Joonhyeok Ahn's Hidden Lead Source: YouTube Comments

Joonhyeok Ahn, an AI consultant for AI first companies who helps automate 80% of marketing and sales operations and founder of Threadsight, recently posted something that made me stop scrolling: "YouTube comments are my hidden lead source." In just a few lines, he explained that he is not relying on paid ads, cold outreach, or scraping follower lists — he is quietly mining YouTube comments instead.

In his original post, Joonhyeok Ahn put it simply:

"YouTube comments are my hidden lead source. Not paid ads. Not cold outreach. Not scraping follower lists. YouTube comments."

That idea really resonated with me, because it captures a shift that many marketers are still missing: your best leads are already talking in public. They are asking questions, complaining about tools, describing their bottlenecks, and sometimes literally saying, "I need someone to build this for my agency."

What Joonhyeok Ahn has done is turn those scattered signals into a system.

Why YouTube Comments Beat Traditional Lead Gen

Most teams still pour money into the same channels:

Paid ads on Google, Meta, or LinkedIn
Cold outbound sequences
Purchased or scraped lists

These can work, but they share a big weakness: you are interrupting people who may or may not be thinking about the problem you solve.

YouTube is different. When someone is watching a video in your niche, they are already:

Searching for a solution
Comparing tools or approaches
Learning how to fix an urgent problem

And unlike a Google search, they often leave a public trail: a comment that spells out their context, budget, frustrations, and sometimes their exact wish list.

That is what Joonhyeok Ahn is tapping into. Instead of guessing who might care, he listens to the people who are openly telling the world, "This is the problem I have right now."

Inside Joonhyeok Ahn's YouTube Comment Engine

In his post, Joonhyeok briefly outlined the system he built to mine YouTube for leads. Every day it:

Scrapes comments from videos
Extracts LinkedIn, IG, X, and Facebook from each commenter's profile
Uses AI to classify intent
Scores relevance and detail level
Saves everything to a Google Sheet

On the surface, this looks like a technical automation stack. Underneath, it is a very smart way to operationalize something every good salesperson already knows: intent and context matter more than volume.

Let’s break down each part of his workflow and why it matters.

1. Scraping Comments from Niche Videos

The first decision is where to listen. Instead of trying to monitor all of YouTube, you focus on videos in your specific niche:

Tutorials about the problem you solve
Reviews and comparisons of tools in your category
Industry breakdowns and strategy deep dives

These videos attract exactly the type of people you want to talk to: practitioners, founders, operators, and decision-makers who are stuck on something you can help with.

2. Extracting Social Profiles from Commenters

A random YouTube username is hard to do anything with. A LinkedIn profile, an Instagram handle, or a personal website is a different story.

Joonhyeok Ahn’s system looks for clues in each commenter’s profile, description, or linked sites, then extracts:

LinkedIn URLs
Social handles
Websites and company domains

That turns an anonymous comment into a real person with a role, company, and digital footprint you can verify.

3. Using AI to Classify Intent

Not every comment is a lead. Some are jokes. Some are vague compliments. Some are deep, detailed breakdowns of a real business problem.

This is where AI comes in. Instead of manually reading every comment, Joonhyeok uses AI models to classify:

Intent: Are they just reacting, or are they actively looking for help or a solution?
Problem type: What are they struggling with (e.g., automation, ad performance, funnels, content, attribution)?
Urgency: Are they experimenting, planning, or urgently trying to fix something that is breaking revenue?

AI does the first pass. Humans can then review the highest-intent, best-fit leads without drowning in noise.

4. Scoring Relevance and Detail

Next, the system scores each comment based on how relevant and detailed it is:

Does this person look like the right type of buyer?
Do they control a budget or own a process?
Did they explain their situation enough that you can craft a tailored message?

A throwaway comment like "Cool video" gets a low score. A comment such as "We run a 12-person agency and we are stuck trying to automate client onboarding without breaking our CRM" scores very high.

5. Saving Everything to a Google Sheet

Finally, instead of burying insights in a database, Joonhyeok Ahn pushes everything into a simple Google Sheet. That means:

You can filter by intent, niche, or channel
Your team can collaborate without learning new tools
You can plug the sheet into CRMs or outreach tools later

It is a deceptively simple backbone for a sophisticated lead engine.

From Comment to Client: A Real Example

Joonhyeok shared one specific outcome from this system: a YouTube comment that said, "I need someone to build this for my agency."

That single sentence contained everything a smart marketer looks for:

Clear intent: they are not just curious; they need help.
Clear context: they run an agency.
Implied budget: an agency with 12 employees is not a hobby.

His system found that comment, attached it to a person who had a LinkedIn profile, a website, and a company with 12 employees. That lead became a client.

No cold email list.
No ad spend.
No spammy scraping of random followers.

Just one highly qualified person publicly asking for help in a place most marketers ignore.

How to Turn YouTube Comments into a Lead System

You do not need to replicate Joonhyeok Ahn’s setup line by line to benefit from his strategy. You can start small and grow into automation.

Step 1: Identify Your Niche Videos

Make a list of videos where your ideal buyers are likely to comment:

Tutorials on your core problem space
Tool comparisons that mention your category
Strategy breakdowns for your target industry

Start with 10–20 videos and monitor their comment sections.

Step 2: Define What a Good Comment Looks Like

Before bringing in AI, define your own criteria for a high-quality comment:

They mention a specific process, tool, or metric
They describe a frustration or blocker
They signal they run or work inside a business (agency, SaaS, ecom, etc.)

This is exactly what Joonhyeok’s AI classification is doing at scale: separating casual viewers from serious buyers.

Step 3: Layer on Automation and AI

Once you see the potential, you can automate pieces of the workflow:

Use tools like n8n or Zapier to pull new comments from selected videos or channels
Enrich commenters with available profile links or websites
Use an AI model to tag and score comments based on your criteria

This is the essence of what Joonhyeok built: an always-on system that spots intent while you sleep.

Step 4: Reach Out Thoughtfully

The magic is not just in the data; it is in how you approach people.

Because you have context, your outreach can sound like this:

"I saw your comment on that YouTube video about automating agency workflows. You mentioned struggling to build X for your 12-person team. That is exactly what we help agencies with. If you want, I can walk you through how we’ve solved this for similar teams."

Notice what is happening there:

You reference a real comment (public information)
You show you understand their context
You offer help, not a generic pitch

This is why YouTube-comment leads convert so well when handled correctly.

Step 5: Respect Privacy and Platform Rules

It is important to add a note of responsibility here. Just because data is public does not mean you should be careless with it.

If you follow Joonhyeok Ahn’s lead:

Do not spam people with automated DMs
Always personalize and add value
Respect platform terms of service
Be transparent if someone asks how you found them

Long-term, the only sustainable lead-gen systems are the ones that feel like help, not harassment.

"You’re Leaving Money in the Thread"

Joonhyeok closed his post with a line that sums it all up:

"If you’re ignoring YouTube comments, you’re leaving money in the thread."

I think he is right. Comments are one of the most underused data sources in modern marketing. They are raw, unfiltered, and packed with buying signals.

By pairing YouTube comments with automation and AI, you can:

Discover high-intent leads without ad spend
Understand what your market actually cares about
Build warm, contextual outreach that feels natural

You do not need a huge audience or a giant budget. You just need to show up where the right conversations are already happening — and build a system, like Joonhyeok Ahn did, that makes sure you never miss the most important ones.

This blog post expands on a viral LinkedIn post by Joonhyeok Ahn, AI consultant for AI first company | I automate 80% of marketing & sales ops with AI systems | Founder, Threadsight. View the original LinkedIn post →