====== Why backend revenue should guide ad scaling ======

===== The scaling reflex =====

Most ad scaling decisions are driven by:

  
  * Day-one ROAS
  * Cost per acquisition
  * Front-end revenue
  * Pixel-reported conversions

When these numbers look strong, budgets increase.

When they weaken, campaigns pause.

This reflex ignores backend revenue.

CBSplit was built to anchor scaling decisions in lifecycle truth.

===== Front-end ROAS is incomplete =====

Front-end metrics capture:

  
  * Initial purchase value
  * Immediate upsell revenue
  * Short attribution windows

They do not include:

  
  * Refund-adjusted revenue
  * Subscription rebills
  * Churn timing
  * Chargeback exposure

Scaling based solely on front-end data risks amplifying hidden instability.

===== Backend revenue reveals durability =====

Backend revenue reflects:

  
  * Rebill survival
  * Cohort-based LTV
  * Upsell retention impact
  * Refund containment

Campaigns with stable backend revenue are structurally stronger.

Campaigns dependent on fragile front-end spikes are vulnerable.

CBSplit measures durability before expansion.

===== Refund clusters compound under scale =====

At low volume, refund ratios may appear manageable.

When scaled:

  
  * Refund counts multiply
  * Net revenue shrinks
  * Processor scrutiny increases
  * Risk thresholds approach

Backend weakness becomes financially significant only at scale.

Scaling without backend evaluation accelerates loss.

===== Subscription funnels require longer evaluation =====

In recurring models, profitability depends on:

  
  * First rebill success
  * Early churn velocity
  * Retention stability
  * Retry recovery efficiency

These signals mature after initial acquisition.

Scaling before they stabilize introduces structural bias.

CBSplit aligns scaling speed with lifecycle maturity.

===== Traffic quality determines backend behavior =====

Two campaigns may show identical front-end metrics.

Over time, one may:

  
  * Produce durable subscribers
  * Maintain low refund ratios
  * Generate stable LTV

The other may:

  
  * Experience early cancellations
  * Create refund clusters
  * Reduce net margin

Backend revenue reveals traffic quality differences.

===== Blended dashboards mask scaling risk =====

Ad dashboards often aggregate:

  
  * Revenue across cohorts
  * Refund impact across segments
  * Retention across traffic sources

Blended reporting hides weak segments.

CBSplit segments backend performance before scaling decisions are made.

===== Scaling should follow outcome confirmation =====

Responsible scaling requires:

  
  * Refund window closure
  * Rebill cycle observation
  * Cohort-based LTV validation
  * Processor-safe performance thresholds

Backend revenue confirms structural stability.

Front-end revenue suggests possibility.