Table of Contents
Why more traffic does not fix a broken funnel
The traffic reflex
When funnels underperform, the most common reaction is to add more traffic.
Increase ad spend. Open new sources. Push more users into the top.
This reflex feels logical, but it is usually wrong.
CBSplit was built to expose why.
Traffic amplifies whatever already exists
Traffic does not correct problems.
It multiplies them.
If a funnel has friction, more traffic creates more friction. If a funnel has leakage, more traffic creates more leakage. If a funnel has hidden failures, more traffic creates more failures.
CBSplit treats traffic as an amplifier, not a solution.
Broken funnels fail after the click
Many funnels look healthy at the top.
They show:
- Decent click-through rates
- Acceptable conversion rates
- Stable traffic volume
But they fail where traditional tools stop measuring:
- During checkout
- During retries
- During upsells
- During subscriptions
More traffic only feeds these hidden failure points.
Conversion rate hides structural damage
A funnel can convert while still being broken.
Common examples include:
- High decline rates masked by retries
- Revenue lost to downgraded offers
- Subscriptions that cancel immediately
- Refunds that arrive days later
Conversion rate does not capture these outcomes.
CBSplit was designed to surface them.
Traffic increases cost before it increases revenue
Every additional visitor increases:
- Ad spend
- Payment processing load
- Fraud exposure
- Support volume
If the funnel is inefficient, costs rise faster than revenue.
CBSplit helps fix the funnel before scaling traffic.
Post-purchase behavior determines scalability
Scalable funnels show healthy post-purchase behavior:
- Clean approvals
- Minimal retries
- Strong upsell acceptance
- Stable subscriptions
Broken funnels show:
- Payment friction
- Retry dependence
- Early churn
- High refund rates
More traffic does not change these patterns.
CBSplit measures them directly.
Traffic masks learning
When traffic increases, noise increases.
It becomes harder to see:
- Which offers fail
- Which paths degrade
- Which segments produce low-quality revenue
CBSplit emphasizes learning from smaller, cleaner samples before scaling.
Why CBSplit discourages blind scaling
CBSplit is not optimized for volume.
It is optimized for signal.
It encourages:
- Fixing decline handling first
- Stabilizing retries
- Improving post-purchase outcomes
- Protecting revenue quality
Only then does traffic scaling make sense.