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Wishlist – Collect and organize user feedback

Hacker News

Wishlist – Collect and organize user feedback

As founders, we know how important it is to talk to our customers in order to avoid wasting time building features that no one wants. It can be difficult to know what to work on next, and how many resources to devote to a particular product or feature. That's why I've decided to build https://getwishlist.io, a (currently free in beta) user feedback tool that will help founders like ourselves not only collect user feedback, but also organize it, and build product roadmaps. As such, I'd love to speak with my fellow founders about how you collect feedback from your team and users, how you do your product roadmaps, and some of the challenges you face while doing so. How do you collect and organize feedback from your users? What are your processes? What don’t you like about them? Care to share?

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user · Missing: mac, agents, macos
88%88% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
55%55% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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.

Incorrect prediction on native model

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