AppstoreSpy: A #1-of-the-Day Product Hunt Launch Converted Only 10 Paying Users — What Grew It Was 113% in the 4 Months After v2

All dollar figures are converted at an approximate rate of $1 = ¥150.

Before we get into this: a note on the numbers

Before covering AppstoreSpy, something needs to be flagged up front. The primary source, Starter Story, displays “$8K revenue/mo” (about ¥1.2M) in both the headline and the profile section — but in the body text, founder Roman Medvedev himself says the following.

Right now we’re operating at breakeven, so we’re not profitable yet. We’re funding development and growth ourselves.

$8,000/month in revenue and “not yet profitable” coexist on the same page. It’s possible “revenue” was used loosely to mean profit, but that can’t be confirmed. This article therefore does not treat monthly revenue as a confirmed figure. What can be confirmed are metrics other than revenue, customer count, traffic, growth rate, pricing. That part, at least, is high-resolution.

What can be confirmed

What it sells

AppstoreSpy is a tool for analyzing App Store and Google Play market data. It provides competitor download estimates, keyword rankings, and revenue estimates, targeting app developers, ASO specialists, and app marketing agencies. Pricing runs three tiers, from $19 for individuals/beginners up to $199 for “large companies that want an edge in the app market”, roughly a 10x spread across the top and bottom of the price ladder.

The founder’s description is deliberately simple: “Find a pain point and solve it. I present my solution and ask people to judge it.”

The turning point wasn’t Product Hunt — it was version 2

What makes this case interesting is that the event you’d expect to matter didn’t, and a quiet one did.

What didn’t work. AppstoreSpy hit #1 for the day at its September Product Hunt launch. The result: 1,400 new visitors, and 10 paid conversions from them. That’s a visit-to-paid conversion rate of about 0.7%. At the cheapest plan, $19, that’s $190/month, about ¥28,500. For a #1 ranking, that’s nowhere near enough to change the business’s cash flow.

What worked. In the four months prior to when the article was written, the business grew 113%. The trigger the founder himself points to is the release of version 2: what doubled the growth rate was rebuilding the product itself, not a new-acquisition event.

The contrast points to a specific reading: the constraint on this early product wasn’t “not being known”. It was “not being good enough to stick with.” If 1,400 people visit and only 10 pay, bringing in another 1,400 just adds another 10. Growing the denominator without growing the numerator is exactly what the PH launch was. Version 2 is what touched the numerator.

How it reached 20,000 visits and 13,000 subscribers with zero ad spend

AppstoreSpy has never used paid advertising. Instead, four channels are functioning.

Word of mouth was the primary early driver, and that’s not a coincidence given the nature of the space. App-market data is the kind of information where “people in the same job have the same questions,” and someone who finds a useful tool has a built-in motive to share it internally or in a community. The more shared the pain is across a role, the wider the word-of-mouth path becomes.

A Chrome extension is also structurally significant as a distribution channel. An extension sits on an existing search surface (the extension store), lowers the barrier to trying it (a low-commitment install), and stays resident in the browser to keep top-of-mind awareness alive. Rather than waiting for traffic to a website, it’s designed to live inside the user’s working environment.

The email list runs 13,000 subscribers, with a disclosed 20% open rate and 6% click rate. That works out to roughly 2,600 opens and 780 clicks per send, a meaningful amount of self-controlled traffic against a base of 20,000 monthly visitors.

Partnerships, influencers, and article content fill in the rest. None of these are paid placements. All are ways of showing up in places existing interest already lives.

Risks and limits

The biggest risk is the ambiguity in the revenue structure itself. Stating “we’re at breakeven, self-funded” while running a 6-person team, set against 20,000 monthly visits and 7,000 MAU, is hard to reconcile. How many of the 7,000 MAU are paying isn’t disclosed. If most usage is free, the cost of App Store/Google Play data acquisition scales with user count, meaning growth would translate directly into higher cost.

Another is dependency risk. AppstoreSpy’s raw material is public data from the App Store and Google Play, putting it at the mercy of both platforms’ API specs, terms of service, and data-availability changes. There’s also plenty of competition in this category.

A third is a gap in the pricing design. There’s a 5x jump between $19 and $99, and a 2x jump between $99 and $199. It isn’t disclosed which tier the 10 Product Hunt conversions landed in, but if they concentrated at the cheapest $19 tier, $190/month falls well short of supporting six salaries. Supporting a 6-person team hinges fundamentally on how many customers sit at $99 or above. Yet the disclosed metrics, MAU and visitor count, don’t distinguish free from paid usage, and that’s not enough to assess the health of the business. The “breakdown of paying customers” is exactly the piece that’s most often missing from self-reported case studies like this one.

Conditions for reproducing this

What’s easiest to reproduce is the methodology: choose a pain point shared across an entire role, build a distribution channel (like a browser extension) that lives inside people’s existing workflow, and grow an email list as your own owned traffic source. None of these require capital.

What’s easy to misjudge is the expectation set by a launch event. A #1-for-the-day Product Hunt result converting to just 10 paying customers is worth remembering. A launch is more efficient once the numerator, conversion and retention, is already dialed in; buzz before that stage just burns through a single shot at the stat.

There’s also a lesson for anyone covering these stories: self-reported numbers can conflict even within the same page. Taking the headline figure at face value isn’t a shortcut worth taking, checking the body text is a step that shouldn’t be skipped.

Related reading

• ScrapingBee’s sale — a developer tool that grew around a “pain point”
• PDF.ai’s Damon Chen — a solo developer’s record of testing both numerator and denominator across multiple products