Funnel Planner

Part of the Problem → Solution → Communicate framework. Sets the foundation — defining funnel stages, targets, and economics that all initiatives measure against.

Help users define conversion funnels, set data-driven targets, validate goals, implement tracking, and calculate unit economics.

Before Starting

Check if the user has outputs from upstream skills:

• Diagnosis output (mkt-diagnosis ) — If available, use the identified root cause and bottleneck to focus the funnel on the right stage. Don't build a full-funnel plan when the problem is isolated.

• Existing initiatives (mkt-initiative-planner ) — If available, align funnel targets to initiative goals so metrics and initiatives stay connected.

If neither exists, proceed normally — the funnel plan stands on its own.

Philosophy

Growth without measurement is gambling. Good growth plans have:

• Outcome focus - Measure results, not activities

• Data grounding - Targets based on baselines and benchmarks, not wishful thinking

• Clear tracking - Every metric has a defined measurement method

• Sound economics - CAC, LTV, and ROI calculated upfront

• Validation - Targets stress-tested before commitment

This skill answers three questions:

• What targets? - Set metrics for each funnel stage

• Why these targets? - Ground in baselines and benchmarks

• How to track? - Implement measurement infrastructure

Mode Detection

Determine the operating mode based on user input:

Plan Mode (Define funnel + set targets with validation):

• User says "plan a funnel", "set targets", "create OKRs", "plan quarterly goals"

• User has a business but needs funnel definition and targets

• User wants help choosing metrics and benchmarks

• User says "validate my targets", "review my OKRs"

Tracking Mode (Implementation):

• User says "track funnel metrics", "set up attribution", "implement UTMs"

• User has targets but needs measurement infrastructure

• User wants help with GA4, events, or attribution models

Economics Mode (ROI calculation):

• User says "calculate CAC", "what's my CPL", "calculate ROI", "budget allocation"

• User wants to understand unit economics

• User needs to justify or optimize marketing spend

If unclear, ask: "Do you want to (1) define your funnel and set targets, (2) implement tracking, or (3) calculate costs and ROI?"

Plan Mode

Use this when helping users define funnels and set validated targets.

Plan Mode Overview

Context -> Funnel Definition -> Baseline Collection -> Target Setting -> Validation

Step 1: Context Gathering

Goal: Understand the business context before setting targets.

Ask (using AskUserQuestion):

Business Context

• What type of product/business? (SaaS, mobile app, Web3, e-commerce, etc.)

• What stage? (Pre-launch, early traction, growth, mature)

• What's the planning horizon? (Quarterly, half-year, annual)

• How many people/teams will work toward these goals?

Strategic Priorities

• What are the top 1-2 company priorities this period?

• Are there constraints? (budget, headcount, runway)

• What happened last period? (wins, misses, learnings)

Step 2: Choose a Funnel Model

Ask: "What type of business are you running? This determines which funnel model fits best."

Model Best For Stages

AARRR (Pirate Metrics) SaaS, apps, subscription Acquisition -> Activation -> Retention -> Revenue -> Referral

AARRR-R (Extended) SaaS/apps with churn focus Acquisition -> Activation -> Retention -> Revenue -> Referral -> Reactivation

AIDA E-commerce, D2C, one-time purchase Awareness -> Interest -> Desire -> Action

TOFU-MOFU-BOFU B2B, content marketing, long sales cycles Top (Awareness) -> Middle (Consideration) -> Bottom (Decision)

Custom Unique business models User-defined stages

When to use AARRR-R (extended model):

• You have measurable churn (users leave and can potentially return)

• Win-back campaigns are part of your growth strategy

• Retention economics matter (typically cheaper to reactivate than acquire new)

See references/funnel-models.md for detailed breakdowns.

Step 2.5: Identify User Segments (Optional but Recommended)

Key Question: "Does your business have distinct user segments with different primary actions?"

Different user archetypes often need fundamentally different funnels. A single funnel obscures critical differences.

Signs you need segment-based funnels:

Signal Example

Different primary actions Creators vs. consumers, sellers vs. buyers

Different value exchange Paying customers vs. free users

Different retention definitions Subscription-based vs. transaction-based

Two-sided dynamics Supply side vs. demand side

Common segment patterns:

Business Type Segment A Segment B

SaaS Buyers (decision makers) Users (daily users)

Marketplace Sellers (supply) Buyers (demand)

Platform Creators (content) Consumers (engagement)

Financial Active users (transactors) Passive users (holders)

If segments exist, for each segment define:

Dimension Segment A Segment B

Primary action [What they mainly do] [What they mainly do]

Retention definition [What counts as retained] [What counts as retained]

Churn definition [When they've churned] [When they've churned]

Success metric [Primary KPI] [Primary KPI]

Cross-segment opportunity: Users who exist in multiple segments typically have 1.5-2x higher LTV. Track cross-segment rate and encourage cross-segment adoption.

See references/segment-based-funnels.md for detailed framework.

Step 3: Define Stage Metrics

For each stage, define:

Stage Definition Primary Metric Secondary Metrics

[Stage name] [What qualifies entry] [Main KPI] [Supporting metrics]

Example for B2B SaaS (AARRR):

Stage Definition Primary Metric Secondary Metrics

Acquisition Visit website Unique visitors Traffic sources, bounce rate

Activation Complete signup + first value action Activation rate Time to activate, feature adoption

Retention Return after Day 7 D7/D30 retention Session frequency, feature stickiness

Revenue Convert to paid Conversion rate ARPU, plan distribution

Referral Invite others Referral rate Viral coefficient, NPS

Extended stages for AARRR-R model (optional):

Stage Definition Primary Metric Secondary Metrics

Retain Keep active users engaged Churn rate, NRR Health score, at-risk %

Reactivate Win back churned users Reactivation rate Win-back CAC, return LTV

See references/lifecycle-states.md for detailed user state definitions.

Step 4: Collect Baselines

Critical step. Targets without baselines are guesses.

Ask:

Current Metrics For each funnel stage, what are your current numbers?

• Awareness: Current reach, impressions, traffic?

• Acquisition: Current signup rate, CAC, conversion rate?

• Activation: Current activation rate, time-to-value?

• Retention: Current D7/D30 retention, churn rate?

• Revenue: Current ARPU, LTV, MRR/ARR?

• Referral: Current referral rate, viral coefficient?

Historical Trends

• What's been the MoM or QoQ trend?

• What drove improvements or declines?

• Are there seasonal patterns?

If user lacks baseline data:

• Use industry benchmarks as starting points

• See references/benchmarks.md for comprehensive data

• Note that benchmarks are starting points, not guarantees

Step 4b: Health Score Assessment (Optional)

For retention-focused funnels, establish user health scoring:

Health Score Components

• How do you measure user engagement? (recency, frequency, depth)

• What behaviors predict churn or retention?

• Do you track feature adoption?

• What risk signals do you monitor?

If the user has mature tracking, help them build a health score model:

Factor Weight How You'll Measure

Recency 25% Days since last activity

Frequency 20% Sessions per period

Feature Adoption 20% % of features used

Tenure 15% Account age

Engagement Trend 10% Week-over-week change

Value Delivered 10% Outcomes achieved

See references/health-scoring.md for complete scoring methodology.

Step 4c: Cohort Analysis Setup (Optional)

Define how you'll segment users for analysis:

Dimension Your Definition

Temporal Signup week/month

Acquisition Channel or campaign

Behavioral Feature used in first 7 days

Value Pricing tier or LTV band

User Segment, geography, or role

See references/cohort-analysis.md for cohort frameworks.

Step 5: Set Targets with Benchmarks

Use this priority order for setting targets:

Historical baseline + improvement goal

• "We're at 8% -> target 12% (50% improvement)"

• Best when you have reliable data

Industry benchmark + confidence adjustment

• "Industry median is 15% -> target 75th percentile (20%)"

• Use when comparing against peers

Reference table fallback

• See benchmark tables in references

• Use when no historical or research data available

Target Formula:

Target = Baseline x (1 + Improvement Factor)

Improvement factors by scenario:

Scenario Improvement Factor Rationale

No optimization yet 20-30% Low-hanging fruit exists

Basic optimization done 10-15% Incremental gains

Mature funnel 5-10% Diminishing returns

Major redesign 30-50% Step change potential

Build Target Table:

Stage Metric Baseline Benchmark Target Why Owner Confidence

[Stage] [Metric] [Current] [Industry] [Goal] [Rationale] [Name] [H/M/L]

Step 6: Validate Targets

Run each target through validation before committing.

Anti-Pattern Quick Check:

Anti-Pattern Detection Fix

Vanity Metrics Metric doesn't connect to revenue Find the metric that drives revenue

Feature Factory Target is "launch X" not impact Convert to "X causes Y improvement"

Sandbagging 100% confidence, no stretch Add 30-50% stretch to baseline

Moonshot Mirage 10x+ improvement, no plan Work backwards from realistic initiatives

Metric Dumping 5+ KRs per objective Force-rank to top 3

Input Trap Measuring activities not results Find the output the activity should produce

Orphan Targets Owner is "the team" Assign single person per metric

See references/anti-patterns.md for detailed detection and fixes.

Stress-Test Questions:

For each target, ask:

• Revenue Test: "If we achieve this but revenue doesn't improve, was it worth it?"

• Falsifiability Test: "What evidence would prove this target is wrong?"

• Ownership Test: "Who specifically owns this? What are they NOT doing to focus on it?"

• Measurement Test: "Can we measure this weekly, or will we only know at period end?"

• 70% Test: "If we hit 70% of this target, is that still valuable?"

See references/stress-tests.md for stage-specific questions.

Validation Output:

For targets with issues:

Target: [Description] Issues found:

• [Anti-pattern]: [Specific problem]
• [Anti-pattern]: [Specific problem]

Recommendation: [How to fix]

Step 7: Map Tracking to Targets

For each target, define how it will be tracked:

Metric Tool Event/Query Frequency

[Metric] [GA4/Mixpanel/etc] [Event name or query] [Daily/Weekly]

Plan Mode Output

Produce a Growth Plan document:

Growth Plan: [Product/Business Name]

Context

Funnel Targets

[Stage 1 Name]

Validation

• Anti-patterns: [None / List any detected]
• Stress test: [Passed / Issues found]

[Stage 2 Name]

[Same format as above]

Review Cadence

• Weekly: [Leading indicators to check]
• Monthly: [Deeper analysis]
• Quarterly: [Target recalibration]

Kill Criteria

If [condition], we will [action - pivot, deprioritize, or stop].

Intervention Mapping (Optional - for retention-focused funnels)

See references/intervention-playbooks.md for complete playbooks.

Tracking Mode

Use this when helping users implement measurement infrastructure.

Step 1: Select Tracking Stack

Ask: "What tools are you currently using for analytics? What's your technical capability?"

Tool Best For Complexity Cost

GA4 Web analytics, standard funnels Medium Free

Mixpanel/Amplitude Product analytics, cohorts Medium Freemium

Segment Data collection + routing High Paid

PostHog Product + privacy-focused Medium Open source

Custom (BigQuery) Full control, complex attribution High Variable

See references/tracking-implementation.md for setup guides.

Step 2: Define Event Schema

For each conversion point, define tracking events:

Funnel Stage Event Name Parameters Trigger

Visit page_view

page, source, medium, campaign Page load

Signup sign_up

method, plan_type Form submit

Activate activation_complete

feature_used, time_to_activate First value action

Purchase purchase

value, currency, plan Payment success

Step 2b: Risk Signal Events (Optional)

For retention-focused tracking, add risk signal events:

Signal Type Event Name Parameters Trigger

Activity drop engagement_decline

previous_rate, current_rate 50%+ decline detected

Feature abandonment feature_abandoned

feature_name, last_used Regular feature unused 14+ days

Support escalation support_escalated

ticket_id, reason Ticket escalated

Payment failure payment_failed

amount, retry_count Payment attempt failed

Competitor mention competitor_mentioned

competitor_name, context Mentioned in support/survey

See references/risk-signals.md for complete signal framework.

Event naming conventions:

• Use snake_case for event names

• Include context parameters (source, medium, campaign)

• Add user properties where relevant

Step 3: UTM Strategy

Define UTM parameters for attribution:

Parameter Purpose Convention

utm_source

Traffic source Platform name (google, facebook, newsletter)

utm_medium

Marketing medium cpc, organic, email, social

utm_campaign

Campaign name campaign-name-date

utm_content

Content variant cta-button, hero-image

utm_term

Keywords (paid) target-keyword

UTM template:

https://yoursite.com/page?utm_source={source}&utm_medium={medium}&utm_campaign={campaign}&utm_content={content}

Step 4: Attribution Model Selection

Model How It Works Best For

Last-click 100% credit to final touchpoint Short sales cycles, simple funnels

First-click 100% credit to first touchpoint Awareness campaigns

Linear Equal credit across all touchpoints Multi-touch journeys

Time-decay More credit to recent touchpoints Long consideration phases

Position-based 40% first, 40% last, 20% middle Balanced view

Data-driven ML-assigned based on patterns High volume, sophisticated teams

Ask: "How long is your typical sales cycle? How many touchpoints before conversion?"

Step 5: Build Tracking Checklist

• Analytics tool configured

• Events defined and documented

• UTM conventions documented

• Team trained on UTM usage

• Attribution model selected

• Dashboard created with funnel visualization

• Alerts set for significant changes

• Data validation process in place

Tracking Output

Produce a Tracking Implementation Document:

Tracking Implementation: [Product/Business Name]

Stack

• Primary: [Tool]
• Secondary: [Tool]

Event Schema

UTM Conventions

[Template and examples]

Attribution Model

[Model name] because [rationale]

Dashboard Metrics

1. [Metric] - [Definition]
2. ...

Implementation Checklist

• ...

Economics Mode

Use this when helping users calculate costs, ROI, and budget allocation.

Step 1: Gather Cost Inputs

Ask: "What are you spending on marketing? Break it down by channel if possible."

Cost Category Amount Frequency

Paid ads (Google, Meta, etc.) $ Monthly

Content creation $ Monthly

Tools/Software $ Monthly

Agency/Freelancers $ Monthly

Events/Sponsorships $ Per event

Sales team (if applicable) $ Monthly

Total Marketing Spend $ Monthly

Step 2: Calculate Stage Costs

For each funnel stage, calculate the cost per outcome:

Metric Formula Example

CPM (Spend / Impressions) x 1000 ($1000 / 100,000) x 1000 = $10

CPC Spend / Clicks $1000 / 500 = $2

CPL Spend / Leads $1000 / 50 = $20

CPA Spend / Conversions $1000 / 10 = $100

CAC Total Acquisition Costs / New Customers $5000 / 25 = $200

See references/unit-economics.md for detailed formulas.

Step 3: Compare Against Benchmarks

Cost Benchmarks (2025):

Industry Avg CPL Avg CAC (Paid) Avg CAC (Organic)

B2B SaaS $200-210 $800-1,200 $290-650

B2B Services $100-130 $500-800 $200-400

E-commerce $30-50 $50-100 $20-40

Mobile Apps $2-5 (CPI) $20-50 N/A

By Channel:

Channel Avg CPL Notes

Google Ads $70 Varies widely by industry

LinkedIn $110 Premium B2B audience

Facebook $17-25 Lower CPL, quality varies

Email $53 Lowest for owned lists

SEO/Content $40-80 Lower CPL, longer payback

Trade shows $800+ High CPL, high intent

Step 4: Calculate LTV:CAC and Payback

Metric Formula Healthy Benchmark

LTV ARPU x Avg Customer Lifespan

LTV:CAC LTV / CAC 3:1 minimum, 5:1 ideal

Payback Period CAC / (ARPU x Gross Margin) <12 months SMB, <18 months Mid-market

Example calculation:

ARPU = $100/month Avg Lifespan = 24 months LTV = $100 x 24 = $2,400

CAC = $600 LTV:CAC = $2,400 / $600 = 4:1

Gross Margin = 80% Monthly Contribution = $100 x 0.8 = $80 Payback = $600 / $80 = 7.5 months

Step 5: ROI Calculations

Metric Formula

ROI ((Revenue - Cost) / Cost) x 100

ROAS Revenue / Ad Spend

Incremental ROI (Incremental Revenue - Incremental Cost) / Incremental Cost

Step 6: Budget Allocation Framework

Allocate budget across funnel stages:

Funnel Stage % of Budget Rationale

Top (Awareness) 30-40% Volume generation

Middle (Consideration) 30-40% Nurturing and education

Bottom (Conversion) 20-30% Closing and conversion

Retention 10-20% Often underfunded

Adjust based on:

• New product: Weight toward top (awareness building)

• Mature product: Balance or weight toward bottom (efficiency)

• High churn: Increase retention allocation

Step 6b: Retention & Reactivation Economics (Optional)

Compare the economics of different user acquisition strategies:

Action Typical Cost vs. New CAC ROI Multiplier

Retain active customer $50-200 10-20% of CAC 5-10x

Save at-risk customer $200-500 25-50% of CAC 2-4x

Reactivate churned $500-1,000 50-100% of CAC 1-2x

Acquire new customer $500-2,000 100% (baseline) 1x

Key insight: It's almost always cheaper to prevent churn than acquire new customers.

Calculate for your business:

Metric Your Number Benchmark

Cost to Retain $ <25% of CAC

Cost to Reactivate $ <100% of CAC

CAC (new) $

LTV Preserved (retention) $

4x cost

See references/unit-economics.md for retention economics formulas.

Economics Output

Produce a Unit Economics Document:

Unit Economics: [Product/Business Name]

Cost Summary

LTV Analysis

• ARPU: $
• Avg Lifespan: X months
• LTV: $
• LTV:CAC: X:1

Payback Period

• Gross Margin: X%
• Monthly Contribution: $
• Payback: X months

ROI Summary

Budget Allocation

Cross-Funnel Metrics (Optional)

Channel LTV Attribution (Optional)

Output Template: Complete Growth Plan

When combining all modes, produce a comprehensive document:

Growth Plan: [Product/Business Name]

Executive Summary

• Funnel model: [AARRR/AIDA/etc.]
• Key conversion bottleneck: [Stage]
• Expected outcome: [Target metric improvement]
• Required investment: [$X]
• Expected ROI: [X:1]

1. Funnel Definition

[Stages, metrics, owners]

2. Targets with Validation

[Baseline -> Target tables with benchmarks and rationale]

3. Tracking Implementation

[Event schema, UTM conventions, attribution]

4. Unit Economics

[CAC, LTV, payback, budget allocation]

5. Action Items

1. [Priority action with owner]
2. ...

6. Review Cadence

• Weekly: [Metrics to review]
• Monthly: [Deeper analysis]
• Quarterly: [Target recalibration]

Integration Points

Skill How Funnel Planner Connects

mkt-diagnosis Root cause analysis upstream — use diagnosed bottlenecks to focus funnel priorities

mkt-imc Channel allocation informs funnel traffic sources and stage ownership

mkt-initiative-planner Funnel targets become initiative KPIs and success criteria

mkt-attribution Downstream validation — map funnel metrics to KPI hierarchy and flag coverage gaps

When working with these skills:

• Diagnosis output highlights which funnel stages to prioritize

• IMC channels map to funnel top (traffic sources)

• Initiative hypotheses often target specific funnel transitions

• Targets should reference funnel benchmarks

• Attribution mapping ensures every funnel metric traces to a business KPI

Next Steps

After completing a funnel plan:

• Plan initiatives → Use mkt-initiative-planner to generate and prioritize initiatives that target your funnel bottlenecks

• Validate alignment → Use mkt-attribution to confirm every funnel metric connects to a KPI and every initiative maps to a funnel stage

• Set up communication → Use mkt-imc to plan channel strategy that feeds the top of your funnel

How to Work

• Context first: Ask about their business model before suggesting a funnel structure

• Baselines required: Always ground targets in data (their baseline or benchmarks)

• Validate before committing: Run anti-pattern checks and stress tests

• Match complexity to capability: Tracking complexity should match technical ability

• Explain the "why": Every target needs benchmark + rationale justification

• Challenge unrealistic targets: Use benchmark data to push back

• Focus on bottlenecks: Find the stage that will have the biggest impact

• One owner per metric: "The team" owns nothing

• Retention-first for mature products: Often 3-5x more efficient than acquisition

Cross-Funnel Best Practices

• Compare like with like: Same entry state, same time cohort, same channel when comparing retention. Mixing new and returning users in the same analysis obscures real patterns.

• Track transitions, not just counts: State changes reveal more than point-in-time snapshots. A user moving from ACTIVE_GROWING to ACTIVE_DECLINING is a signal even if they're still "active."

• Separate first-time from repeat behaviors: First churn ≠ repeat churn. First-time churners may return; repeat churners have a fundamental fit issue.

• Dual-segment users have higher LTV: Track and encourage cross-segment adoption. A user who creates AND consumes, or sells AND buys, is more valuable and more retained.

• Feature adoption is a leading indicator: Multi-feature users have 40-60% higher retention. Feature education often has better ROI than acquisition.

• Reactivation quality > quantity: High reactivation rate with high second-churn rate just delays inevitable churn. Track post-reactivation retention.

Reference Files

Reference Use For

funnel-models.md Detailed funnel stage definitions, drop-off diagnostics

segment-based-funnels.md Multi-segment funnel design, cross-segment dynamics

benchmarks.md Industry benchmarks by stage (tiered: Poor/Average/Good/Excellent)

unit-economics.md Cost formulas, LTV, CAC, retention economics, reactivation quality

tracking-implementation.md Analytics setup guides

anti-patterns.md Target-setting pitfalls

stress-tests.md Target validation questions

lifecycle-states.md User state machine model (simple + granular 3-tier)

health-scoring.md Health score calculation, segment-specific weights, feature adoption

risk-signals.md Early warning indicators

cohort-analysis.md Cohort comparison frameworks, churn cohort analysis

user-segmentation.md Segmentation dimensions

intervention-playbooks.md Stage-specific action plans, time-based triggers