Automated competitor tracking tool for startups

Hacker News

Almost every company that I've worked for has always used Notion or Google Docs to manually track their competitors. Once I started working on my own startup, I quickly realized that it's pretty tedious to try to keep an eye on my competitors and keep all the data updated manually. RivalHunt is designed to empower startups in tracking their competitors effortlessly. It offers: Real-time updates on competitors' website changes, such as pricing updates, team modifications, and feature releases. Stay informed about competitors' latest social media posts across Twitter, Facebook, and Reddit. Access the most recent Crunchbase data on competitors, including funding rounds, acquisitions, and team alterations. Customizable update frequencies to suit your monitoring needs. There is a Free plan available for anyone who wants just to try it out. Thanks!

Visit product View on Hacker NewsSep 7, 2023

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Actual performance

2points

Did not reach leaderboard

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

93%93% 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 · Missing: mac, agents, macos

83%83% 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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% 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: google, way · Missing: mobile apps, ios, personal

37%37% 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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