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I'm writing a book on finding a startup name

Hacker News

I'm writing a book on finding a startup name

I'm writing a book for finding a startup/product name, aptly titled "The Book of Naming." http://kcovia.com/thebookofnaming.html It's pretty much a complete walkthrough to the whole process. I take a 4-part approach: create a ton of names, analyze them, test them, then compare them for a final decision. Any feedback would be awesome! (Also, I've posted on HN for years under keiferski but actually changed my _real_ name because it's extremely Polish and doesn't work well for English speakers.)

Visit product View on Hacker NewsMay 5, 2014

Actual performance

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Did not reach leaderboard

Launch Intel predictions

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

73%73% 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

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

35%35% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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