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====== Why continuity offers distort performance data ======

===== The recurring revenue illusion =====

Continuity offers promise predictable revenue.

Monthly billing.
Automatic renewals.
Growing lifetime value.

On dashboards, they often look strong.

But continuity models distort performance data in ways most marketers overlook.

CBSplit was built to untangle this distortion.

===== Front-end metrics hide backend variability =====

Continuity funnels often show:

  
  * Strong initial conversions
  * Acceptable cost per acquisition
  * Positive day-one ROAS

These metrics ignore:

  
  * First rebill survival
  * Early churn velocity
  * Refund timing
  * Failed payment recovery

Revenue in continuity offers is distributed over time.

Front-end data captures only the first fragment.

===== Rebills create delayed performance signals =====

True profitability depends on:

  
  * How many customers reach second billing
  * How long subscriptions persist
  * Whether retries recover failed payments
  * How churn stabilizes across cohorts

Early reports do not include these signals.

Continuity revenue appears healthier than it truly is.

===== Refund windows overlap billing cycles =====

In many models:

  
  * Refund eligibility extends into subscription periods
  * Customers may cancel after first rebill
  * Chargebacks may occur weeks later

This overlap creates performance distortion.

Gross revenue temporarily exceeds net stability.

CBSplit recalculates results after refund resolution.

===== Average LTV masks cohort fragility =====

Continuity dashboards often report:

  
  * Average lifetime value
  * Blended churn rate
  * Aggregate retention metrics

These averages conceal:

  
  * Traffic-specific churn spikes
  * Geography-based retention gaps
  * Messaging-driven refund clusters

One unstable segment can distort the entire model.

CBSplit preserves segmentation to reveal structural risk.

===== Aggressive scaling amplifies distortion =====

When continuity offers scale quickly:

  
  * Early revenue spikes
  * Backend churn accumulates
  * Refund ratios increase
  * Processor scrutiny intensifies

Short-term scaling decisions may rely on incomplete data.

Lifecycle maturity has not yet stabilized.

===== Retry logic creates artificial stability =====

Payment retries can:

  
  * Recover failed transactions
  * Delay churn visibility
  * Inflate apparent retention

Retry-dependent revenue is fragile.

It may collapse if retry efficiency changes.

CBSplit distinguishes durable rebills from recovery-driven revenue.

===== Continuity requires longer evaluation horizons =====

Accurate performance measurement requires:

  
  * Multiple billing cycles
  * Refund window closure
  * Cohort aging
  * Traffic-quality filtering

Short evaluation windows distort conclusions.

Continuity offers require lifecycle-aware analysis.