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Rekko – Stripe app to recover failed subscription payments

Hacker News

Rekko – Stripe app to recover failed subscription payments

Hey HN, I built a small tool to automate failed payment recovery for Stripe subscriptions. Context: I run a SaaS on the side and got tired of losing customers to expired cards. Stripe's built-in retries help but the recovery rate is meh. I wanted something that would ping customers via email and SMS right when a payment fails, with a fresh billing portal link. It's a native Stripe app so setup is just OAuth, no webhooks to configure manually. Stack is Next.js, uses Stripe's billing portal API to generate session links on the fly (so no expired links). Still early, looking for feedback from anyone running subscriptions on Stripe. Free access for early testers. Curious what others are using for this. I looked at Churn Buster and Dunning but they're overkill (and expensive) for small SaaS.

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: stripe, email, context · Missing: mac, agents, macos
77%77% 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
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
42%42% 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
33%33% 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 · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, 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
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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