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I Made a Tool to Simplify Tracking App Subscription Earnings
Hacker NewsI Made a Tool to Simplify Tracking App Subscription Earnings
Hi HN! I've been working on a tool that allows you to upload your financial reports from Apple/Google and track your real earnings, split by subscription products. I developed it because I need to allocate specific amounts to a developer API, and thought it might be helpful for others as well. It currently only fits my needs, but just contact me if you would use it but it's missing something! Also, you can check out the demo access on the page to see how it works.
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting74%74% 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: apple, google · Missing: mac, agents, macos65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released26%26% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue17%17% 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, crypto0%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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