LiftMRR – a lightweight tool for Stripe users run automated campaigns

Hacker News

Hi everyone, After trying out a few SaaS ideas, one thing kept frustrating us: getting users to convert from free trials to paid. We noticed the simplest solution was well-timed email campaigns — reminders, feature highlights, even simple “here’s what you might be missing” messages. But setting up these automated flows is harder than it should be, especially if you just want to connect Stripe and quickly target trial users. So we’re working on LiftMRR.com — a lightweight tool to help Stripe customers run automated campaigns that encourage trial → paid conversion. It’s still early stage, please do give it a try and would love to hear your feedback?

Visit product View on Hacker NewsAug 20, 2025

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Launch Intel predictions

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

67%67% 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, stripe, email · 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: users · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

44%44% predicted probability of success on AppSumo, 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

32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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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