An iOS Flurry Analytics Client

Hacker News

As an iOS developer I've used Flurry for my analytics for some time for many of my apps. There are some ok iOS apps to check your stats out there already but I ended up making my own for the pieces of information that I most care about and check on a daily basis. I used it for a little while myself but recently submitted it to the app store. I also made a feature to check an iOS app rank within a specific category, device and country. It is a little work to set up but if you know your app's market and you are within the top 300 I think its a great way to find your real time rank. Here is a direct link to the app in iTunes: https://itunes.apple.com/us/app/daily-statistics-for-flurry/id590200641?mt=8&ign-mpt=uo%3D2 (Unfortunately iTunes changed their screen shot upload policy so I will have to wait for a new version to update more of them.) I hope this is helpful to other iOS developers like it has been for me and if anyone has any feedback I'd love to hear it.

Visit product View on Hacker NewsJan 16, 2013

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

83%83% 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, apps, new · Missing: mac, agents, macos

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

63%63% 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 released

33%33% 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 · Missing: arr, mrr, revenue

25%25% 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

23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

0%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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