Many founders, service businesses, and small B2B teams who use social media comments to discover demand eventually fall into the same workflow.
Open a platform. Find relevant videos or posts. Scroll through the comments. Look for phrases that sound like buyer intent. Copy the useful ones into notes or spreadsheets.
I know that workflow well because I have done it myself.
After restarting my journey as an AI founder, I went through a phase of building products quickly. Later I realized the real bottleneck was not whether I could build fast. The harder part was building something people actually needed.
So I stopped pushing out more products and started doing more direct demand research.
At first, my method was simple:
- scan Xiaohongshu comment sections
- read raw user language
- judge which line sounded like a real problem
- copy it into GetNote
- slowly organize the useful parts
That process did have value.
Comment sections and posts often contain the most unfiltered market language you can find. People do not speak in polished positioning statements. They speak in fragments, complaints, questions, and practical details.
But the process was also exhausting.
Some days I would spend hours doing this and only end up with a few signals worth keeping.
Then I had a conversation with a CNC business owner.
I originally thought we would talk about larger AI directions like AI customer support, AI quoting, or AI sales systems.
But the most important detail from that conversation was much smaller and much more practical.
He told me that he regularly goes to popular TikTok videos and scans the comment section to see whether people are asking about:
- material options
- MOQ
- sampling
- suppliers
For a B2B team, those comments are not just engagement. They can be early demand signals, especially when someone is already asking about materials, quantities, samples, suppliers, or quotes.
My first reaction was immediate:
He was doing the same kind of manual, experience-heavy, hard-to-sustain work that I had been doing.If you want to see this problem in a more specific channel context, I also broke down how to find B2B leads on TikTok without turning comment scanning into a messy manual workflow.
That made me realize this was not just a personal habit. It was a repeated workflow that other people were already using because the demand signals were real, but the process itself was too manual.
Why manual comment scanning feels useful at first
Manual comment scanning has one obvious advantage.
It has almost no startup cost.
You do not need a system at the beginning. You do not need complex software. You do not need to define a complete workflow before you start.
You just:
- find relevant videos, posts, or discussions 2. open the comments 3. read through them 4. copy what looks useful
That is a reasonable early-stage method because it helps you get closer to real user language.
In B2B, custom manufacturing, and professional service contexts, demand does not always start as a formal inquiry. It often starts as a comment like:
- "Can you make this in aluminum?"
- "What is your MOQ?"
- "Need supplier for this part."
- "Can you quote 500 pcs?"
These comments do not look like polished leads.
But they are often where real demand first appears.
So the problem is not that manual scanning is wrong.
The problem is that it works better as an observation method than as a sustainable operating workflow.
Where the workflow starts breaking
The reason this process becomes unsustainable is not the existence of the task itself. It is what happens when the task becomes part of daily work.
1. It depends too heavily on patience
For the first ten minutes, you read carefully.
After half an hour, you start skimming.
After an hour, you begin missing things or judging too quickly.
2. Context gets lost easily
You may copy a useful comment, but later forget:
- which video it came from
- which account posted it
- what the surrounding context was
- whether it was curiosity, complaint, or real buying intent
By the next day, the signal is weaker because the context is gone.
3. It is hard to standardize
If you are the one scanning, you may have a rough sense of which comments are worth saving.
But once another person does the same work, the standards change.
Who counts as high intent? Who is just casually asking? Which comment should be saved? Which one should be ignored?
Without structure, the workflow stays personal instead of becoming repeatable.
4. It does not naturally become an asset
If today's useful comments are only copied into scattered notes, tomorrow the process starts from zero again.
The comments may contain signals, but the workflow does not reliably turn those signals into something reusable.
5. It does not connect cleanly to downstream work
Comments are not only useful for lead discovery.
They are also useful for:
- content ideas
- FAQ language
- follow-up messaging
- offer positioning
- product validation
If the raw language is not consistently preserved and organized, those downstream benefits also disappear.
That is why the real issue is not whether there is demand in comment sections.
The real issue is that manual comment scanning is not a sustainable way to run demand discovery over time.
A better question: how do you turn comments into structured demand signals?
Instead of only asking where to find potential clients, a more useful question is:
Can scattered, informal comments be turned into something structured enough to judge and follow up on?
This does not require a massive AI system.
A small workflow can already look like this:
- find relevant videos, posts, or discussions 2. capture the comments already visible on the page 3. preserve the original wording, account, source, and context 4. judge whether the comment looks like casual talk or something closer to sourcing, quoting, sampling, small-batch, or production demand 5. save the result into a filterable, reviewable lead pool
That may sound small.
But it solves a more important early problem:
- who is already expressing demand
- how that demand is being expressed
- which comments deserve follow-up
- which repeated questions should become content or FAQ material
For many early-stage businesses, this matters more than trying to automate the entire sales process.
Why I chose a narrow comment-to-signal workflow instead of a larger system
Based on that CNC conversation and my own demand research workflow, I did not want to start with a full CRM or an all-in-one acquisition system.
That would have been too large and too abstract.
Instead, I chose a narrower question:
Can potential leads inside TikTok comment sections be identified and organized in a more stable way?
That led me to build a Chrome sidebar prototype called LeadRadarforTikTok.
What it currently does is intentionally narrow:
- reads comments already visible on the current page
- preserves the original comment, account, and video source
- identifies expressions that look more like sourcing, quoting, sampling, small-batch, production, or supplier intent
- stores the result in a lightweight local lead pool
- supports basic filtering and CSV export
It also supports button-triggered auto-scroll collection within the current comment DOM range.
That means the user can keep browsing in a normal way, but can also expand visible comments faster on the current page when needed.
In simple terms, the job is:
Turn comments into cleaner, easier-to-review signals.
Why I want real people to try it now
For me, the most important thing is not turning this into a feature-heavy system as fast as possible.
What matters more is whether it can enter a real workflow and actually help someone:
- save time
- miss fewer useful comments
- keep better raw signals
- reduce repetitive manual sorting
What I care about most right now is:
- are you also manually scanning comments today
- which step takes the most time
- which outputs are useful to you and which are not
- what is the smallest part of the workflow you want this tool to improve first
Because a tool like this should not be shaped only by my own assumptions.
It should be shaped by what real users are already doing every day, where the process breaks, and where time gets wasted.
What this article is really trying to say
If you are using TikTok comments, short-form video comments, forums, or public discussions to look for demand signals, the main takeaway is not "go build a plugin immediately."
The more useful takeaways are:
- Comment-section demand signals are real, but they usually appear early, informally, and in scattered language. 2. Manual scanning can work as an observation method, but it is difficult to sustain as a real workflow. 3. If you want to systematize this process, standardizing one small step is often more realistic than trying to automate everything. 4. The value of a small AI tool is often not that it promises to rebuild the whole funnel. The value is that it redesigns one repeated, time-consuming, judgment-heavy task.
For me, LeadRadarforTikTok is exactly that kind of experiment.
At a higher level, this is part of the same acquisition problem:solo service businesses need a client acquisition system that does not depend only on referrals.
CTA
I am now opening early access for LeadRadarforTikTok.
If you already use TikTok, short-form video comments, or public social content on overseas platforms to look for B2B leads, and you feel this tension:
manual comment scanning is exhausting, but there really might be opportunities inside those comments,
you can leave your email and I will send you the tool to try.
If you are not sure whether you want to test it yet, you can still leave your workflow needs and suggestions.
For example:
- which platform you scan most often
- which demand signals you most want to identify
- which step takes the most time
- which manual action you most want this tool to reduce first
I will prioritize future iteration based on real usage feedback.
How Solo Service Businesses Get Clients Without Relying on Referrals