Bucket – From feature launch to customer satisfaction, consistently

Hacker News

Hi HN! We have put A LOT of effort and soul into crafting this product. It’s something we’ve wanted to have ourselves for so long. We hope you like it! Many tech companies end up as feature factories. Bucket is here to help product teams consistently evaluate features, so they can double down on the features that matter - and cut their losses on the ones that never hit home. We do this based on engagement data and on qualitative feedback. We use the STARS framework, starsframework.org, to combine this data into a consistent feature satisfaction score. We then report the feature satisfaction and live customer feedback to you and your team on Slack until you make a decision: Looks good or needs another iteration. Engineers, PMs and designers: We’d love to hear your feedback!

Visit product View on Hacker NewsJun 8, 2023

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Actual performance

21points

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Launch Intel predictions

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

85%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.

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

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

24%24% 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

16%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, 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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