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
| Field | Value |
|-------|-------|
| **Period** | [Q1 2025 / H1 2025] |
| **Business Type** | [SaaS / E-commerce / etc.] |
| **Funnel Model** | [AARRR / AIDA / etc.] |
| **Planning Date** | [Date] |

### Funnel Targets

## [Stage 1 Name]

| Metric | Baseline | Target | Why | Track | Owner |
|--------|----------|--------|-----|-------|-------|
| [Metric] | [Current] | [Goal] | [Benchmark + rationale] | [Event in Tool] | [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)

| Trigger | Playbook | Owner | Response Window |
|---------|----------|-------|-----------------|
| Health score <50 | At-Risk Account | CSM | 3 days |
| Activity drop >50% | Engagement Decline | CSM | 7 days |
| Payment failure | Payment Recovery | Billing | 24 hours |
| NPS 0-6 | Detractor Follow-up | CSM | 48 hours |

See [references/intervention-playbooks.md](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
| Event | Parameters | Trigger |
|-------|------------|---------|
| ... | ... | ... |

### 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
| Metric | Current | Benchmark | Status |
|--------|---------|-----------|--------|
| CPL | $ | $ | [OK/High/Low] |
| CPA | $ | $ | [OK/High/Low] |
| CAC | $ | $ | [OK/High/Low] |

### LTV Analysis
- ARPU: $
- Avg Lifespan: X months
- LTV: $
- LTV:CAC: X:1

### Payback Period
- Gross Margin: X%
- Monthly Contribution: $
- Payback: X months

### ROI Summary
| Channel | Spend | Revenue | ROI |
|---------|-------|---------|-----|
| ... | ... | ... | ... |

### Budget Allocation
| Stage | Current % | Recommended % | Change |
|-------|-----------|---------------|--------|
| ... | ... | ... | ... |

### Cross-Funnel Metrics (Optional)
| Metric | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| NRR | % | >100% | |
| Cost to Retain | $ | <25% CAC | |
| Cost to Reactivate | $ | <100% CAC | |
| Full Funnel Efficiency | $ rev/$ spend | | |

### Channel LTV Attribution (Optional)
| Channel | CAC | Avg LTV | LTV:CAC | Action |
|---------|-----|---------|---------|--------|
| ... | $ | $ | :1 | [Scale/Maintain/Optimize/Cut] |

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

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