Octolens, a social-listening tool for tech brands, spent a little over €600 on a ChatGPT ad campaign. After two weeks, one ad shown in the US had brought about 25,000 impressions, just under 200 clicks, and zero signups.
During the same period, 10 people signed up after ChatGPT recommended Octolens in an ordinary conversation.
Founder Charlotte Schmitt reported both results. Her team checked the zero against OpenAI's conversion tracking and their own analytics. People clicked but didn't sign up.
Schmitt's interpretation was: "The recommendation has trust but an ad sitting next to it doesn't inherit that."
The comparison doesn't isolate trust as the cause. It was a small test, and those 10 signups don't establish a viable business. But treating the paid result as a verdict on the whole product would erase a difference that matters: people reached through another route did sign up.
You paid to put your offer in front of people under particular conditions. Those conditions are part of what you tested.
The failed campaign tells you something about that attempt. It doesn't make every way of reaching a customer equivalent to the one you paid for. Nor does the better result elsewhere tell you to keep investing indefinitely.
The stakes can be much larger. One founder on Hacker News put it plainly: "I am at a crosspoint now. I don't know if I should continue to work on my startup." They reported raising about $100,000, most of it spent on ads, travel, and accelerator workshops, and making about $4,000 in revenue. They still did not know whether to continue.
When you've paid for ads, outreach, or help finding customers and sales are still low, you have a bill, a disappointing result, and the same decision: spend again or stop the project.
You hoped a real attempt would settle the question. It can feel harder to leave when "maybe we didn't spend enough" is still available as an explanation.
When your own sales are low, "this attempt didn't sell" can become "this business can't sell." The opposite leap is just as tempting: "We haven't spent enough yet."
Both leap past the explanation you need before committing more money.
That is where a diagnosis earns its place. It clarifies what the customer was actually trying to buy, what your current offer gave them, and whether the gap is worth another round of time and money. It can recommend continuing, changing the offer, repositioning, or stopping, and explain why the evidence supports that recommendation.
Calling the result "a marketing problem" and handing you another campaign leaves that decision untouched. A useful diagnosis explains which part of the business the failed attempt puts in doubt, and whether the evidence gives you a reason to fund another attempt.
A disappointing result can justify stopping an attempt. Turning it into a verdict on the business requires an explanation.
Before committing the next budget, bring the failed attempt back into view as something specific: this offer, reaching people through this route, asking them to take this step.
The next payment deserves a reason stronger than "the last one didn't work."
Top comments (12)
Hey Dan, I read your post about 200 ad clicks and zero signups. I'm a solo founder too and stuck on the same problem with my own tool, so I'm curious: is it more that the right people never see it, or that they see it and don't click?
Kevan, for this case neither option matches: the ads produced about 200 clicks, so people saw them and clicked through — what never happened was the signup. Whether the right people saw it, the numbers can't say; clicks don't come labeled by fit.
Impressions, clicks, and signups can fail for different reasons, and each points to a different fix. Which of the three is sitting at zero in your own numbers?
That’s a good way to break it down. For FounderPulse, the hardest part is getting the right founders to discover it in the first place. I’m trying to understand whether the bottleneck is visibility, interest, or conversion rather than assuming the product is the problem. Have you found anything that helped you distinguish between those three?
For FounderPulse, reaching founders isn't the same as reaching founders who are actively trying to find their next customer. Someone browsing AI tools can visit the same page for a very different reason. Signup numbers alone won't tell you whether to change the product or how you're reaching people.
I review stalled products and offers to help founders decide what to change, keep, or stop. My profile explains that work if it's relevant to the decision you're facing.
That’s an interesting angle. Finding the right people is only half the problem; founders also need to understand what to do with the feedback they get. Do you usually find that stalled products struggle more with positioning or with the product itself?
I can't say which is more common. "I don't understand what this does" and "I understand it, but I don't need it" can both look like weak interest.
For FounderPulse, are you looking to work this out yourself, or considering an independent review?
I'm working on it myself for now, but I'd definitely be open to an independent review. I'm still trying to understand whether the main issue is positioning, targeting, or the product itself. What would you look at first?
I’d first separate two cases: the right founders never see FounderPulse, or they see it and still have no reason to act. I’d look at what each person was trying to do, what they understood, and where they stopped. That can point to positioning, targeting, or the product without treating every weak signup result as a product verdict.
If you want an independent review, I can look at the current offer, the evidence from people you reached, and the next decision. I’d tell you what I’d change, keep, or stop for this situation, rather than hand you a generic funnel checklist. The work is described on my profile if you want to talk through scope, timing, or price.
dan, this is the exact trap early-stage founders fall into, and your distinction between "this attempt didn't sell" and "this business can't sell" is the most important framing in indie hacking.
when you are bootstrapping with severe constraints (e.g., building entirely on a $150 phone), you cannot afford to treat marketing as a black box of "spend more until it works." you have to diagnose the specific friction point.
the octolens example perfectly highlights that trust and context are the actual currencies, not just impressions. a recommendation carries inherent credibility; a blind ad does not. that’s why constraint-driven founders often find more success in high-signal, organic communities (like dev.to or niche discords) than in broad paid campaigns. you aren't just testing an offer; you're testing the environment in which the offer is received.
fantastic, grounded perspective. diagnosing the specific gap between the customer's intent and the current offer is the only way to earn the right to iterate. 🐯📊
Harun, when money is tight, "spend more until it works" leaves very little room to learn from a failed attempt. You can end up paying for another campaign without getting any closer to knowing what to change.
The detail that would change the founder's decision is the one about the ten signups: those people were already inside a conversation about the problem when the product came up. An ad shows the offer to people who match a keyword; a recommendation shows it to people who match a situation.
The affordable test between "this attempt didn't sell" and "this business can't sell" is to reproduce the second condition on purpose. Write down the situation those ten were in (role, company size, what they were trying to do that week), count how many such people exist, and reach a hundred of them directly with the same offer. A hundred answers, even mostly no, is a diagnosis. Two hundred clicks from people whose situation you never learned is not. We build lists like that for teams, and the counting step alone usually settles whether the ad was the wrong route or the offer was.
Learning what those ten people needed would help. Reaching similar people directly could be useful, but it's a different test: now the seller is introducing the product, rather than ChatGPT recommending it during a conversation.
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