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PaywallPro – Analyze and optimize app subscription paywalls

Hacker News

PaywallPro – Analyze and optimize app subscription paywalls

Hi HN, I’m Jason While building subscription apps over the past few years, I noticed a recurring problem: paywall design often decides the difference between making a living and making nothing. • Two apps in the same category can see 3× different conversion rates just because of how the paywall is designed. • Yet most developers lack real data or examples to guide their decisions. That’s why I built PaywallPro. It helps developers, PMs, and designers: • Explore 46,000+ iOS paywall screenshots across categories. • Track historical versions to see how competitors iterate on pricing, design, and copy. • Benchmark with revenue metrics (ARPU, RPD, etc.). • Watch 2,600+ onboarding flow videos to study the full funnel. Try it here: https://paywallpro.app I’d love to hear your feedback, suggestions, or feature requests. I’ll be around in the comments to answer questions.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: apps · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000, io · Missing: https docs, excited, just released
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: ios, apps, video · Missing: mobile apps, personal, entrepreneurs
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue, recurring, subscription · Missing: arr, mrr, profit
32%32% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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