Hello Inbox – Free email deliverability checklist for marketers

Hacker News

After struggling with low open rates in my small lifestyle software business, I decided to go down the rabbit hole that is email deliverability, learned a bunch of things I didn't know even after being in tech for a decade. A/B tested a bunch of things and finally got good results. I then decided to put together a comprehensive checklist of everything I learned and got me results to share with others. Now I help companies fix poor email deliverability and boost their email ROI. I want this checklist to be a repository of email best practices so I'm always looking to improve and update it. Your feedback is welcome.

Visit product View on Hacker NewsSep 16, 2023

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Actual performance

37points

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Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released

73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

25%25% 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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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