A SEO Keyword Search Engine

Hacker News

I had posted here before and the thread blew up. Now I am ready with the product Here is the thread in question... https://news.ycombinator.com/item?id=36227470 and this video that I had posted... https://www.youtube.com/watch?v=b-873SkJ2RI Here is what it does... Before you write any content, you want to know what questions people ask on Google, Who is ranking on the top 10 and what other keywords do they rank for?, and 'Google Suggestions' for the phrase. So I created a tool that does that based on all the interest I received. It is at https://www.keywordranking.me/ It's been a whirlwind last couple of weeks since the idea popped into my head till the launch but a post about the journey is for another day. Hope you like what I have managed to build. I would love the feedback from you guys

Visit product View on Hacker NewsJun 22, 2023

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

66%66% predicted probability of success on Indie Hackers, 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

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

53%53% 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, intuitive

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, google · Missing: mobile apps, ios, personal

47%47% 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

18%18% 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

1%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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