentioned repeatedly that I may be under-leveraging in my content. 6. Three social proof post topics directly pulled from patterns in these reviews. 7. One education post topic that would directly address the most common pre-hire concern mentioned across these reviews. HERE ARE MY REVIEWS: [PASTE 10 TO 20 REVIEWS] Deliver each section clearly labeled. Use the customers' actual words wherever possible. Do not paraphrase or sanitize their language. Their exact words are the entire point.
Where the output goes.
Save the prompt output in two places.
First. Paste it into your workbook on the Review Mining Output page.
Second. Save it as a document inside your Claude Project's knowledge files. The rest of the content prompts in Modules 11 and 12 will pull from this output automatically.
Three modules use this output directly.
Module 10. When you build your Core 30 website pages, the customer language from Outputs 1, 2, and 4 goes into your page copy. Not the marketing language you used to write. The customer language.
Module 11. The Social Proof Post Prompt uses Outputs 1, 2, and 6 directly. The Education Post Prompt uses Output 7.
Module 13. The 30-day playbook references this output for content language at every step.
This is one of the highest-impact 10 minute exercises in the course because everything downstream of it gets better.
How often to re-mine.
Run this prompt once now using your current review archive. That output covers the next 90 days of content.
Then set a recurring reminder. Every 90 days, re-run the prompt with your most recent batch of reviews. Customer language shifts over time. Seasonal problems change. Newer reviews catch newer patterns.
Treat it as a quarterly ritual, not a one-time task.
Reviews are an input, not just an output.
Mining them turns past customers into your content strategy. Module 5 is done. Next module locks entity trust through citations.
The vocabulary that follows you.
- AI Review Mining
- The prompt-driven analysis that extracts patterns, language, problems, fears, and outcomes from your existing reviews and converts them into content fuel.
- Customer language source
- Any pool of customer-written content (reviews, inquiries, texts, support tickets) where the words your market actually uses are sitting unedited.
- Under-featured service
- A service that gets mentioned repeatedly across your reviews but is not yet a featured part of your website, profile, or content calendar.
- Pre-hire concern
- The specific hesitation or fear a prospect carries before they decide to hire. Often visible in reviews where the customer describes what made them nervous.