Sociaaal · B2C · AI-native app studio
Run the same style of test ads for apps you might build or buy
Strong ad results count as a key data point in buy decisions; no numbers
What they did
What happened
In their words
Patrick Stuart-Constant & Pablo Sánchez - $16M AI CEOs: How to Make $1.3M/Month With AI Apps
Joseph ChoiIs there a thesis for how you buy these apps and why you like why you buy certain apps? And I assume because you you have a growth machine, you know, you're running 4,000 ads a month, and there's some reason that you think you can grow certain apps.
Patrick Stuart-ConstantYeah, absolutely. We're very data-driven in our approach, and so we look we've got at the same time sort of top-down approach, so we look at the market, the market structure, what seems to be the general willingness to pay in that market, how fast it's growing, and so that's sort of that top-down. But we also have a bottom-up approach where we'll run ads for uh apps which don't yet exist or that we're looking at buying.
Patrick Stuart-ConstantAnd if the ads perform well, and we've built quite a big benchmark and they're very standardized ads so that the data is as comparable as possible, then that would be another very strong data point. And then obviously, you know, just like looking into the data of the app itself, you know, the classic things: the ARPU, the retention, any of their own marketing data if they've run any. My dream is sort of, one day, to be able to buy an app without even like knowing what the app does. Just like looking at the data, generating our own data, and making the decision uh like that.
Joseph ChoiThat's really interesting. I want to dive like fully into the data part cuz I think that's uh a differentiator that you guys have. But like what what is your just to get an idea of the apps first, like what is your biggest app? Maybe uh maybe you're not able to share like revenue.
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More from this episode
- Buy apps that went viral and are now declining, then grow them againBought 6-year-old Celebs mid-decline; now bigger than ever, ~$300K+/month
- Drop the paywall after onboarding; show the lookalike result fast and ask laterMore revenue per user; later paywalls converted the users who used to drop
- Rerun old A/B tests that lost once the rest of the app has changedOnboarding paywall removal lost several times, then won big
- Run about 100 A/B tests a month, starting small and scaling the winnersPortfolio ARPU roughly 3x in 18 months, 2x in 12; about 1 in 10 tests wins
- Write down the reason behind every A/B test so AI agents can learn from it
- Make about 4,000 AI video ads a month, with people coming up with the ideasProfitable at 3x LTV/CAC; creative team tripled in 6 months to keep up
- Keep testing brand-new ad ideas instead of endless versions of one winnerCredited with staying market leader in their niches; no numbers
- Use AI to find TikToks from other industries to inspire your ads
- Ask ad visitors many questions, reflect answers back, then offer a $1 first weekFounders say the long quiz works very well; no numbers yet
- Give new users one-tap task buttons so the AI assistant learns about them fastUsers get more engaged once the agent knows them; still needs more guidance
Tags: fake-door, standardized-ads, benchmark, due-diligence