PR

PR Boost gets your startup in the news with PR campaigns powered by AI

Hacker News

PR Boost gets your startup in the news with PR campaigns powered by AI

Hey everyone! For the last year and a half we’ve been working hard on building the future of PR software at Press Hunt ( https://presshunt.co ). We’ve compiled one of the most advanced journalist databases available. One of the biggest requests we’ve had from our users is to have guided campaigns ran by PR experts integrated into our core software product. We’ve launched a similar service in the past called Howler––Boost is a remastering of Howler, integrating it into our core do-it-yourself PR tool and hooking it up with our refined Press Hunt 2.0 dataset. Here’s how Boost ( https://presshunt.co/boost ) works: 1. We use proprietary algorithms to match you with 1,000 targeted journalists from our database of ~400k 2. Our team of PR experts writes your pitches––we’ll refine them until you’re happy & give us the green light 3. We run your PR outreach campaign to the journalists & reporters in your list 4. We introduce you to the journalist directly when they express interest in your story so you can set up a time for an interview Boost is the simplest way to run a PR campaign without spending hundreds of hours manually researching journalists, trying to find their emails, and sending emails to them. We take care of the whole process so you can just quickly get your campaign out without all the BS. We’ve been working really hard on this, and we’d love your feedback! If you’d like to give Boost a try, I made a 20% off lifetime discount code that will be live for the next few days. Use the code ‘EARLY’ at checkout. Thanks for your time! – Matt, Aaron, & Frances PS: We just launched on Product Hunt: https://www.producthunt.com/posts/pr-boost-by-press-hunt

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
94%94% 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: user, new, email · Missing: mac, agents, macos
80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000 · Missing: https docs, excited, just released
34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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