Table of Contents
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.