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StatsKit – Product Analytics and A/B Testing
Hacker NewsStatsKit – Product Analytics and A/B Testing
Hi HN, I've been working on StatsKit for the past year. It combines product analytics, feature flags, A/B testing, session replay and funnels into a single platform. https://statskit.ai Happy to answer any questions.
Share cardActual performance
2points
Did not reach leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: single · Missing: mac, agents, macos85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting42%42% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released42%42% 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, revenue21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews15%15% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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