Userfeed
Indie HackersProduct feedback made for Intercom
We built Userfeed to solve a problem we experienced in our other startup: what to build next. Userfeed uses data from Intercom to help you identify opportunities to features that are more impactful across your customers.
Share cardActual performance
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: user · Missing: mac, agents, macos56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal26%26% 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, revenue16%16% 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, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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