Fi

Find profitable niche in App Store and Google Play Market

Hacker News

Find profitable niche in App Store and Google Play Market

Hi, This is my brand new project which helps indie iOS/Android devs discover the most profitable phrase niches in Apple App Store and Google Play Market for their apps and monitor position of their apps in markets. Please give me your feedback :) Regards, Wiktor

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Actual performance

63points
20comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
TrustMRRFits verified-revenue profile · Strong signals: ios, apps, google · Missing: mobile apps, personal, entrepreneurs
78%78% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: apple, google, apps · Missing: mac, agents, macos
74%74% 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: io · Missing: https docs, excited, just released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: profit, profitable · Missing: arr, mrr, revenue
42%42% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
16%16% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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