Funnel Analysis
/funnel-analysis
Claude Code analyzes your funnel step-by-step to identify where visitors are leaving and why. It maps drop-off rates across each stage, compares performance by segment and traffic source, and generates recommendations prioritized by the volume of conversions recoverable at each stage.
What this skill does
Map funnel step-by-step conversion rates and identify the stages with the highest drop-off
Segment funnel performance by traffic source, device type, and user segment to find performance gaps
Identify the root cause of drop-off at each stage using behavioral data and page analysis
Generate optimization recommendations ranked by the number of conversions recoverable per fix
How to use it
Export your funnel data
Pull funnel reports from GA4, Mixpanel, or your analytics tool showing step-by-step conversion rates and drop-off volumes.
Run /funnel-analysis
Share the data with Claude Code and describe each funnel stage (visit, sign-up, activation, purchase) so it can contextualize the drop-offs.
Prioritize and fix
Start with the step that has the highest drop-off volume and the most recoverable conversions based on Claude Code's impact ranking.
Example prompts
$Here's our GA4 funnel for the free trial signup flow. We lose 70% between the pricing page and the sign-up form. What's likely causing this?
$Analyze this e-commerce funnel. We have 12% add-to-cart, 6% reach checkout, and 2% purchase. Where should we focus first?
$Our SaaS onboarding funnel drops 60% at the 'connect your first integration' step. Here's the data. What are the likely causes and fixes?
Who it's for
SaaS growth teams diagnosing where trials drop off before converting to paid
E-commerce teams with low add-to-cart or checkout completion rates
Marketing managers presenting funnel performance and improvement plans to leadership
Frequently asked questions
What funnel data do I need to run this analysis?
Step-by-step conversion rates and the absolute number of users at each stage. If you have segmentation by device, traffic source, or user type, include it. Claude Code can work with basic funnel data and get more specific as more data is available.
How do I know if a drop-off is a UX problem or a traffic quality problem?
Segment the funnel by traffic source. If one source drops off dramatically more than others at the same step, the problem is likely traffic quality or message mismatch. If all sources drop similarly, it's a UX or content issue at that step.
Where do most funnels lose the most conversions?
The highest absolute drop-off is usually between the first visit and the lead capture or sign-up step, because most visitors haven't committed yet. But the highest recoverable drop-off is often deeper in the funnel where users have already shown intent. Claude Code calculates both.
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