QR & Barcode Scanner App Portfolio (iOS/Android) w/ $16M Revenue

Acquire.com

A globally scaled QR code and barcode scanning platform operating across iOS and Android with over 863M lifetime downloads and 39.2M monthly active users. The business is embedded in the daily behavior of consumers worldwide and generates one of the largest independent streams of first-party product scanning data outside of major retailers. Approximately 2.7M daily active users scan products and QR codes daily, creating valuable commerce intent data with significant monetization potential. T...

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Actual performance

$1,333,333/yr

Made the leaderboard

Traction signals

Asking price$24,980,459

Annual revenue$16,000,000

Revenue multiple1.6x

Launch Intel predictions

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

86%86% 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.

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

71%71% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comFits revenue-generating acquisition profile · Strong signals: revenue, active · Missing: arr, mrr, profit

67%67% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

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

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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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