Last year I built a SaaS in six months. Polished UI, solid architecture, clean code. Launched to 12 signups. Zero paying users. Shut it down.
The postmortem was brutal but clear: I built something nobody needed. I fell in love with the solution before verifying the problem existed.
Since then I've been rebuilding my customer discovery process from scratch. Not with frameworks from books, with conversations that went sideways, signal that turned out to be noise, and a few lines that stuck.
Here's the filter I'm using now. It's not finished, but it's already better than anything I had before.
The Problem With "Would You Use This?"
Most founder advice says: talk to users, validate your idea, get feedback.
The problem is that feedback is cheap. People will tell you your idea is great, that they'd totally use it, that they'd pay for it. Then they won't.
The gap between what people say and what people do is where startups die.
I needed a filter that separated enthusiasm from commitment.
The Three Questions
When someone names a painful task, I stopped asking "would you want this automated?" Instead I ask three things:
1. The Receipt Question
"Walk me through the last time this actually hurt you."
Not "would this be useful?" but "when did it last cost you something?" A specific moment, a specific cost, a specific workaround they used instead.
If they can't anchor it to a specific incident, the signal is weak no matter how enthusiastic the answer sounds.
2. The Cost Question
"What stays broken if this never gets fixed?"
This separates complaints from costs. A recurring annoyance is a complaint. A recurring annoyance with a broken process and a willingness to change is a wedge.
3. The Switch Question
"Would you switch tomorrow?"
But with one caveat, the answer only counts if it attaches to a dated incident.
"When this broke last month, I spent two hours on it" counts.
"Yes, I would probably switch" doesn't.
If the switch answer cannot attach to a specific memory, it is still a wish describing itself as a decision.
The Ratio Test
Pass/fail isn't enough.
Twenty conversations where five produce dated incidents and fifteen produce adjectives is a different market than the reverse.
The filter gets sharper when you track two things:
- Did one vivid story appear? (pass/fail)
- How often does the same kind of costly incident repeat? (the ratio)
A founder with one dramatic anecdote and a shrug on frequency is a different signal from a boring incident that happens every week.
Both count as receipts. Only one of them is a market.
The Calendar Test
"How many times this month?"
This is the whole filter.
One dramatic story is a blog post. The same boring incident every week is a business.
You don't just need the story. You need the calendar.
"When did it last hurt?" gets the receipt. "How many times this month?" tells you if it's a market or an anecdote.
The Stall Index
There's an outside check that helps too: looking at public postmortems of stalled products.
The same split keeps showing up. The ones that moved had a current cost someone was already paying — a workaround, a bad alternative, an annoying manual step, a risk they were already living with.
The ones that stalled had interest, but not much cost.
Interest without cost is a compliment. Cost without a workaround is a market.
The Full Filter In One Sentence
Enthusiasm is cheap. A dated incident with a real cost is the receipt.
A vivid incident is not always a recurring one. The receipt proves the pain is real. The frequency tells you whether it is a market.
What I'm Doing With This
I'm running this filter against 20 more conversations before I publish the full version with interview questions baked in.
If you're building something and want to pressure-test your own discovery, steal these questions. Run them against your next five conversations. Track the ratio. See what holds.
I'll update this post with the refined version when it's ready.
This filter was built in a public thread on DEV where someone pressure-tested my frameworks in real time. The best ideas here aren't mine, they came from the conversation.
If you've run your own customer discovery that went sideways, I'd genuinely like to hear what you learned.
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