Checkout abandonment costs e-commerce billions annually. Often, it comes down to payment friction.
When customers reach checkout and don’t see their preferred payment option prominently displayed, many simply leave. The challenge isn’t offering every payment method—it’s showing the right one first.
understanding the problem
A fintech platform powering checkout for hundreds of merchants wanted to optimize conversion. Their data showed that customers who had to scroll or search for their payment method converted at significantly lower rates than those who saw their preferred option immediately.
The complexities included:
- Regional payment preferences varying dramatically
- Device and context affecting choice patterns
- Repeat customers with established preferences
- New customers with no historical data
building prediction models
We developed models that predict payment preference based on available signals: geography, device type, cart contents, time of day, and behavioral patterns from the session.
Our approach involved:
- Feature engineering from transaction and session data
- Ensemble models balancing accuracy with latency
- Real-time inference at checkout render
- Continuous learning from actual selections
conversion gains
The optimized checkout flow increased payment completion rates by 12% across the platform. Mobile conversions saw even stronger gains at 18%, where screen real estate makes ordering crucial. Merchants saw measurable revenue increases without any changes to their own systems.
