Ezan Vakti

TrustMRR

Live on the App Store since 2018, this is one of the top 2-3 prayer times (ezan vakti) apps of choice for Turkish users in Turkey and across Europe. Turkish-only interface serving a loyal niche audience with ~100K monthly active users. Daily-use utility with strong retention and consistent organic growth.

Visit product View on TrustMRRApr 18, 2018Mobile Apps Mobile App B2C

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Actual performance

621customers

$489MRR/mo

Did not reach leaderboard

Traction signals

MRR growth 30d-2.3%

Launch Intel predictions

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TrustMRRFits verified-revenue profile · Strong signals: apps, month, monthly · Missing: mobile apps, ios, personal

79%79% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: apps, user · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface, users · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, 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

34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: growth, active · Missing: arr, mrr, revenue

30%30% 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

22%22% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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