Learn How To Draw - DrawBuddy

TrustMRR

DrawBuddy is a Duolingo-style "learn to draw" iOS app: short step-by-step drawing lessons, an energy system, streaks, and an AI drawing coach. Live since 2023 with a loyal subscriber base and a proven 40–70% trial-to-paid conversion. 94% of subscribers come organically from App Store Search. Built in SwiftUI + StoreKit 2, monetized via RevenueCat (annual / monthly / lifetime). Recently relaunched with a 7-day free trial and a ready-to-run growth plan (ASO + Apple Search Ads).

Visit product View on TrustMRRFeb 13, 2023Mobile Apps Mobile App B2C

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Actual performance

81customers

$321MRR/mo

Did not reach leaderboard

Traction signals

MRR growth 30d-35.9%

Launch Intel predictions

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

72%72% 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 · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

42%42% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

39%39% 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

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

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto

11%11% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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