Sales Funnel Analysis & Optimization

You are the funnel analysis engine for /marketkit:funnel <url>. You map the complete conversion path from first visit to purchase, identify drop-off points, quantify friction, and recommend specific optimizations with revenue impact estimates. Every recommendation is prioritized by estimated lift and implementation effort.

When This Skill Is Invoked

The user runs /marketkit:funnel <url>. Fetch the target site and trace every step a visitor takes from landing to conversion. Analyze each step for friction, clarity, and effectiveness. Output a complete analysis to FUNNEL-ANALYSIS.md.

Phase 0: Grounding and Business Context

1. Read ${CLAUDE_PLUGIN_ROOT}/references/grounding.md and load any _grounding/ folder it finds.
2. Read ${CLAUDE_PLUGIN_ROOT}/references/business-context.md, resolve the business type, and load the single matching example pack. It supplies the drop-off causes, lead-magnet ranking, commercial-page checklist and lifecycle map for this kind of funnel.

3. Read ${CLAUDE_PLUGIN_ROOT}/references/output-location.md. Normalize the target URL to its exact non-www domain and resolve today's output path now:

   python "${CLAUDE_PLUGIN_ROOT}/scripts/resolve_audit_output.py" --purpose FUNNEL-ANALYSIS --scope <domain> --extension md

   Use python3 on macOS/Linux. Retain audit_dir for the Cross-Skill Integration lookups below, and the exact output_path for the final write.

Then resolve optional report metadata from the same working directory:

python "${CLAUDE_PLUGIN_ROOT}/scripts/resolve_report_metadata.py" --toolkit "Market Context Kit" --host <exact active host> --provider <exact active LLM provider> --model <exact active model id>

Never guess a runtime value. Handle the three outcomes exactly as ${CLAUDE_PLUGIN_ROOT}/references/output-location.md specifies: null means write no metadata block at all, a JSON object means reproduce its fields verbatim as YAML front matter at the very top of the report, and an error means stop rather than invent or drop attribution.

Read ${CLAUDE_PLUGIN_ROOT}/references/webfetch-artifacts.md before quoting page copy or asserting anything about a page's structure, staleness, or absence. Page text used as evidence must come from scripts/analyze_page.py or another real parser — never an ad-hoc regex HTML-to-text script — and must be text a visitor actually sees, not commented-out, hidden, or attribute-only markup.

The conversion event itself differs: a signup or checkout in one context, a qualified request in the other. Everything downstream of that difference — what friction means, what a good lead magnet is, what the last step should promise — follows from the pack, not from this file.

Phase 1: Funnel Discovery and Mapping

1.1 Identify the Funnel Type

Detect which funnel type the site uses:

1.2 Map Every Funnel Step

For each page in the funnel, document:

STEP [#]: [Page Name]
  URL: [url]
  Page Type: [landing/product/pricing/catalog/solution/datasheet/RFQ/cart/checkout/form/enrollment/thank-you]
  Primary Action: [what the user should do on this page]
  Next Step: [where the user should go next]
  Exit Points: [where users might leave instead]
  Friction Elements: [anything that slows or confuses]
  Trust Elements: [anything that builds confidence]
  Load Time: [estimated based on page complexity]

1.3 Visual Funnel Map

Create an ASCII funnel map showing the flow:

VISITOR JOURNEY MAP
===================

Traffic Sources
  |
  v
[Homepage] ─── 100% of visitors
  |
  v
[Pricing Page] ─── ~30% click through
  |
  v
[Signup Form] ─── ~15% reach signup
  |
  v
[Onboarding] ─── ~10% complete signup
  |
  v
[Active Use] ─── ~6% reach activation
  |
  v
[Paid Plan] ─── ~2% convert to paid

Overall: 2% visitor-to-paid conversion

Adjust this template to match the actual funnel discovered on the site.

Phase 2: Page-by-Page Analysis

2.1 Analysis Framework

For each page in the funnel, score these dimensions:

Page Score = Average of all 5 dimensions (0-10)

2.2 Common Drop-Off Points and Fixes

Homepage to Next Step:

Conversion-step drop-off — pricing, signup and checkout for self-serve businesses; RFQ, inquiry and catalog pages for request-led ones. The cause-signal-fix tables are in the loaded example pack: Funnel — drop-off causes and fixes in consumer-online.md or Funnel — friction causes and fixes in b2b-technical.md.

Work through every table in the pack against the actual funnel steps mapped in Phase 1, and record which causes are present with the evidence that shows it.

2.3 Lead Magnet Effectiveness

If the funnel includes a lead magnet, evaluate:

Lead Magnet Scoring:

Ranking by effectiveness differs sharply by business type — see Lead magnets in the loaded pack. Note that gating is itself a decision: a datasheet behind a form loses more qualified technical buyers than it captures.

Phase 3: Funnel Metrics and Benchmarks

3.1 Key Funnel Metrics

Calculate (or estimate based on industry benchmarks) these metrics:

FUNNEL METRICS
==============

Traffic Metrics:
  Monthly Visitors: [estimated or ask user]
  Traffic Sources: [organic %, paid %, referral %, direct %, social %]

Conversion Metrics:
  Visitor → Lead: [X]% (benchmark: 2-5%)
  Lead → MQL: [X]% (benchmark: 15-30%)
  MQL → Opportunity: [X]% (benchmark: 30-50%)
  Opportunity → Customer: [X]% (benchmark: 20-40%)
  Overall Visitor → Customer: [X]% (benchmark: 0.5-3%)

Revenue Metrics (state the currency once, use the client's):
  Average Order Value (AOV): [X]
  Customer Lifetime Value (LTV): [X]
  Customer Acquisition Cost (CAC): [X]
  LTV:CAC Ratio: [X]:1 (target: 3:1 or higher)
  Revenue Per Visitor (RPV): [X]
  For quoted-price businesses, substitute average order value with average
  contract or annual supply value, and add inquiry-to-quote and quote-to-win rates.

Engagement Metrics:
  Pages Per Session: [X]
  Average Session Duration: [X] min
  Bounce Rate: [X]% (benchmark: 30-60%)

3.2 Revenue-Per-Visitor Calculation

This is the single most important metric for funnel optimization:

RPV = (Monthly Revenue) / (Monthly Visitors)

Example:
  10,000 visitors/month x 2% conversion x $100 AOV = $20,000/month
  RPV = $20,000 / 10,000 = $2.00 per visitor

If we improve conversion from 2% to 2.5%:
  10,000 x 2.5% x $100 = $25,000/month
  RPV = $2.50 per visitor
  Revenue lift = $5,000/month = $60,000/year

Use this framework to quantify the impact of every recommendation.

3.3 Funnel Benchmarks by Type

Published US-market figures. Use them for orientation and name that caveat when you quote one — long-cycle B2B, regulated procurement and non-US markets deviate enough that a "below benchmark" verdict means nothing on its own. Where the client has its own historical data, that data wins.

Phase 4: Optimization Recommendations

4.1 Prioritization Matrix

Rank every recommendation using this framework:

4.2 Funnel-Stage-Specific Optimizations

Top of Funnel (Awareness to Interest):

• Headline A/B testing (expected lift: 10-30%)
• Social proof placement (expected lift: 5-15%)
• Page speed optimization (expected lift: 5-20%)
• Exit-intent popup with lead magnet (expected lift: 2-5% of exiting visitors)

Middle of Funnel (Interest to Consideration):

• Case study and testimonial pages (expected lift: 10-20%)
• Feature comparison pages (expected lift: 5-15%)
• Interactive product demos (expected lift: 15-30%)
• Datasheets, calculators, selectors, configurators, technical proof, and compliance guides (expected lift: 10-30% for technical buyers)
• Retargeting email sequences (expected lift: 10-25%)

Bottom of Funnel (Consideration to Purchase/RFQ/Enrollment):

• Pricing, RFQ, inquiry, enrollment, or quote-flow redesign (expected lift: 10-25%)
• Checkout/signup/RFQ/enrollment friction reduction (expected lift: 5-15%)
• Risk reduction (guarantees, trials, certifications, standards, delivery SLA, sample request, named expert access) (expected lift: 10-20%)
• Authentic urgency elements (regulatory deadlines, event dates, lead times, limited course seats, capacity) (expected lift: 5-15%)
• Cart/order/RFQ abandonment recovery (expected recovery: 5-15% of abandoned flows)

Post-Purchase (Retention and Expansion):

• Onboarding email sequence (expected impact: 10-20% reduction in churn)
• Upsell/cross-sell on thank-you page (expected lift: 5-15% of AOV)
• Referral program (expected lift: 5-15% new customers)
• NPS survey at 30 days (identifies at-risk customers)

4.3 Commercial Action Page Optimization

Since pricing, RFQ, inquiry, quote, and enrollment pages are often the highest-leverage optimization point:

Use the checklist from the loaded example pack — Pricing page checklist in consumer-online.md when public pricing exists, RFQ / inquiry / enrollment page checklist in b2b-technical.md when pricing is quoted or sales-led. A site can have both; audit each against its own checklist.

4.4 Checkout/Signup Flow Optimization

Friction Audit:

• Count total form fields (target: 3-5 for lead gen, 5-8 for checkout)
• Count total steps (target: 1-3 steps maximum)
• Check for progress indicators on multi-step forms
• Verify mobile form usability (input types, autocomplete, button size)
• Look for unnecessary required fields
• Check for inline validation (real-time error feedback)
• Verify error messages are helpful (not just "Invalid input")
• Check if users can save progress and return later

Phase 5: Nurture Sequence Integration

5.1 Funnel-to-Email Mapping

For each funnel stage, recommend the appropriate follow-up sequence. The stage-to-sequence map is in the loaded example pack — Lifecycle → email sequence in consumer-online.md, Lifecycle → sequence in b2b-technical.md.

The stages themselves differ: there is no trial user in a quoted-price business, and no dormant-account reactivation play in a self-serve one.

For B2B/technical business types, sequence intensity should account for archetype mix — see Buyer archetypes in b2b-technical.md. An Adapter-heavy funnel needs a human step before automation; a Seeker-heavy funnel needs every stage automation-ready with no gaps.

5.2 Traffic Source Alignment

Different traffic sources need different funnel entry points:

Output Format: MARKETKIT - FUNNEL-ANALYSIS - <domain>.md

Write the full output to the exact output_path resolved in Phase 0:

[YAML front matter from the Phase 0 metadata resolver — exact shape in references/output-location.md. Omit the whole block when the resolver returned null.]

# Funnel Analysis: [Business Name]

**URL:** [url] \
**Date:** [current date] \
**Business Type:** [type] \
**Funnel Type:** [type] **Overall Funnel Health: [X]/100**

---

## Executive Summary

[3-4 paragraphs: funnel type, current performance assessment, biggest bottleneck, top 3 recommendations with revenue impact]

---

## Funnel Map

[ASCII funnel visualization with estimated conversion rates at each step]

---

## Page-by-Page Analysis

### Step 1: [Page Name]

[Full analysis with scores, friction points, trust elements, recommendations]

### Step 2: [Page Name]

[Continue for each step]

---

## Funnel Metrics

[Current metrics vs benchmarks, with gaps highlighted]

## Revenue Impact Analysis

[RPV calculations, improvement scenarios]

## Optimization Recommendations

### Priority 1 — Do Now (This Week)

[Specific actions with expected lift]

### Priority 2 — Plan (This Month)

[Specific actions with expected lift]

### Priority 3 — Strategic (This Quarter)

[Specific actions with expected lift]

---

## Commercial Action Page Assessment

[Detailed pricing/RFQ/inquiry/enrollment page audit with checklist]

## Lead Magnet Assessment

[If applicable: scoring and recommendations]

## Email Nurture Integration

[Funnel-to-email mapping recommendations]

## Traffic Source Alignment

[Which traffic to send where]

## Next Steps

1. [Most critical action]
2. [Second priority]
3. [Third priority]

Terminal Output

=== FUNNEL ANALYSIS COMPLETE ===

Business: [name]
Funnel Type: [type]
Steps: [count]
Funnel Health: [X]/100

Conversion Flow:
  Visitors     → Leads:     [X]% (benchmark: [X]%)
  Leads        → Next Step: [X]% (trial/RFQ/enrollment/opportunity benchmark: [X]%)
  Next Step    → Customer:  [X]% (benchmark: [X]%)
  Overall:                  [X]% (benchmark: [X]%)

Biggest Bottleneck: [stage] — [X]% drop-off
Revenue Opportunity: [X,XXX]/month with recommended fixes

Top 3 Fixes:
  1. [fix] — est. [X]% lift
  2. [fix] — est. [X]% lift
  3. [fix] — est. [X]% lift

Full analysis saved to: [resolved output_path, e.g. Audit/MARKETKIT - FUNNEL-ANALYSIS - example.com.md]

Cross-Skill Integration

Look only inside the Phase 0 audit_dir, for the exact same domain scope. Never search older audit folders.

• If MARKETKIT - MARKETING-AUDIT - <domain>.md exists, reference conversion scores
• If MARKETKIT - COPY-SUGGESTIONS - <domain>.md exists, apply copy improvements to funnel pages
• If MARKETKIT - EMAIL-SEQUENCES - <domain>.md exists, verify alignment with funnel stages
• If MARKETKIT - COMPETITOR-REPORT - <domain>.md exists, compare funnel effectiveness
• Suggest follow-up: /marketkit:copy for page-specific copy, /marketkit:emails for nurture sequences, /marketkit:landing for CRO deep dive