====== Why affiliates kill profitable offers too early ======

===== The impatience problem =====

Affiliates operate under pressure.

Ad spend moves fast.
Cash flow fluctuates.
Dashboards update in real time.

When numbers dip, offers are paused.
When EPC drops, traffic is cut.
When refunds rise slightly, panic begins.

CBSplit was built to separate short-term noise from long-term profit.

===== Early signals are often incomplete =====

Offers are usually evaluated using:

  
  * Conversion rate
  * EPC
  * Day-one ROAS
  * Initial refund rate

These metrics reflect early behavior.

They do not reflect:

  
  * Subscription rebills
  * Long-term LTV
  * Traffic learning curves
  * Funnel stabilization

Affiliates often kill offers before these signals mature.

===== Traffic needs calibration time =====

New campaigns require adjustment.

Early traffic may include:

  
  * Mismatched audiences
  * Creative misalignment
  * Learning-phase instability
  * Platform optimization noise

Performance during calibration rarely represents steady-state reality.

CBSplit tracks behavior over time, not just at launch.

===== Refund patterns stabilize slowly =====

Refund rates are not instant indicators.

They:

  
  * Appear after product consumption
  * Reflect expectation alignment
  * Stabilize across cohorts

An offer may show early refund spikes that normalize later.

Killing the offer early prevents learning.

CBSplit evaluates refund-adjusted performance by cohort maturity.

===== Rebills reveal hidden strength =====

Some offers:

  
  * Convert modestly
  * Produce average front-end numbers
  * Show slow early momentum

But generate strong rebill retention.

These offers look weak in short windows.

They look strong in extended windows.

CBSplit gives rebills time to reveal value.

===== Affiliates optimize for speed, not durability =====

Under pressure, affiliates favor:

  
  * Fast decisions
  * Immediate ROAS
  * Short evaluation windows

Durable revenue requires:

  
  * Patience
  * Cohort tracking
  * Refund reconciliation
  * Rebill observation

CBSplit encourages outcome-based patience.

===== Early volatility is not permanent weakness =====

Many profitable offers experience:

  
  * Initial performance swings
  * Cost fluctuations
  * Conversion instability
  * Traffic experimentation effects

Volatility often declines as systems stabilize.

Killing offers during volatility locks in losses.

CBSplit differentiates volatility from structural failure.

===== Scaling requires evidence, not emotion =====

Affiliates often react emotionally to:

  
  * A bad day
  * A refund spike
  * A temporary EPC drop

Short-term pain does not always signal long-term unprofitability.

CBSplit evaluates offers using:

  
  * Net revenue after refunds
  * Cohort-based LTV
  * Payment stability
  * Subscription durability

This reduces premature termination.