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====== Why revenue attribution breaks after the first sale? ======

{{ :std-attrib-sale.png?nolink&600 |}}

Traditional affiliate attribution assumes revenue is fully understood at the moment of the first sale.

CBSplit exists because this assumption fails in real subscription and rebill-driven businesses.

Attribution does not end at the first sale.
Reality begins after it.

===== The traditional attribution model =====

Most affiliate systems follow a simple model:

  * Ad → Click
  * Click → Sale
  * Sale → Commission

Once the first transaction is recorded, attribution is considered complete.

This model assumes revenue is immediate, final, and stable.

That assumption is incorrect.

===== What happens after the first sale =====

In real-world funnels, the first sale is only an entry point.

Revenue after the sale is affected by:

  * Refunds and chargebacks
  * Trial-to-paid conversion failures
  * Upsells and downsells
  * Subscription churn
  * Failed rebills
  * Payment processor declines
  * Delayed cancellations

Standard attribution systems do not observe these events correctly.

===== Why attribution breaks =====

Revenue attribution breaks because it is:

  * Front-loaded
  * Time-blind
  * Outcome-agnostic

Affiliates are often credited and paid on Day 0, while revenue reality unfolds over weeks or months.

When refunds or churn occur later, attribution is not corrected.

===== The attribution mismatch =====

Standard attribution answers:

Who caused the first transaction?

CBSplit asks:

Who generated durable, net-positive revenue?

This mismatch causes:

  * Overpayment to low-quality traffic
  * Undetected refund-heavy sources
  * Misleading campaign performance data
  * Scaling of unprofitable funnels

===== What standard dashboards fail to show =====

Most dashboards display:

  * Gross revenue
  * Initial conversions
  * Affiliate payout totals

CBSplit exposes what is missing:

  * Revenue decay over time
  * Rebill survival curves
  * Source-level refund ratios
  * Net revenue after all adjustments
  * Long-term traffic quality trends

Attribution appears correct until these layers are examined.

===== CBSplit’s approach to attribution =====

CBSplit treats attribution as a **time-based system**, not a single event.

It aligns credit with:

  * Revenue actually collected
  * Revenue that survives refund windows
  * Subscription continuity
  * Net profitability per source

Attribution is continuously re-evaluated as revenue evolves.

===== Why first-sale attribution creates false winners =====

Traffic sources that perform well on Day 0 often fail long-term.

Common patterns include:

  * High initial conversions
  * High refund rates
  * Poor rebill retention
  * Rapid revenue decay

Without post-sale attribution, these sources appear profitable when they are not.


This is why revenue attribution breaks after the first sale.