FlyCode's payment optimization and failed payment recovery technology is powered by ML and AI to stop payment failures before they happen, recover lost revenue, and reduce passive churn. Start increasing revenue today with zero integration!
Hello Everyone 👋 I’m Tzachi and with my cofounders, Jake @jake_vacovec and @etai - we’re thrilled to introduce FlyCode’s Stripe App for Failed Payments.
Many teams we’ve spoken to view failed payments as an unsolvable black box—a cost of doing business. But when a payment fails, it doesn’t just cost you immediate revenue; it significantly impact the customer’s lifetime value (LTV).
With our newest Stripe app, you can stop chasing your customers about their failed payments and recover more payments with zero development work.
Why is this is huge for teams using Stripe? TL;DR before and after:
⚡Fully configurable recovery period with retries tailored to each customer and payment error
⚡ Don’t trigger emails on every retry - recover payments in the background
⚡Communications are coordinated with retries to ensure they’re sent at optimal times
⚡ Faster recovery that you can regain lost revenue to fit your settings
⚡ Increase MRR and LTV on autopilot
We’re early in our journey so really happy to show this to you all, thank you for reading about it! Please let us know your thoughts and questions in the comments.
@jake_vacovec@etai@ynextz few of the founder friends are now irritated by service and loss of revenue by Stripe, will definitely share this. A heating usecase I think
All the best for the launch 🚀
@jake_vacovec@etai@ynextz Wondering about integration - besides Stripe, do you have plans to support other payment processors in the future? It could be handy for businesses using multiple platforms.
@kyrylosilin the models take into account the different payment gateways authorization rate and the local time of the customer. Happy to show you how it works. Feel free to DM me or email us hello at flycode thanks!
Intriguing solution for Stripe users. How effectively does FlyCode's AI predict payment failures? Zero integration is appealing, but I'm curious about the accuracy rate. Would love to see concrete data on revenue impact and churn reduction. Passive churn is a tricky problem - interested to know how FlyCode's approach compares to manual recovery efforts.
@andyroamer thanks. Our product is ROI based and directly design to improve your recovery rate % MoM. Happy to saw you how it works. For case studies you can see more here: https://www.flycode.com/case-stu...
@bensongao One of the biggest challenges is that payment providers, issuing banks, and card networks do not have unified error codes and write their own risk-rules. Each may have errors classified into different categories, which adds complexity and leads to many errors getting bucketed together, such as ‘Do not honor’.
We built decisioning models, taking into account payment metadata and internal classifiers of error codes, as well as an email product that coordinate emails and retries per transaction.
With FlyCode you can recover more payments with (1) the least number of retries (2) as quickly as possible and (3) with the fewest customer communications (4) compliant with the Card networks rules (5) coordinate the communications with the retries automatically for each customers payments. On top of that, solutions like Card Account Updater (CAU) and Network Tokens help to replace old details like expiration date automatically.
I read through the website and one of the case studies, and I bewilders me that a really good payment recovery product can improve revenue by that much! It makes sense to me that the model can leverage customer’s historical payment data to make decision on when, how often the system will retry payment and communicate. I’m curious to learn more what separates FlyCode from other payment recovery apps out there? Is the background retry without communication the real game changer?
Congrats on the launch @ynextz and team!
@tonyhanded the game changer is how the models/agents work together. It's a very complex problem behind the scenes to solve when treating each customer and their payment issues uniquely, especially because they often change mid-recovery. Part of what separates us from others is that we started FlyCode when more sophisticated AI/ML capabilities were available, which allows us to be far more dynamic and customizable in our approach.
@ynextz FlyCode’s approach to preventing payment failures and reducing passive churn with AI sounds like a great way to recover lost revenue effortlessly. How does the platform adapt to different payment systems and ensure smooth recovery across various regions?
@ynextz@haris_gul we localize to adapt to different geo's and languages! We support the vast majority of global payment systems and if a specific one isn't we can quickly add it for a customer.
@jake_vacovec that's a great way of handling it. Also If you have a moment, please hover over the 'Coming Soon' badge next to my name and click 'Notify Me' on InterWiz AI—I’d appreciate your support and look forward to your feedback when we launch!
🌟 Congratulations on the launch, @ynextz@jake_vacovec@etai ! 🎉
🚀 The ability to stop payment failures before they happen and recover lost revenue with zero integration is a game-changer for businesses using Stripe. How does FlyCode’s AI tailor retries and optimize the recovery period for different customers and payment errors?
💸 Reducing passive churn and increasing MRR on autopilot is impressive! Could you share more about how FlyCode coordinates retry attempts with customer communications to ensure the highest recovery success rates?
🔧 The no-development work feature is a big win for teams. How customizable is FlyCode for businesses with specific retry strategies or customer communication preferences?
Looking forward to seeing how FlyCode transforms payment recovery and boosts revenue for businesses. Keep up the amazing work! 🎉
The fact that you’re using AI and ML to stop payment failures before they happen and recover lost revenue is truly impressive. It’s a game changer for businesses looking to reduce passive churn effortlessly. Best of luck with this innovative solution.
FlyCode’s zero integration model is brilliant but an idea to make it even more impactful would be to add a predictive analytics feature that forecasts potential future payment failures. This way businesses could take proactive steps beyond just recovering failed payments.
@alexandergreen we actually have this feature already : ) we help segment which customers have high vs. low risk of failure and the outcome. This helps companies analyze where higher vs. lower quality customers are acquired from, their characteristics and even the types of product/offers that are attracting them.
Loving the AI-driven approach to payment recovery! It's awesome how it figures out the best times to send payment reminders and retries. Super useful for anyone looking to maximize their subscription revenue
@poll_coll thanks! For existing customers we also enable our dynamic billing product to charge customers at optimal times/days to reduce payment failures before they happen!
Hi Tzachi, Jake, and Etai! FlyCode's Stripe app sounds like a game-changer for businesses dealing with failed payments. It's impressive how you've tackled such a common pain point. How does the ML/AI component adapt to different types of payment errors?
@web3_antivirus the models/agents work together and it's because of this that it can dynamically adapt mid-recovery when for example an error code changes, billing details are updated, or even a backup payment method is substituted. There are also limits set by the networks so the adaptability is as much of a safeguard as it is a competitive advantage.
FlyCode’s AI-powered payment recovery looks like a smart solution for businesses using Stripe. The idea of stopping payment failures before they happen and recovering lost revenue is really practical. The zero-integration setup is a big plus, making it easy to start using right away. It would be interesting to see how the ML/AI predicts and prevents payment failures in real-time. Congrats on the launch! 🚀
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