Read the comments,
know what to make.
Here is a quiet machine that tells me exactly what my audience wants, without a single survey or poll. I point it at a set of creators and channels in my niche, and it watches their videos. But the video is never the point. The machine reads the comments underneath. It runs sentiment analysis on each one, then sorts them into three buckets: what people loved, what was missing, and what they suggested next. From that it hands me a content action plan. It tells me what to make, what to avoid, and what nobody else is doing yet. It runs in the background and hands me a report, so I come in one step ahead of everyone else, with ideas straight from the community.
Your next hit is not a guess. It is already sitting in your rivals’ comments.
Most content is a gamble. You pick a topic, you make it, and you hope the audience cares. Surveys barely help, because few people answer and fewer answer honestly. But the truth is already public. Under every popular video in your niche, real people say what they loved, what was missing, and what they wish someone would make next. So I built a machine that reads those comments for me. It watches chosen creators, reads the comments under their best videos, runs sentiment analysis, and sorts every one into loved, missing, and suggested. Then it hands me a content plan. 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 makes content and is tired of guessing what to make next. Creators, marketers, course sellers, anyone with rivals whose comment sections are full of honest feedback. It works even if you are small, because the audience you want is already commenting on someone else’s videos, telling you exactly what they wish existed.
What goes wrong
Without it, you make content from your own head and hope it lands. Surveys get ignored, polls get gamed, and the honest feedback stays buried in comment sections you never read. You are not short on ideas. You are short on a way to hear what your audience already tells other people they want.
How the machine works
You pick the channels. The machine watches their videos, keeps the ones that cleared a view bar, and reads the comments. It scores each comment for sentiment and sorts it into loved, missing, or suggested. You read nobody’s comments by hand. The machine gathers them, sorts them, and hands you a plan.
What you get back
A content action plan built from what the audience already asked for. It tells you what to make, what to avoid, and the gap nobody is filling. Better-aimed content earns a few more conversions per piece, and as your library grows, those small edges compound into real numbers.
I did not want to run another survey. I wanted the truth people already say out loud.
This automation is part of the business intelligence work I run, and it lets me gather a lot of real audience data without running a single survey or poll. The idea is simple. If I want to know what my audience wants, I do not need to ask them. I just need to listen to what they already say in public, under the videos they watch. So I configured a machine to do exactly that, at a scale I could never manage by hand.
The machine watches a set of creators and channels in my niche. It looks at their videos, but only the ones that clear a view bar I set, because a video that landed is a video whose comments are worth reading. Then comes the part that matters. The machine is not interested in the video itself. It is interested in the comments and the way people react to it, because that is where the honest feedback lives.
So it reads the comments and runs sentiment analysis on each one, to work out whether it is positive or negative. Then it classifies each comment by what the person is actually saying. I was looking at three things. First, what people absolutely loved about the video. Second, what was missing from it. Third, the suggestions people made, the ones saying it would be great if you did a video on this.
From all of that, the machine comes back with a content action plan. It tells me what to make, what to avoid, and what nobody else is doing that I should be doing, because that is the gap the audience is asking someone to fill. The most useful signal in any market is what the audience wants, and here it is, gathered and sorted, without me watching a single video or reading a single comment myself.
The numbers here are kept deliberately modest. Content built from what the audience already asked for simply converts a little better than content pulled from my own head. Say each well-aimed piece earns a handful more conversions than a guess would. That edge is small on one video, but I publish many, and the library keeps working for years. A small edge on every piece, compounding across a growing library, quietly turns into real revenue.
Proof point: I have documented how I run my business on autopilot and the income streams behind it on YouTube, in my 28 income streams breakdown and a real look at the daily work in a day in my life, so the conservative numbers on this page are checkable.
Three buckets that turn public comments into a content plan
What made this work was treating a comment section as market research, not noise. Most people scroll past comments, or read a handful and forget them. But under every popular video, the audience is telling you what they loved, what was missing, and what they want next. The loop here reads those comments, scores them for sentiment, and sorts every one into three buckets. From those buckets a plan writes itself: make what they loved and asked for, avoid what fell flat, and fill the gap nobody else is filling. That is how content stops being a guess.
Loved: keep making what already works
The first bucket is the praise. The machine reads every positive comment under your rivals’ best videos and gathers the ones that say what people loved, the exact parts that landed. You are not guessing which angle resonates. The audience said it to someone else, in plain words, and the machine collected it for you. When you see the same thing praised again and again across many videos, you know it is a safe bet, not a hunch. So you keep making more of what the audience has already shown it wants, and you skip the slow, costly trial and error of finding that out yourself.
Missing: fix what the audience wished for
The second bucket is the gap inside a video that did well. The machine reads the comments that say what was missing, the moments where people wanted more, or felt something was skipped. This is gold, because the video already worked, and the audience is telling you how to make the next one even better. You do not have to wonder where your rival fell short. Their own viewers wrote it down. So you make the version that includes what they wished for, and it lands harder, because you already know the exact thing the audience felt was left out.
Suggested: make what nobody has made yet
The third bucket is the request. The machine reads the comments that say it would be great if you did a video on this, the direct suggestions for content that does not exist yet. This is where you get one step ahead. The audience is naming the video they want, and often nobody has made it. So you make it first. You are not copying a rival or reacting to a trend. You are creating the exact thing the community asked for, before anyone else notices the demand. That is content built from the audience up, and it is why the outreach lands so much better.
Once those three buckets are in place, guessing stops. The machine reads the comments, scores them for sentiment, and sorts every one into loved, missing, or suggested. You keep making what already works, you fix the gaps your rivals left open, and you make the videos the audience asked for before anyone else does. You get a content plan built from a machine that ran while you slept, and every well-aimed piece adds a little that compounds.
Before the system
- Making content from my own head and hoping it landed
- Surveys and polls that few people answered honestly
- Honest feedback buried in comment sections I never read
- Guessing at topics while my rivals guessed at theirs too
- Good ideas arriving late, after someone else made them first
After the system
- Real audience data gathered without asking a single person
- Every comment scored for sentiment and sorted into three buckets
- A clear list of what to make and what to avoid
- The gap nobody is filling, handed to me before rivals notice
- A content plan built from what the community already wants
Prompt 1: pick the creators and channels worth watching
Before you read a single comment, you need the right channels. The biggest mistake is watching creators whose audience is not yours. Use this prompt to name the exact creators and channels whose comment sections hold the feedback you actually need.
Channel selection planner
Act as an audience research strategist. I want to gather what my audience wants by reading the comments under videos from creators in my niche, without running surveys or polls. About my niche: [describe your topic, who your audience is, and what you make or sell]. Pin down the targets: which creators and channels share my exact audience, how to tell a channel worth watching from one that is off, which nearby niches to include or avoid, and what view level makes a video worth reading the comments on. For each choice, one line on why it matters.
The output is your watch list, the exact channels whose comments hold your audience’s honest feedback. Get this right and every comment the machine reads comes from a person you actually want to reach.
Prompt 2: set the rules for reading sentiment in a comment
A comment is only useful once you know how the person feels. The system works because each comment is scored for sentiment before it is sorted. Use this prompt to set clear rules for telling a positive comment from a negative one.
Sentiment scoring guide
Act as a sentiment analysis advisor. I read comments under videos in my niche and I want each one scored for how the person feels, so I can trust the sorting. About my content: [describe the kind of videos and the tone of the audience in your niche]. Set the rules: how to tell a positive comment from a negative one, how to handle sarcasm and mixed feelings, what to do with off-topic or spam comments, and how to keep the scoring steady across thousands of comments. For each rule, one line on why it keeps the read honest.
The output is the scoring guide that keeps your read trustworthy. Settle it once and every comment the machine sorts reflects how the person actually felt.
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 intelligence automations like this?
I teach the same mechanics that power this business intelligence machine inside Automations Made Easy. 1,000+ students have used these mechanics to save two hours a day and build little machines that quietly handle the boring work. No coding required.
Week one: your channels chosen, your first comments sorted.
Most people put off building a research machine because it sounds technical, so they keep guessing at content forever. That is exactly why this machine matters. An intelligence machine is meant to start simple and run quietly, not arrive perfect. In week one you pick your channels, let the machine read the comments under their best videos, sort them into three buckets, and see a real content plan take shape, so you know it works before you scale it.
Week one looks like this. On Monday you pick the creators and channels in your niche and set the view bar for which videos count. Then you let the machine read the comments under those videos and score each one for sentiment. By midweek you set the rules that sort every comment into loved, missing, and suggested. By the end of the week you have a first content plan, built from real audience feedback, with the machine still reading new videos and comments on its own.
The point of the first week is not the number of comments read. The point is to confirm the machine watches the right channels, reads the comments honestly, and sorts them into the three buckets that matter. Once that is locked, every new video from your chosen creators gets read, scored, and sorted on its own, and your content plan keeps updating itself while you do nothing at all.
From there the maths is simple and conservative. Content built from what the audience already asked for converts a little better than a guess, so each well-aimed piece earns a handful more conversions. That edge is small on one video, but you publish many, and the library keeps working for years. Over three to five years, those small edges stack across a growing library and quietly compound into real revenue, all from feedback you never had to ask for.
All of this runs while you work, sleep, or make your next video. The machine watches the channels, reads the comments, scores the sentiment, sorts the buckets, and hands you the plan. No more surveys nobody answers, no more guessing at topics, no more good ideas arriving late. Your content plan fills on its own, built from the exact things your audience already tells other people they want.
Prompt 3: sort every comment into loved, missing, or suggested
Sentiment alone is not enough. The plan only writes itself once each comment is sorted by what the person is asking for. Use this prompt to define the three buckets clearly, so nothing useful gets lost.
Three-bucket classifier
Act as a content research analyst. I have thousands of scored comments from videos in my niche, and I want each one sorted into what people loved, what was missing, and what they suggested. About my goal: [describe what you want to learn, for example which topics to make more of]. Define the buckets: what counts as loved, what counts as missing, what counts as a suggestion, how to handle a comment that fits two buckets, and how to spot the same request repeating across many videos. For each bucket, one line on why it feeds the plan.
The output is a clean split of every comment into the three buckets. Run this once and the raw feedback becomes an ordered picture of exactly what your audience wants.
Prompt 4: turn the buckets into a content action plan
The buckets are the research. The plan is what you act on. A good plan tells you what to make, what to avoid, and where the open gap is. Use this prompt to turn the sorted comments into a clear, ready-to-shoot content plan.
Content action plan writer
Act as a content strategist. I have my audience's comments sorted into loved, missing, and suggested, and I want a clear plan for what to make next. About my channel: [describe what you publish, how often, and the result you want from your content]. Write the plan: which topics to make more of based on what they loved, what to avoid based on what fell flat, and the videos to make first based on what they asked for that nobody has made yet. Rank the ideas by demand, and give each a one-line reason drawn from the comments.
The output is a content plan built from what your audience already asked for. Get it right and you come in one step ahead, making the exact videos the community wants before your rivals notice.
The exact build, step by step
Pick the creators and channels to watch
Start where your audience already talks. Your rivals in the niche have comment sections full of honest feedback, so you name the creators and channels the machine should watch. This single choice sets the whole thing up, because the machine only reads comments from people who share your audience. This is the step most people skip, and it is why their research misses. When you pick channels whose viewers are the people you want, every comment the machine gathers comes from someone who could genuinely become your audience or your customer.
Keep only the videos that cleared a view bar
Instead of reading every video ever posted, the machine is picky. It looks at the videos from your chosen channels and keeps only the ones that cleared a view level you set. A video that got real views is a video whose comments are worth reading, because it landed with the audience. The quiet flops are skipped, so you are never wading through comment sections nobody visited. This happens in the background, on its own, and keeps catching new hits as they post. By the time you look, only the videos that actually mattered have been queued for reading.
Read the comments, not the video
Here is the move that changes everything. The machine is not interested in the video itself. It reads the comments underneath, because that is where the honest feedback lives. People say what they loved, what annoyed them, and what they wish existed, all in their own words, with nobody prompting them. You do not scroll through hundreds of comments by hand. The machine gathers them for you, the same careful way on every video, so no useful signal gets missed. What you end up with is not a pile of videos but a pile of real reactions from the exact audience you want.
Score the sentiment and sort into three buckets
Now the machine makes sense of the pile. It runs sentiment analysis on each comment to work out whether it is positive or negative, then classifies what the person is actually saying. Every comment lands in one of three buckets: what they loved, what was missing, or what they suggested. This is the quiet step that turns noise into a picture. A single comment is just an opinion, but when the machine sorts thousands of them, the patterns jump out. By the time it is done, you can see at a glance what the whole audience feels, without reading a word yourself.
Turn the buckets into a content action plan
Now the buckets become a plan. The loved pile tells you what to make more of, because the audience already showed it works. The missing pile tells you how to make the next one better, by including what people wished for. The suggested pile hands you the videos nobody has made yet, the exact ones the audience asked for. The machine pulls it all together into a clear action plan: make this, avoid that, and fill the gap nobody is filling. There is nothing for you to guess. The plan is built entirely from what your audience already said out loud.
Read the plan and come in one step ahead
Final piece. The machine hands you a report, a content plan built straight from the community, not from your own head. You come in knowing what to make, what to avoid, and the gap your rivals left open. These are not hunches. They are the exact things your audience asked for, gathered while you slept. Over the following weeks you make those videos, and because the demand was already there, they land harder and convert a little better than a guess would. This is the part that compounds. Every well-aimed piece adds a small edge, and across a growing library, those edges stack into real numbers.
Pick the channels
You name the creators in your niche whose comment sections hold your audience’s honest feedback.
Read the comments
The machine keeps the videos that cleared a view bar and reads the comments under them.
Score and sort
It runs sentiment analysis and sorts every comment into loved, missing, or suggested.
Get the plan
The buckets become a content plan: what to make, what to avoid, and the gap nobody fills.
Build this intelligence engine inside the same playbook 1,000+ students use
Automations Made Easy teaches the mechanics behind intelligence machines like this one. Step by step, no code, plain English. Save two hours a day and own little machines that tell you exactly what your audience wants, so you spend your time making the content instead of guessing at it.
The six months after I switched it on
Here is the shape of the first six months after I turned the business intelligence machine on. The line tracks the extra value from content built on real audience feedback, the small edge each well-aimed piece earns over a guess, which is exactly how this machine pays off in practice.
Monthly value from audience-led content
Three things matter on this chart. The value climbs steadily as more audience-led pieces publish and start working, not in a spike. The gains come from content built on feedback the machine gathered for free, without you running a single survey. And every one of those months happens while the machine watches the channels and sorts the comments for you.
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 stopped guessing at topics. The machine showed me the same question asked under a dozen rival videos, so I made that video first. It is now my best performer by a wide margin.”
“The missing bucket was the gift. My rivals’ viewers kept writing what those videos left out, so I made the complete version. The comments on mine now say it is the one they were waiting for.”
“I used to read comment sections for hours and forget everything. Now the machine reads them, sorts them, and hands me a plan while I sleep. I just pick the top three and shoot them.”
“Knowing week one was just picking my channels and reading the first comments kept it easy. By month two the plan was updating itself and I had a backlog of ideas the audience had already asked for.”
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, write, and follow up 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 business intelligence machine: common questions
Pulled from what readers and Automations Made Easy students ask most.
Do I need to be a developer to set this up?
No. You pick the creators and channels in your niche and the kind of feedback you care about, then let the machine read the comments and sort them for you. The skills you need are knowing your own niche and turning a plan into content, which is exactly what Automations Made Easy teaches. The reading and sorting is handled for you.
Why read the comments instead of the videos themselves?
Because the video is one creator’s guess, but the comments are the audience answering back. That is where people say what they loved, what was missing, and what they wish existed, all in their own words. The most useful signal in any market is what the audience wants, and the comment section is where they say it out loud, for free.
How does the sentiment analysis actually work?
The machine reads each comment and works out whether it is positive or negative, then classifies what the person is saying. I keep the method light here on purpose, but the result is what matters: every comment ends up scored and sorted into loved, missing, or suggested, so the patterns across thousands of them become clear at a glance.
How is this better than running a survey or a poll?
Surveys get few answers, and the answers are often what people think they should say. Comments are unprompted and honest, written the moment someone reacts to a real video. You also get far more of them, from the exact audience you want, without asking anyone anything. The numbers on this page stay modest, but audience-led content simply converts a little better than a guess.
Does watching my rivals’ comments stay fair and above board?
Yes. These comments are public, written openly under videos anyone can watch. The machine reads what people already chose to say in the open, the same way you might if you had endless time. You are not spying on anything private. You are listening to what the audience is already telling the whole world it wants, and then making it for them.
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.
Build this business intelligence machine yourself, or learn the mechanics inside Automations Made Easy.
If you want to learn the mechanics behind intelligence 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 pick your channels, read the comments, and turn the feedback into a content plan that wins, I take a small number of consulting clients each month.
€497 one-time · Lifetime access · 1,000+ students
