The market already told me
what to build next.
I point a bot at competing courses and products. It reads their reviews, sorts the praise, the complaints, and the missing pieces, and hands me a build plan. I build what the public already wants, from data, not a hunch.
I used to build what I wanted. Now I build what they ask for.
Before I make a course, a service, or a product, I want to know it will land. So I point a bot at the products already out there in that space. It reads their reviews, ignores the noise, and sorts the rest into three piles: what people loved, what people disliked, and what people wished was there. Then it hands me a plan. I keep the loved parts, drop the disliked ones, and fill the gaps. Here is who it is for, what goes wrong without it, how it works, and what you get back.
Who it’s for
Anyone who creates and sells something people review. Course creators, authors, coaches, software makers, and anyone planning a paid product. Especially useful if you have ever built something you loved and watched it not sell. If you are about to make a product, this tells you what the market already wants before you build a single thing.
What goes wrong
Most people build from a hunch. You make the course you would want, ship it, and hope the market agrees. Often it does not, because you built for yourself, not the buyer. The reviews of rival products held the answer the whole time, but reading hundreds of them by hand is a job nobody finishes. Building blind is why good products quietly miss what buyers were asking for out loud.
How the machine works
You give the bot a handful of competing products on your topic. It reads their reviews, throwing out the noise about shipping and refunds. It pulls out three lists: the praised parts to keep, the complained-about parts to drop, and the missing parts to add. Then it writes a build plan from those lists, turned into modules and lessons. Rival reviews in, a plan for a product people want out.
What you get back
A product built on what buyers actually said, not what you guessed. It carries the loved features, skips the hated ones, and fills the gaps rivals left open. That raises perceived value, the stick rate, and the conversion rate, quietly and on every launch. You stop building blind and start building the thing the market already asked for.
For years I built the product I wanted, not the one they wanted.
For a long time I built things the way most creators do. I would pick a topic I knew well, design the course or the product I personally thought was best, record it, and put it out. Sometimes it worked. Often it did not, and I could never quite say why. The truth was simple and a little uncomfortable. I was building what I wanted, and my taste is just one person’s taste.
Meanwhile the answer was sitting in plain sight. Every competing product on my topic already had buyers, and those buyers had left reviews. They had written down, in their own words, exactly what they loved, what let them down, and what they wished had been there. That is gold. But reading hundreds of reviews across several products, by hand, sorting them into what matters and what is noise, is a job nobody actually finishes. So I never did it properly, and I kept building on a hunch.
So I built a bot that does the reading for me. I give it a handful of competing courses, books, or products on the topic I am about to build. It goes out and gathers the feedback for each one. It is trained to ignore some reviews and focus on others, so the noise about postage or a grumpy one-star rant gets thrown out. What it keeps is feedback about the actual content and value.
From there it sorts everything into three clean piles. First, what people particularly liked, so I make sure those things are in my product. Second, what people disliked, so I make sure those things stay out of mine. Third, the gaps, the things people wished were there but were not, so I can fill them. By the time the bot finishes, I have all that data compiled from the field, and I can build something the public actually wants instead of something I want.
The numbers here are modest by design and honest. I am not promising a product that sells ten times more. I am promising a product that matches what buyers already asked for, which converts a little better, refunds a little less, and holds attention a little longer on every launch. That edge is permanent, because I never build blind again. Proof point: I document my income streams and how I build them openly on YouTube, in my 28 Income Streams breakdown and my road to 10 million video, so the shape of how I work is checkable, not a guess.
Three moves that turn rival reviews into a product people already want
What made this work was treating every review of a competing product as a free instruction. Buyers had already said what they wanted, in writing. The framework hangs on three moves: read every review without the noise, sort the signal into loved, disliked, and missing, then turn those three piles into a build plan I can record from.
Read every rival review, drop the noise
I hand the bot a few competing products on my topic and it gathers the feedback for each. The first job is filtering. A review about late shipping, a refund argument, or a one-star tantrum that says nothing about the content all get thrown out. The bot is trained to focus only on feedback about the actual value, what taught people something, what felt thin, what they wished went deeper. That filtering is what makes the rest of the report trustworthy. I am reading signal about quality, not a pile of complaints I cannot use.
Sort the signal into loved, disliked, and missing
The kept feedback gets sorted into three piles. The loved pile is the things people praised over and over, so I know exactly what to keep in my own product. The disliked pile is the things people complained about, so I know what to leave out. The gaps pile is the things people wished were there but were not, the openings rival products left wide open. These three lists are the whole point. They turn a vague topic into a precise picture of what the market wants and what it is tired of.
Turn the three lists into a build plan
Three lists are not a product yet. The last move turns them into a plan I can act on. The bot takes loved, disliked, and missing, and drafts a curriculum: the modules, the lessons, and the order, each one tied back to a real piece of feedback. I open the plan and I already know what to build and why every part is there. Then I record it. The result is a product shaped by buyers, so it brings them the value they asked for, which is what raises the conversion rate at the end.
Once those three moves are in place, every product I build starts from what the market said instead of what I assumed. Read without noise, sort into three piles, build from the plan, and ship something people already want.
Before the gap finder
- I built the product I personally wanted and hoped buyers agreed
- Rival reviews held the answer, but reading them all was never finished
- Good features got left out because I never knew people loved them
- Hated features crept in because nobody told me they were a problem
- Obvious gaps stayed open, the exact things buyers wished for
After the gap finder
- The bot reads every rival review and drops the noise for me
- I get three clean lists: loved, disliked, and the gaps to fill
- My product keeps what people praised and skips what they hated
- The gaps rivals left open become my strongest selling points
- Higher perceived value and stick rate on every launch, from data
Prompt 1: gather and clean the reviews of a rival product
The first job is turning a competing product into clean, usable feedback. Use this prompt to have the bot read its reviews and throw out everything that is noise rather than signal about the content.
Review gatherer and filter
You are a market research assistant. I will give you the name of a competing product, course, or book on a topic I am about to build for. Read its public reviews and feedback. Competing product: [paste the product name and where its reviews live]. Do this. First, gather the reviews and feedback you can find for this product. Second, ignore reviews that are not about the content or value: complaints about shipping, refunds, support tone, price alone, or one-star ratings with no useful reason. Third, keep only feedback about the actual product: what taught them something, what was thin, what was missing, what they wished went deeper. Return the kept feedback as short, plain bullet points, each one a single clear point a buyer made. Do not summarise yet, just give me the clean list of real, content-level feedback with the noise removed.
This is the reading I never finished by hand. The noise filter is what makes everything after it trustworthy, because I am only ever working with feedback about the thing itself.
Prompt 2: sort the feedback into loved, disliked, and missing
Clean feedback is only useful once it is sorted. Use this prompt to split the kept reviews into the three piles that tell me what to keep, what to drop, and what to add.
Three-pile sorter
You are a market research assistant. I will give you a clean list of content-level feedback about a competing product. Sort it into exactly three lists. Feedback list: [paste the output from Prompt 1]. Return three headed lists. LOVED: the things people praised, the parts they valued most, so I know what to keep in my own product. DISLIKED: the things people complained about in the content itself, so I know what to leave out. MISSING: the things people wished were there but were not, the gaps I can fill. Under each item, note how often it came up, for example mentioned by many, or mentioned once. Order each list with the most common points first. If something does not clearly fit one pile, leave it out rather than forcing it. Keep every point short and in plain words.
These three lists are the heart of the whole thing. Loved tells me what to keep, disliked tells me what to drop, and missing is the gap that becomes my strongest selling point.
The 3-minute overview of how this works
Before the build steps, watch this short overview. It’s the exact video from the Automations Made Easy page, and it walks through the mechanics behind machines like this one. 1,000+ students have used these mechanics to save two hours a day, with zero coding.
Want to learn the mechanics behind automations like this?
I teach the same mechanics that make this gap finder work inside Automations Made Easy. 1,000+ students have used these mechanics to save two hours a day and build products the market already wants. No coding required.
Week one: one topic in, a build plan the market already wrote.
Most people expect a system like this to feel dramatic on day one. It does not, and that is the point. There is no flood of sales in the first week, because nothing has been built or sold yet. The first week is about proving the loop works. You pick a topic, hand the bot a few rival products, and a clean three-pile report comes back. That quiet report is the whole thing working.
Week one looks like this. You are about to build a course on a subject. Instead of opening a blank page and writing what you think should be in it, you list five competing courses and books. You point the bot at them. It reads their reviews, drops the noise, and hands you back three lists: what their buyers loved, what they hated, and what they wished was there. You read it in ten minutes and suddenly you know exactly what your course should contain.
The point of the first week is not a sales number. The point is to prove the loop closes: you hand over a few rival products, the bot reads their reviews and drops the noise, and a build plan comes back that is shaped by real buyers instead of your own guess about what they want.
From there the maths is simple and quiet. A product built from review data tends to convert a little better and refund a little less, because it matches what buyers already wanted. That is not a spike, it is an edge. And the edge applies to every product you build this way, not just one. A few points of extra conversion on launch after launch, compounding across everything you make, is real money over a year.
All of this runs while you focus on the part only you can do, which is building and recording. The bot does the reading and the sorting. You make sound decisions based on actual feedback from the field, not a hunch and not what you personally wanted to make.
Prompt 3: turn the three piles into a build plan
Three lists are not a product. Use this prompt to turn loved, disliked, and missing into a curriculum I can actually record, with every part tied to a real piece of feedback.
Build plan writer
You are helping me design a product the market already wants. I will give you three lists from rival product reviews: LOVED, DISLIKED, and MISSING. Three lists: [paste the output from Prompt 2]. Product I am building: [course, book, service, etc.] on [topic]. Draft a build plan. Start with a short outline of modules or sections in a sensible order. For each module, list the lessons or parts it contains. Beside each part, note why it is there in a few words, tied to the lists: keep because people loved it, added because people said it was missing, or avoided because people disliked it. Make sure every LOVED point is kept, every MISSING point is filled, and nothing from DISLIKED is included. End with a one-line note on what makes this product different from the rivals, based on the gaps it fills.
This is the plan I record from. The reason-beside-each-part rule keeps me honest, so every lesson exists because a real buyer asked for it, not because I felt like adding it.
Prompt 4: sharpen the offer around the gaps you fill
The gaps are your strongest selling points, so they belong on the sales page too. Use this prompt to turn the missing list into plain-language promises that raise perceived value before anyone buys.
Offer angle from the gaps
You are helping me write the selling points for a product I built from rival review data. I will give you the MISSING list, the gaps that competing products left open and my product now fills. Missing list: [paste the MISSING items from Prompt 2]. Product: [name and topic]. For each gap, write one short, plain-English selling point that says what my product gives them that the others did not. Avoid hype words. Just state the thing they wished for and confirm my product has it. Then write a two-line summary I can put near the top of the sales page that makes clear this product was shaped by what buyers actually asked for. Keep every line readable at a glance and free of jargon.
This is what turns research into perceived value. When a buyer reads the page and sees the exact things rival reviews said were missing, the product already feels made for them.
The exact build, step by step
Pick the rival products to read
The whole thing starts with a good list of competitors. Before you build anything, gather a handful of products on the same topic: courses, books, paid communities, or software that buyers already review. Five is plenty to start. These are the products whose reviews hold the answers you need. Choose ones with real, public feedback, because the more honest reviews a product has, the more the bot can learn from it. Keep the list focused on your exact topic, not the whole category.
Have the bot read reviews and drop the noise
Feed the products to the bot one at a time using the review gatherer and filter prompt. It reads the feedback for each and throws out everything that is not about the content: shipping gripes, refund fights, support tone, and empty one-star ratings. What it keeps is feedback about the value itself. This filtering step is the one most people skip, and it is the one that matters most. A clean list of real, content-level feedback is worth far more than a thousand raw reviews you would never finish reading.
Sort the signal into loved, disliked, and missing
Now take the clean feedback and run the three-pile sorter prompt. The bot splits every point into one of three lists. Loved is what people praised, so you keep it. Disliked is what people complained about in the content, so you drop it. Missing is what people wished was there, the gaps you will fill. The bot also notes how often each point came up, so you can tell a one-off grumble from a pattern. These three lists are the moment the research becomes a decision.
Turn the three lists into a build plan
Now make it a product. Run the build plan writer prompt over the three lists. The bot drafts a curriculum: the modules, the lessons, and the order, with a short reason beside each part tied back to the feedback. Kept because people loved it, added because people said it was missing, never included because people disliked it. By the end you are holding a complete outline where every single piece exists because a real buyer asked for it. That is what you record from, instead of a blank page.
Build the offer around the gaps you fill
The gaps are not just for the product, they are your best selling points. Run the offer angle from the gaps prompt over the missing list. It turns each gap into a plain-language promise: the thing buyers wished for, confirmed as something your product gives them. Put these near the top of your sales page. When a reader sees the exact things rival reviews complained were missing, the product feels made for them before they buy. That is how this research raises perceived value, not just product quality.
Keep a running file for every product you build
The last step quietly saves every analysis to a simple file. Topic, the rivals you read, the loved list, the disliked list, the gaps, and the final plan. This running file is worth more than it looks. It stops you redoing the same research, it shows which gaps you have already filled, and over time it becomes a record of what your market wants across every product you make. When you plan the next one, you start from data you already gathered, not a blank page and a hunch.
Rivals chosen
You pick a handful of competing products on your topic whose buyers already left honest reviews.
Bot reads and sorts
The bot reads every review, drops the noise, and splits the signal into loved, disliked, and missing.
Plan drafted
The three lists become a build plan, with each part tied to a real piece of buyer feedback.
You build and ship
You record a product the market already wanted, the file grows, and the next one starts from data.
Build this gap finder inside the same playbook 1,000+ students use
Automations Made Easy teaches the mechanics behind bots like this one. Step by step, no code, plain English. Build products the market already wants instead of guessing, and let the reading happen for you.
The six months after I switched it on
Here is the shape of the first six months after I started building every product from review data. The line ramps up modestly, because each product takes time to build and launch, then the steadier conversions on each one begin to stack.
Extra monthly revenue from products built on review data, after switching on
Three things matter on this chart. The line keeps climbing because each product built this way adds to the last instead of replacing it. The edge holds, because a product shaped by buyers keeps converting after launch. And every dollar of it came from feedback rivals left lying in the open, which I would have walked past if reading it all was still a chore.
What other students built
I teach the simple skills behind machines like this in Automations Made Easy. Students who built their own version sent back what changed in their first month.
“I always built the course I wanted. The bot showed me three things buyers kept asking competitors for and never got. I put all three in. It sold better than anything I have made.”
“The disliked list saved me. I was about to pad my product with the exact filler rival buyers complained about. The bot caught it before I recorded a single lesson.”
“I run this before every product now. I hand it five competitors, it reads their reviews, and I get a plan in ten minutes. I have not started from a blank page since.”
“What surprised me was the perceived value. I put the gaps I filled on the sales page word for word, and buyers said it felt like I had read their minds. I just read the reviews.”
What’s inside Automations Made Easy
AME isn’t a library of pre-built automations. Every business is slightly different. What’s reusable across all of them is the underlying mechanics: how to set up little machines that listen, research, and report while you sleep, and how to wire the pieces together without writing code.
The program walks you through six modules: The Right Tools (the cost-effective, no-code stack I actually use), Task Selection Mastery (which automations are worth building first), Design Secrets (mapping an automation before you build it), Zero to Hero (complete beginner to confident automator), Real-World Application (we build a full automation together, end to end), and Monetization Mastery (turn the skill into a side-business).
It also includes done-for-you templates you import in two clicks, over-the-shoulder training videos, and the same playbook 1,000+ students have used to save two hours a day. No coding required. If you can copy and paste, you can build this.
The gap finder: common questions
Pulled from what readers and Automations Made Easy students ask most.
What does the bot actually hand me at the end?
It hands me three clean lists pulled from real reviews of competing products. A loved list of the things people praised again and again, so I know what to keep in. A disliked list of the things people complained about, so I know what to drop. And a gaps list of the things people wished were there but were not, so I know what to add. On top of that it writes a short build plan that turns those three lists into modules and lessons. I open the report and I already know what to build, in what order, and why. No guessing about what the market wants, because the market already told me in its own reviews.
Why is building from reviews better than building from a hunch?
Because a hunch is just my taste, and my taste is one person. Reviews are hundreds of buyers telling you, in plain words, what they paid for and what let them down. When I build from a hunch I build what I want. When I build from reviews I build what they want, and they are the ones who pay. The bot turns scattered complaints and praise into a clear map. I follow the map instead of my mood. That one change is the difference between a product that sells and a product I personally like.
How does the bot know which feedback to ignore?
I train it to throw out the noise. Reviews about shipping, refunds, a rude support reply, or a one-star tantrum that says nothing useful all get ignored. What it keeps is feedback about the actual content and value: what taught them something, what was missing, what felt thin, what they wished went deeper. So the three lists it hands me are about the product itself, not the postage. That filtering is what makes the report trustworthy. I am reading signal about quality, not a pile of complaints about things I do not control.
Does this only work for courses?
No. It works for anything people review. A course, a book, a paid community, a piece of software, a coaching program, a physical product. Anywhere buyers leave feedback, the bot can read it and sort it into loved, disliked, and missing. The job is always the same: find what the market praised, what it complained about, and what it wished for, then build the thing that fixes all three. I have used it before a course and before a service. The mechanics do not change, only the product I am about to build does.
How does this raise perceived value and conversions?
Because the product ends up matching what buyers already said they wanted. When someone reads my outline and it includes the exact things rival reviews said were missing, it feels made for them. That raises perceived value before they even buy. After they buy, the loved parts keep them happy and the filled gaps keep them watching, which lifts the stick rate. Higher perceived value and a higher stick rate both push the conversion rate up. None of it is a trick. It is just building the thing people asked for, which sells better than the thing I guessed at.
How much does this realistically change my results?
The honest framing is steadier, not explosive. A product built from review data tends to convert a little better and refund a little less, because it matches what buyers wanted in the first place. Say a few points of extra conversion and a few points fewer refunds on each launch. That compounds quietly across every product I build this way. It is not a magic spike, it is a permanent edge from never building blind again. I document my income streams openly on YouTube so the shape of this is checkable, not a guess.
More Money Makers like this one
Built from the same handful of mechanics. Each one captures, converts, or earns money that would have walked out the door.
Run this gap finder yourself, or learn the mechanics inside Automations Made Easy.
If you want to learn the mechanics behind automations like this and build your own at home, Automations Made Easy is the playbook. Step by step, no code, plain English. If you want to talk through how to wire this to your own products and topics first, I take a small number of consulting clients each month.
€497 one-time · Lifetime access · 1,000+ students
