When people hear “split,” they assume:

* Automatic A/B testing
* Variant creation
* Statistical winner declaration
* Auto-traffic shifting

CBSplit does not do this.

And that is intentional.

Classic split testing tools:

* Inject scripts into pages
* Modify headlines or layouts
* Track event-level differences
* Declare winners based on short-term metrics

CBSplit operates at a different layer.

It evaluates revenue systems, not page elements.

Creating variants and sending traffic to them is simple.

Understanding:

* Refund-adjusted performance
* Rebill durability
* Cohort-based LTV
* Processor-safe scaling

Is not simple.

CBSplit focuses on outcome intelligence, not variant automation.

Auto-running split tests can:

* Shift traffic based on early signals
* Reward impulse-driven conversions
* Scale fragile revenue
* Amplify refund risk

Without lifecycle visibility, automation accelerates mistakes.

CBSplit requires lifecycle maturity before decisions are made.

CBSplit does not:

* Change your headlines
* Modify your button colors
* Inject DOM experiments
* Optimize surface-level elements

It provides:

* Refund-adjusted metrics
* Rebill-aware analysis
* Cohort stability tracking
* Outcome-based segmentation

It measures what matters after the experiment ends.

Split testing is a tactical tool.

CBSplit is infrastructure for:

* Revenue durability
* Lifecycle evaluation
* Traffic-quality filtering
* Risk-aware scaling

It supports testing decisions. It does not replace strategic judgment.

CBSplit does not remove human decision-making.

It provides:

* Clear outcome signals
* Lifecycle-adjusted data
* Segmented performance insights
* Risk indicators

Operators still design experiments. CBSplit ensures experiments are judged correctly.

Many systems promise:

* Auto-optimization
* Automatic winners
* Hands-free scaling

These systems optimize events.

CBSplit ensures that optimization aligns with net revenue truth.