I led product work on an inline retry capability for a B2B payment optimization and recovery platform. The goal was to improve payment approval outcomes by identifying failures that were likely recoverable immediately and retrying them within the same payment flow.
This work was part of a broader payment optimization strategy. There are many ways to improve payment performance, but not all of them are equally actionable. Some levers sit with merchants, some sit with gateways or issuers, and some require heavy integration or configuration changes.
The product question was: where could we focus effort inside our own axis of control and see measurable impact quickly?
Inline retry became a strong candidate because it targeted a specific class of failures, created a foundation for future optimization, and allowed us to experiment without waiting on merchant-side or gateway-side changes.
We were building payment optimization features with the goal of increasing approval rates and improving payment recovery outcomes.
We wanted to focus on areas where we had signals that additional treatment could improve approvals. One pattern we had observed was a subset of failures caused by transient network or connectivity issues. These failures were not necessarily true customer declines. In some cases, they appeared to be technical interruptions where a second attempt immediately after the first transaction could succeed.
We also saw inline retry as a foundation for future optimization. Over time, the same capability could potentially support more sophisticated logic, such as modifying payment data between retry attempts or applying different treatments based on failure patterns.
Payment optimization can become an endless list of possible ideas: retry timing, gateway behavior, payment data quality, routing, account updater logic, merchant configuration, issuer behavior, messaging, and recovery workflows.
The key was not just finding ideas. It was choosing the right place to invest.
We needed an opportunity that was: