A DB of SaaS email examples

Hacker News

Hi everyone! I’ve spent the last couple of months building a library of SaaS email examples. Why? Well, if you’re the one creating email campaigns for your SaaS, you know how hard is to find good examples to take inspiration from. I’m not just talking about popular sequences, but also about simple emails that are essential for a business like subscription renewal reminders or pricing updates. That’s why I’ve collected, and organized by use case, hundreds of examples from top Startups as well as niche indie SaaS. The database is new and growing, and there’s still a lot of work to do but I’d love to hear your thoughts!

Visit product View on Hacker NewsOct 24, 2024

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

75%75% 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 · Missing: supports, reddit linkedin, podcasting

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

51%51% 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: month · Missing: mobile apps, ios, personal

46%46% 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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas, subscription · Missing: arr, mrr, revenue

25%25% 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.

Incorrect prediction on native model

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