Follower Segmentation for your Twitter audience

Hacker News

Hey folks! My co-founder and I built a tool that lets you segment your Twitter audience. We parse your followers bios and then do some keyword analysis to determine interests, occupations, and other keywords. We also grab all of the available public metrics that Twitter's API offers, like followers, follows, likes, etc... If this sounds interesting, feel free to reach out on Twitter or just shoot us a note at email at wildfire.so, and I will give you an HN discount! We're curious what use cases this might support or what value people might gain from this. We have a hunch that the answer is basically zero. People like the data, but aren't sure what else to do with it. In posting here, I was hoping folks of HN might have some ideas. Let me know what you think!

Visit product View on Hacker NewsJun 7, 2022

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

82%82% 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 · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

75%75% 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.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

41%41% 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 · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs

34%34% 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

12%12% 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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