Featuretracker.com – A simple way to track features

Hacker News

Hey, HN! Cody and Jake here, the founders of FeatureTracker ( https://www.featuretracker.com ). We have one simple goal, to allow creators to make data-backed decisions when building interfaces. Maybe you have a really fancy date picker and are looking for ways to scale it back. With FeatureTracker you can track the granular usage of each component of that date picker, then decide what to keep and what to cut based on actual numbers. We track specific clicks and provide a few simple data points to make you feel a little more comfortable making decisions and less like you’re driving into the dense fog that can be product development. We built this to help us answer questions and hope it can help a few others too. We're aiming to keep things simple as possible. We'd love to hear from you, so let us know if you have any questions or have feedback. Thanks, Cody and Jake

Visit product View on Hacker NewsAug 19, 2017

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

61%61% predicted probability of success on Hacker News, 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

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

55%55% 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 · Missing: plus, platform, intuitive

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%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, 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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