Automated and personalized push notifications and emails using LLM

Hacker News

I'm currently trying to find proof of concept around this idea i have. It's pretty early and any feedback is appreciated. Currently in email marketing you can personalize marketing emails based on tags and group them into fx. a segment. This allows the marketer to personalize the email to the extend an limited to the segmentation, tags of the user and usage of those tags in the emails. But what if you could fully automate it and personalize it to create unique marketing emails for everyone where real-time user data, behaviour and the environment the user is in also could be part of this. I'm working on a beta. The idea is that rather than writing your marketing email your would instead provide an "intent". Fx. "new product launch at https://gemini.google.com/ ". This information could then be used along with information about the users to fully customize the email for each of them. Even based on the weather and stuff like that. The hypothesis is that this would increase AARRR.

Visit product View on Hacker NewsFeb 25, 2024

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

95%95% 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 · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% 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, io · Missing: https docs, excited, just released

44%44% 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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

14%14% 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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