I turned my Reddit pain point analysis into a full service. Here's what I built and why.
A few weeks ago I posted about mapping ETF investor pain points from Reddit data. That post got some interesting responses — people asking if I could do the same for their niche.
So I built it out properly.
airiya.org is now a full end-to-end service:
→ Pain point research reports (40+ sources, priority matrix, verbatim user quotes)
→ Free tool development targeting the #1 pain point
→ High-converting landing page built from real user language
→ SEO/GEO monitoring
The first complete case study is live — ETF investing, from research to tool to landing page. Including a free ETF Overlap Checker that surfaces the #4 pain point (portfolio overlap) interactively.
Still early. No paying customers yet. But the pipeline is real.
Two questions for the IH community:
If you're validating a product idea right now, what's your biggest research bottleneck?
Would you pay for a pain point research report before building? Or only after?
Brutal honesty appreciated.
Curious — how are you handling support/customer messages on that?
I’m realizing it becomes messy way faster than expected, even at low scale.
Still trying to find something simple that doesn’t feel overkill.
This is really insightful — especially the idea that “every message is a signal.”
I’m seeing something similar with a Kickstarter campaign I’m running right now. At this stage, the volume is still low, so handling everything personally actually helps me understand what people are really thinking before they decide to back.
It’s interesting how the longer, more thoughtful messages tend to come from people closer to taking action — while short ones are more surface-level interest.
Feels like early on, the goal isn’t efficiency but learning.
Curious — at what point do you decide the volume is high enough to start systemizing support?
yeah I like that framing a lot , especially the “efficiency vs learning” part
I don’t think it’s really about volume tbh
it’s more the moment when things start slipping through the cracks , when you realize you’ve seen the same thing twice but didn’t connect it, or forgot something someone mentioned earlier
that’s usually when it stops being “learning” and starts getting a bit chaotic
curious if you’ve started noticing any of that yet in your campaign?
Keeping it deliberately simple for now — just a contact form that goes straight to email, and I respond personally to everything.
At this stage I actually want the friction. Every message is a signal. If someone fills out a form and writes three paragraphs about their problem, they're a real lead. If it's one line, usually not.
Once volume becomes an actual problem, I'll solve it then. But I've seen too many early founders spend time building support infrastructure before they have anyone to support.
What are you building — B2C or B2B?
yeah that makes a lot of sense — that early signal is super valuable
I’m building more on the B2B side, and seeing a similar pattern where everything still goes through email / direct messages early on
what I’ve been exploring is how to keep that raw input, but make it easier to spot what’s actually recurring vs one-off
not really replacing the flow, just helping you see what matters faster
Spot on. The 'raw input' is where the real nuance lives. Whether it's a Reddit rant or a B2B email, the challenge is always distilling that messy human emotion into a clear product signal.
Keep me posted on your progress — identifying 'what matters faster' is a huge win for any founder.
yeah that’s exactly it — “distilling it into a clear signal” is the hard part
what surprised me is how fast things start slipping through the cracks even when volume is still low
I’ve been building a simple way to surface those signals automatically from conversations
happy to share what it looks like if you’re curious
I totally feel that. The 'cracks' usually start appearing before we even realize the volume is growing.
I’m not quite ready to automate my own flow yet, but I’m definitely curious to see how you’re surfacing those signals. It’s a fascinating challenge to solve.
yeah that makes sense — I’m not a big fan of automating too early either
it’s really more about visibility than automation on my side
I can show you quickly — it basically sits on top of your conversations and highlights patterns / signals without changing your workflow
want me to send you a quick preview?
Biggest bottleneck I've hit is that people describe problems differently in surveys vs when they're actually frustrated in a Reddit thread. Survey answers get sanitized. Reddit captures the raw language, which matters a ton — if your landing page doesn't use the same words your users use to describe the problem, you lose them even when you solve exactly what they need.
To your second question — before building, definitely. But the report would need to include verbatim quotes, not just summarized pain points. The phrasing is the real insight.
The simplicity of the website is a big positive action to have, you want to decrease the amount of clicks and information that the user has to go by before getting to the exact information that it has. And the option to have a custom made report is a powerfull move since it cleans out the output of the information
Wonderful...Awesome!!... that's very good
Wow, thanks for all the sharp eyes here! A few quick updates based on your feedback:
On the 'Roast' (Ayesha): You're 100% right about the visual overload. I'm already working on a cleaner Hero section to highlight the 'one clear promise' instead of a wall of icons.
On Scalability (joongix): The secret is the 'high-emotion' filter. I focus on long-form rants (3+ paragraphs) with specific analogies. It's about 70% automated discovery and 30% manual curation to keep the 'so-what' actionable.
On Trust (Suhail & BAOYA): I hear you on the 'sample first' mindset. I'll be releasing a 'mini-version' of the ETF report soon so you can see the depth before committing.
Really appreciate the IH community for helping me refine airiya.org in real-time!"
Pivoting from a one-off analysis post to a full service because people asked for it in the comments is one of the cleanest demand signals you can get. You didn't guess at the market, you watched it raise its hand. The verbatim user quote angle is smart positioning too. Most market research gets sanitised until it sounds like a copywriter wrote it, not an actual frustrated user. Did the ETF investor pain points mostly confirm what the founder expected, or were there things that genuinely surprised you in the data?
Great question, Amanda! What surprised me most wasn't the technical friction (like high fees), but the emotional anxiety around 'Portfolio Overlap.' >
I expected investors to want data on ROI. Instead, the data showed a recurring fear of 'feeling stupid' when they realize their 'diversified' funds all hold the same top 10 tech stocks.
That specific emotional signal — the fear of redundant risk — is something I never would have guessed. It's why the ETF Overlap Checker became my first tool; it solves a psychological pain, not just a math one.
Brutally honest roast for airiya.org:
✅ Strong Method: Mentioning "40+ sources" and "7 days" gives the service a tangible edge. It feels like a machine, not just a vague agency.
❌ Visual Overload: The hero section has too many competing numbers and icons. My eye doesn't know where to land first.
❌ Passive Headline: "Turn Real User Pain into Market Advantage" is a bit wordy. It's good, but not punchy enough for a quick scroll.
Score: 7.5/10 — The "How it Works" section is elite, but the Hero needs a cleaner visual hierarchy.
I'm currently at 96/100 audits for the day! If you want the full 10-point AI breakdown (covering trust signals and mobile friction), you can run the rest of the roast here: https://roastmylanding.vercel.app/
Let's get you in as #97! 🌶️
Really interesting approach, Eric. I'm in a similar spot — building JewelViz
an AI tool that helps Indian jewellers create professional jewelry photos without expensive photoshoots. Zero paying customers yet, but product is live.
My biggest research bottleneck was actually talking to customers directly — I built first, validated later. Big mistake in hindsight.
On your question: I'd pay for the report only after seeing a sample. Trust has to be built first, especially for early-stage founders with zero budget.
Good luck — honest building-in-public posts like this are rare.
This is really interesting — especially the focus on pulling actual user language from Reddit.
I’ve been seeing something similar while looking into peptide therapy clinics. The problem isn’t just finding information, it’s that everything is inconsistent — pricing, what’s included, how treatments are packaged, etc.
It makes it really hard to even define the “problem” properly because users don’t know what they’re comparing half the time.
Curious — when you’re pulling pain points, how do you separate signal from noise? Especially in niches where a lot of the info is vague or conflicting?
Really impressive execution, Eric — especially how you turned raw Reddit insights into something structured and actionable.
The part about using real user language to shape the landing page stands out. That’s something I’m realizing matters a lot more than expected when trying to convert interest into actual users or backers.
Also appreciate the honesty about being early with no paying customers yet — but having a real pipeline. That’s a strong signal.
On your question — I’d personally lean toward validating first before paying for a full report, but I can definitely see the value once there’s clearer intent and a defined audience.
Curious to see how this evolves — thanks for sharing this so transparently.
This is a clever evolution from the original post. The jump from "I analyzed pain points manually" to "I built a service that does this end-to-end" is exactly the kind of validation loop that makes sense — you proved demand existed before productizing it.
The ETF overlap checker as a free tool targeting pain point #4 is smart positioning. It gives people a taste of the value without requiring commitment.
One thing I'm curious about: with the 40+ sources in the research report, how much of the synthesis is automated vs. manual curation? I imagine at some scale the signal-to-noise ratio on Reddit specifically gets tricky (lots of venting that doesn't translate to actionable product insight). Would love to know how you handle that filtering step.
This has solid potential, especially for early-stage founders.
But right now it sounds like multiple services instead of one clear promise.
What you’re really offering is: “build what people already want.”
If you make that obvious through a simple before/after or case study demo, conversions will improve.
A sharp 20–30 sec video can do that much better than text If you want i can make it for you
The 'requests coming in after the first post' → service product is a clean validation arc. You didn't pitch the service first; you did the work publicly, let people ask for more of it, then built around what was already being demanded.
The 40+ sources + verbatim user language approach is the right foundation for landing pages. Most copy fails because it uses founder language instead of the words people actually use when describing their own problem. Reddit data is a useful forcing function for that — it's unsolicited, unpolished, and much closer to real pain than any survey.
Curious about the free tool component — is that primarily for SEO/lead capture, or are you finding it builds enough product trust to convert to paid reports directly?
Spot on. Reddit is the best 'unsolicited truth' machine — it forces you to drop the 'founder ego' and speak the user's language.
To answer your question about the free tool: It’s actually both, but the priority is Trust.
SEO/Lead Capture: Yes, it targets a high-intent keyword (ETF overlap).
Proof of Work: It’s a live demo of the research. When people see I can turn messy Reddit rants into a functional solution, the 'Research Report' stops being abstract and starts being tangible.
It’s less about a direct 'click-to-buy' and more about proving I understand the niche before asking for a sale. Curious — are you also using public data to validate something right now?
That “research → tool → landing page” chain makes a lot of sense.
We’ve seen something similar where the biggest shift comes from using actual user language instead of what founders think people care about.
The tricky part is turning that into something people will actually pay for.
From what we’ve seen, most founders are happy to read research, but only pay once it’s tied directly to something actionable.
Curious if the interest you’ve had so far is more around the reports themselves, or the full “build something from it” side?
Really useful framing — and honestly it matches what I'm seeing so far.
The few people who've reached out are less interested in "give me a report to read" and more interested in "help me figure out what to build or how to position what I already have." The research is the means, not the end.
Which is why I structured the full package as research → tool → landing page. The report alone is table stakes. The value is what you do with it — and most early-stage founders don't have time to translate insights into product decisions themselves.
So to answer directly: the interest seems to be on the "build something from it" side. The report is just the foundation.
What's your context — are you currently validating something?
Yeah that makes sense, the value is in what comes out of it, not the report itself.
We’ve been validating more around systems that already exist rather than starting from zero. In most cases the problem isn’t “what to build”, it’s aligning what’s already there with how people actually think about it.
That translation step is where things usually break.
Spot on, Rebecca. 'The translation step is where things usually break' is a powerful way to frame the core problem.
Most teams have the data, but they lack the bridge between raw user rants and strategic positioning. That’s exactly why I prioritize using verbatim language over founder jargon — it’s the only way to ensure that 'alignment' actually happens on the landing page.
This insight helps me frame my own service much more clearly. Appreciate the exchange!
"Eric, the way you've mapped out a full end-to-end service—from priority matrices to developing tools like that ETF Overlap Checker—is a masterclass in solving 'research bottlenecks.' Since you're still early with a real pipeline but no paying customers yet, there’s a competition where you can submit this — entry is $19 and winner gets a Tokyo trip. Prize pool just opened at $0. Your odds are the best right now. It might be the exact spark you need to turn that pipeline into revenue!"