Most optimization tools are built around experimentation.

Run a test. Change a variable. Wait for statistical significance. Declare a winner.

This mindset works in controlled environments.

Revenue systems are not controlled environments.

CBSplit was built with this reality in mind.

Classic experiments assume:

• Stable traffic sources
• Consistent user behavior
• Predictable systems
• Clean attribution

At scale, these assumptions break.

Payments fluctuate. Traffic quality shifts. External systems fail.

CBSplit does not depend on stability to function.

At low volume, small changes appear meaningful.

At scale, what matters is:

• How systems behave under load
• How retries perform
• How refunds accumulate
• How subscriptions survive

These are not experimental questions.

They are operational ones.

CBSplit focuses on operational truth.

Experiments isolate components.

Scale tests interactions.

At scale:

• Retry logic interacts with traffic quality
• Upsells interact with refunds
• Subscription churn interacts with acquisition promises

CBSplit observes these interactions continuously.

Traditional experiment tools struggle when:

• Traffic shifts rapidly
• Funnels are modified frequently
• Outcomes are delayed
• External dependencies fail

They require resets, re-baselining, and clean test windows.

CBSplit is designed to operate without resets.

At scale, optimizing events is dangerous.

What matters is:

• Net revenue
• Payment stability
• Refund containment
• Processor health

CBSplit optimizes for these outcomes continuously, not in test windows.

Instead of isolated experiments, CBSplit enables:

• Continuous learning
• Outcome-based routing
• Real-time risk awareness
• Long-term revenue protection

This is how systems survive at scale.

CBSplit does not reject experimentation.

It rejects the idea that experiments are the foundation of revenue systems.

At scale, systems must:

• Adapt constantly
• Absorb failure
• Handle uncertainty
• Protect revenue

CBSplit was built for this environment.