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Visual, interactive analytics for esports (CS:GO)

Hacker News

Visual, interactive analytics for esports (CS:GO)

I'm working on a visual, interactive suite of analytics tools for Counter-Strike Global Offensive called https://www.noesis.gg . We're bootstrapped and have been working on the project for a bit less than a year. The product has been tested by users in our closed beta since November last year. In March we opened it up to the public with a paid open beta. There are a few other competitors in the space, primarily https://sixteenzero.net/ and https://shadow.gg/ . Their product is targeted pro-teams and cost significantly more than ours. Something on the order of 1000€ per month, against our 30€ per month. We're super happy that we've received overwhelmingly positive feedback from our users! We've even been told several times by users that have access to both ours and our competitors products, that ours compares very well to theirs. Even without taking the major price difference into account. We currently offer a 14 day free trial and are steadily getting more users. But we would of course love to accelerate our user acquisition even more! So far, we've been making partnerships with different low-tier teams and streamers, and paying for ads on facebook, twitter, reddit, and google. We don't see a lot of conversion on our paid ads though, and are looking for other options to reach new users. We would love to hear any growth hacking ideas that you may have! As well as on the product itself, of course :)

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
91%91% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, new · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released
66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: month, google, users · Missing: mobile apps, ios, personal
56%56% 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
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: growth, bootstrapped, active · Missing: arr, mrr, revenue
22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · 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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