/digital-marketing-pro:funnel-audit

Script location. If your host does not set ${CLAUDE_PLUGIN_ROOT}, the scripts are in this plugin's scripts/ folder, next to skills/. Run them with Python 3.10 or newer: python, or python3 where python is not found (macOS, many Linux systems).

Purpose

Analyze the complete customer acquisition and conversion funnel to identify where prospects drop off, why they disengage, and what changes will have the highest impact on overall conversion rate.

Input Required

The user must provide (or will be prompted for):

• Funnel stages: The stages to analyze (or use standard: Awareness > Interest > Consideration > Intent > Purchase > Retention)
• Funnel data: Metrics per stage (traffic, leads, MQLs, SQLs, opportunities, customers) or qualitative description
• Traffic sources: Where visitors/leads originate
• Conversion points: Key actions at each stage (form fill, demo request, trial start, purchase)
• Known pain points: Any stages the user already suspects are underperforming
• Tech stack: CRM, analytics, and marketing automation tools in use

Process

1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply brand voice, compliance rules for target markets (skills/context-engine/compliance-rules.md), and industry context. Also check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json — if present, load restrictions and relevant category files. Check for custom templates at ~/.claude-marketing/brands/{slug}/templates/. Check for agency SOPs at ~/.claude-marketing/sops/. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults.
2. Map the current funnel with conversion rates between each stage
3. Benchmark stage-to-stage conversion rates against industry averages
4. Identify the biggest drop-off points and calculate revenue impact of each gap
5. Analyze potential causes per bottleneck: messaging, targeting, UX, timing, offer, follow-up
6. Evaluate lead quality signals — are the right people entering the funnel?
7. Assess nurture effectiveness at each stage
8. Model improvement scenarios: "If stage X improves by Y%, overall revenue increases by Z%"
9. Prioritize recommendations by revenue impact and implementation effort
10. Size and validate the fix: For the top recommendation, size the validating experiment with python "${CLAUDE_PLUGIN_ROOT}/scripts/sample-size-calculator.py" --baseline-rate {stage-rate} --mde {mde} --mde-type absolute --significance 0.95 --power 0.80 (pass --mde-type relative if the target is a relative lift — the two differ by ~40× at a 5% baseline). Once the fix has run, confirm the improvement is statistically real with python "${CLAUDE_PLUGIN_ROOT}/scripts/significance-tester.py" --control-visitors {n} --control-conversions {n} --variant-visitors {n} --variant-conversions {n} --confidence 0.95 rather than declaring a winner off raw rate deltas.

Output

A structured funnel audit containing:

• Funnel visualization with conversion rates per stage
• Industry benchmark comparison per stage
• Top 3 bottlenecks ranked by revenue impact
• Root cause analysis per bottleneck with supporting evidence
• Improvement scenarios with projected revenue impact
• Prioritized action plan with quick wins and strategic projects
• Measurement framework to track improvements

Agents Used

• marketing-strategist — Funnel architecture, lead quality analysis, strategic recommendations
• analytics-analyst — Conversion data analysis, benchmarking, impact modeling
• cro-specialist — Conversion bottleneck diagnosis, A/B test recommendations, form and checkout optimization, statistical significance testing