Table of Contents
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.