Tier 4

marketing_funnel

Analyze and optimize the customer journey from awareness to revenue using the AARRR framework

Usage in Claude Code: /marketing_funnel your question here

Marketing Funnel Analysis

Overview

Analyze and optimize the customer journey from awareness to revenue using the AARRR framework

Steps

Step 1: Map the customer journey

Define the stages of your specific funnel:

Standard AARRR stages with typical milestones:

ACQUISITION - How users find you:

  • Website visit
  • Landing page view
  • Content consumption
  • Ad click-through

ACTIVATION - First value experience:

  • Account creation
  • Onboarding completion
  • First key action (varies by product)
  • “Aha moment” reached

RETENTION - Users come back:

  • Day 1 return
  • Week 1 return
  • Month 1 return
  • Sustained usage pattern

REVENUE - Money exchange:

  • Trial to paid conversion
  • First purchase
  • Subscription continuation
  • Upsell/expansion

REFERRAL - Users bring others:

  • Invite sent
  • Referral completed
  • Word of mouth (harder to track)
  • Public advocacy (reviews, social)

Customize stages for your specific business:

  • SaaS: Focus on trial-to-paid and monthly retention
  • E-commerce: Focus on cart completion and repeat purchase
  • Marketplace: Track both sides of the market
  • Consumer app: Focus on daily/weekly active users

Document the specific actions that define each stage transition.

Step 2: Establish baseline metrics

Collect current performance data for each stage:

For each funnel stage, measure:

  • Volume: How many users reach this stage?
  • Conversion: What % move to the next stage?
  • Time: How long does the transition take?
  • Cohort trends: Is it improving or declining?

Key ratios to calculate:

  • Acquisition rate: Visitors / Impressions
  • Activation rate: Activated users / Signups
  • Day 1/7/30 retention: Users returning / Activated users
  • Revenue rate: Paying users / Activated users
  • Referral rate: Referrals made / Active users
  • Viral coefficient: New users from referrals / Existing users

Segment by:

  • Traffic source (organic, paid, referral, direct)
  • User type (B2B vs B2C, enterprise vs SMB)
  • Geography if relevant
  • Time period (look for trends)

Set up tracking if not in place:

  • Implement event tracking for key actions
  • Create funnel reports in analytics tool
  • Build cohort analysis capability
  • Set up conversion tracking by source

Step 3: Benchmark against standards

Compare your metrics to industry benchmarks:

Typical SaaS benchmarks:

  • Website visitor to signup: 2-5%
  • Signup to activation: 20-40%
  • Activation to paid: 2-5% (freemium), 15-25% (free trial)
  • Monthly retention: 95%+ (enterprise), 90%+ (SMB)
  • Annual churn: <5% (enterprise), <15% (SMB)
  • Referral rate: 2-5% of users refer

E-commerce benchmarks:

  • Add to cart rate: 8-15%
  • Cart completion rate: 50-70%
  • Repeat purchase rate: 20-40%
  • Email capture rate: 1-5%

Consumer app benchmarks:

  • Day 1 retention: 25-40%
  • Day 7 retention: 10-20%
  • Day 30 retention: 5-10%
  • Viral coefficient: 0.15-0.25 (good), 0.5+ (viral)

Note: Benchmarks vary significantly by:

  • Industry and product type
  • Price point and sales cycle
  • Target market
  • Business model

Use benchmarks as directional guidance, not absolute targets. Your best benchmark is your own historical performance.

Step 4: Identify the leaky bucket

Find where users drop off most:

Calculate absolute drop-off at each stage:

  • Stage N visitors - Stage N+1 visitors = Drop-off
  • Rank stages by drop-off volume

Calculate relative drop-off:

  • (Expected conversion - Actual conversion) x Volume
  • This shows opportunity size, not just drop-off

Root cause analysis for biggest leaks:

Acquisition leaks:

  • Wrong audience targeting
  • Weak value proposition
  • Poor landing page messaging
  • High bounce rate signals

Activation leaks:

  • Complicated signup process
  • Confusing onboarding
  • Time to value too long
  • Users don’t reach “aha moment”

Retention leaks:

  • Product doesn’t deliver on promise
  • No habit formation
  • No re-engagement triggers
  • Better alternatives found

Revenue leaks:

  • Price-value mismatch
  • Friction in purchase process
  • Free version too generous
  • Wrong timing for upgrade ask

Referral leaks:

  • No referral mechanism
  • No incentive to share
  • Product not shareable
  • Customers not delighted enough

Prioritize fixing the leak with highest volume x conversion gap.

Step 5: Diagnose specific conversion problems

Deep dive into the biggest leak:

Quantitative analysis:

  • Segment drop-off by user characteristics
  • Compare successful vs unsuccessful users
  • Identify correlation patterns
  • Analyze timing and sequence

Qualitative analysis:

  • Session recordings of users who drop off
  • User interviews with churned users
  • Support ticket analysis for friction points
  • Survey users who didn’t convert

Common conversion killers by stage:

Acquisition problems:

  • Targeting: Wrong people seeing your ads/content
  • Messaging: Value proposition unclear or weak
  • Channel: Wrong channels for your audience
  • Competition: Others capturing the demand

Activation problems:

  • Friction: Too many steps to get started
  • Clarity: Users don’t know what to do next
  • Value delay: Too long before first value
  • Technical: Bugs, performance, compatibility

Retention problems:

  • Engagement: Users forget about you
  • Value: Core promise not fulfilled
  • Habits: No trigger for return visits
  • Competition: Better alternatives found

Revenue problems:

  • Timing: Asking too early or too late
  • Pricing: Too high or value unclear
  • Process: Purchase flow has friction
  • Trust: Concerns about commitment

Document specific hypotheses to test.

Step 6: Design optimization experiments

Create experiments to address identified problems:

For each hypothesis, design an experiment:

  • Hypothesis: “If we [change], then [metric] will improve by [amount]”
  • Control: Current experience
  • Variant: Changed experience
  • Sample size: Users needed for significance
  • Duration: How long to run
  • Success criteria: What constitutes a win

Experiment types by stage:

Acquisition experiments:

  • A/B test landing page headlines
  • Test different value propositions
  • Try new acquisition channels
  • Optimize ad creative and targeting

Activation experiments:

  • Simplify signup flow
  • Improve onboarding sequence
  • Reduce time to first value
  • Add progress indicators

Retention experiments:

  • Trigger-based email sequences
  • Push notification timing
  • Feature discovery prompts
  • Habit-forming product changes

Revenue experiments:

  • Pricing page optimization
  • Trial length variations
  • Upgrade prompt timing
  • Payment method options

Referral experiments:

  • Referral incentive types
  • Sharing mechanism placement
  • Referral message optimization
  • Viral loop integration

Prioritize using ICE framework:

  • Impact: How big will the improvement be?
  • Confidence: How sure are we it will work?
  • Ease: How easy is it to implement?

Step 7: Run and measure experiments

Execute experiments systematically:

Before launch:

  • Verify tracking is in place
  • Document starting metrics
  • Set up test and control groups
  • Communicate to relevant teams

During experiment:

  • Monitor for technical issues
  • Don’t peek at results too early (p-hacking)
  • Document anything unusual
  • Maintain test integrity

After experiment:

  • Calculate statistical significance
  • Measure primary and secondary metrics
  • Look for unexpected effects
  • Document learnings

Interpreting results:

  • Win: Variant better with statistical significance
  • Loss: Control better with statistical significance
  • Inconclusive: Not enough signal, need more data
  • Learning: Even losses teach something

Common mistakes to avoid:

  • Stopping too early when you see a positive trend
  • Running too many variants (dilutes sample)
  • Changing experiments mid-flight
  • Not accounting for novelty effects
  • Ignoring secondary metrics that show harm

Step 8: Iterate and compound gains

Build on learnings to accelerate improvement:

After each experiment cycle:

  • Update baseline metrics
  • Recalculate opportunity sizes
  • Reprioritize experiment backlog
  • Apply learnings to new hypotheses

Compounding improvement:

  • Small gains multiply across funnel
  • 10% improvement at 5 stages = 61% overall improvement
  • Focus on sustainable gains, not one-time bumps

Build optimization into culture:

  • Regular experiment review meetings
  • Shared learnings documentation
  • Celebrate learnings, not just wins
  • Track experiment velocity

Set up continuous monitoring:

  • Alert when metrics drop
  • Weekly funnel review
  • Monthly deep dive
  • Quarterly strategy review

Document institutional knowledge:

  • What works in your context
  • What doesn’t work (avoid repeating)
  • Benchmark improvements over time

When to Use

  • When you need to understand why growth is stalling
  • Before investing heavily in acquisition channels
  • When conversion rates seem low but you don’t know where
  • Starting to scale after achieving product-market fit
  • When CAC (Customer Acquisition Cost) is too high
  • Diagnosing why users sign up but don’t convert to paying
  • Setting up growth metrics and dashboards
  • Prioritizing growth experiments

Verification

  • Every funnel stage has measurable conversion metrics
  • Leaks are prioritized by actual opportunity size, not assumptions
  • Experiments have clear hypotheses and success criteria
  • Results are statistically significant before action
  • Learnings are documented and applied to future experiments
  • Metrics show improvement trend over time

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