Performance Marketer

Performance Marketer

Expert performance marketing guidance for paid acquisition, conversion rate optimization, and data-driven growth — from ad creative to attribution modeling.

Philosophy

Great performance marketing is a system, not a series of tactics:

1. Measure what matters — Vanity metrics kill budgets 2. Test with intention — Random A/B tests waste time and traffic 3. Optimize the full funnel — A great ad to a bad landing page burns money 4. Compound learnings — Every test teaches something for the next

How This Skill Works

When invoked, apply the guidelines in rules/ organized by:

• paid-* — Paid advertising strategy, creative, and copy
• landing-* — Landing page optimization
• testing-* — A/B testing and experimentation frameworks
• analytics-* — Attribution, CAC/LTV, and conversion tracking
• budget-* — Budget allocation and scaling

Core Frameworks

The Performance Marketing Loop

┌─────────────────────────────────────────────────────────┐
│                                                         │
│   ┌──────────┐    ┌──────────┐    ┌──────────┐         │
│   │   ADS    │───▶│  LANDING │───▶│ CONVERT  │         │
│   │ (Reach)  │    │  (Hook)  │    │ (Action) │         │
│   └──────────┘    └──────────┘    └──────────┘         │
│        ▲                                │               │
│        │          ┌──────────┐          │               │
│        └──────────│ ANALYZE  │◀─────────┘               │
│                   │ (Learn)  │                          │
│                   └──────────┘                          │
│                                                         │
└─────────────────────────────────────────────────────────┘

The CAC/LTV Equation

Funnel Math

Impressions × CTR = Clicks
Clicks × CVR = Conversions
Conversions × Close Rate = Customers
Customers × ARPU = Revenue

Channel Selection Matrix

Key Metrics by Stage

Budget Allocation Framework

The 70-20-10 Rule

• 70% — Proven channels and campaigns (scale what works)
• 20% — Optimization tests (improve what's working)
• 10% — Experimentation (test new channels/approaches)

Scaling Checklist

Before scaling a campaign:

• [ ] LTV:CAC ratio ≥ 3:1
• [ ] Consistent performance over 2+ weeks
• [ ] Statistical significance on key metrics
• [ ] Landing page handles traffic spikes
• [ ] Tracking verified on all conversion events

Anti-Patterns

• Scaling too fast — Doubling budget overnight breaks algorithms
• Testing everything at once — Can't learn what caused the change
• Ignoring attribution — Last-click lies, multi-touch tells the truth
• Copy-paste ads — Same creative across channels fails
• Optimizing for clicks — Clicks don't pay bills, conversions do
• Set and forget — Ads fatigue, audiences shift, competition changes
• Vanity metrics — Impressions feel good, revenue feels better

1. Paid Advertising Strategy (paid)

Impact: CRITICAL Description: Channel selection, campaign structure, audience targeting, and overall paid media strategy.

2. Creative & Copy (creative)

Impact: CRITICAL Description: Ad creative best practices, copy formulas, visual guidelines, and platform-specific formats.

3. Landing Page Optimization (landing)

Impact: CRITICAL Description: Landing page structure, message match, conversion optimization, and page speed.

4. Testing & Experimentation (testing)

Impact: HIGH Description: A/B testing frameworks, statistical significance, test prioritization, and learning systems.

5. Analytics & Attribution (analytics)

Impact: HIGH Description: Attribution modeling, conversion tracking, CAC/LTV analysis, and reporting.

6. Budget & Scaling (budget)

Impact: MEDIUM-HIGH Description: Budget allocation, scaling strategies, bid optimization, and efficiency.

7. Retargeting (retargeting)

Impact: MEDIUM-HIGH Description: Retargeting strategy, audience segmentation, frequency capping, and sequential messaging.

Attribution Modeling

Impact: HIGH

Attribution determines which channels get credit for conversions. Wrong attribution leads to wrong decisions and wasted budget.

Attribution Models Overview

Model Comparison Example

Customer journey: Google Ad → Blog Post → LinkedIn → Demo → Close

When to Use Each Model

Multi-Touch Attribution Setup

1. Define conversion events
   └── Primary: Trial signup, demo request, purchase
   └── Secondary: Pricing page, feature page, content DL

2. Identify touchpoints
   └── Paid: Google, Meta, LinkedIn
   └── Organic: SEO, direct, referral
   └── Owned: Email, blog, social

3. Connect data sources
   └── Ad platforms → Analytics → CRM

4. Set lookback window
   └── B2C: 7-30 days
   └── B2B: 30-90 days

5. Choose model
   └── Start with position-based
   └── Move to data-driven when mature

Tracking Implementation

UTM Parameter Standards

utm_source    = where traffic comes from (google, linkedin, email)
utm_medium    = marketing medium (cpc, social, email)
utm_campaign  = campaign name (spring-sale, product-launch)
utm_content   = creative variation (blue-button, video-a)
utm_term      = keyword (for paid search)

UTM Best Practices

✓ Good UTM structure:
?utm_source=linkedin
&utm_medium=cpc
&utm_campaign=2024-q1-awareness
&utm_content=carousel-testimonials

✗ Bad UTM structure:
?utm_source=LinkedIn%20Ad%20Campaign%20March
(spaces, inconsistent naming, no other parameters)

UTM Naming Convention

Attribution Challenges

Good Attribution Practices

✓ Compare multiple models
  → Last-click says cut SEO, position-based shows it drives awareness

✓ Segment by customer type
  → Enterprise has different journey than SMB

✓ Regular attribution audits
  → Check tracking is working, parameters are consistent

✓ Use control groups
  → Measure incrementality, not just attribution

Bad Attribution Practices

✗ Trusting only last-click
  → Overvalues bottom-funnel, undervalues awareness

✗ Platform-reported conversions only
  → Each platform takes credit, totals exceed actual conversions

✗ Ignoring assisted conversions
  → First/middle touches build the pipeline

✗ No offline conversion tracking
  → B2B especially needs CRM integration

✗ Set-and-forget tracking
  → UTM parameters drift, pixels break

View-Through vs Click-Through

View-through guidance:

• Useful for awareness/brand campaigns
• Default windows often too long (reduce to 1-7 days)
• Easy to over-count (same user sees many ads)
• Better for display/video than search

Incrementality Testing

Attribution shows correlation. Incrementality shows causation.

Anti-Patterns

• Last-click worship — Ignores awareness and consideration
• Trusting platform numbers — Each platform overclaims
• No lookback window management — Default windows are too long
• Ignoring dark social — Not all traffic is trackable
• View-through over-reliance — Easy to inflate
• No holdout testing — Attribution ≠ causation
• Inconsistent UTMs — Garbage in, garbage out

CAC/LTV Analysis

Impact: HIGH

Unit economics determine if your growth is sustainable. Great CAC/LTV ratios are the foundation of scalable businesses.

Core Metrics

CAC Calculation Methods

Blended CAC

CAC = (Sales + Marketing Spend) ÷ New Customers

Fully Loaded CAC

CAC = (Sales + Marketing + Salaries + Tools + Overhead) ÷ New Customers

Channel CAC

CAC = Channel Spend ÷ Customers from Channel

LTV Calculation Methods

Simple LTV

LTV = ARPU × Average Customer Lifespan (months)

Gross Margin LTV

LTV = ARPU × Gross Margin % × Avg Lifespan

DCF LTV (Discounted)

LTV = Σ (Monthly Revenue × Margin) ÷ (1 + Discount Rate)^month

LTV:CAC Benchmarks

Payback Period Benchmarks

CAC by Channel

LTV Improvement Levers

CAC Reduction Levers

Cohort Analysis Framework

Track cohorts to understand true LTV:

| Cohort    | M0  | M1  | M2  | M3  | M6  | M12 |
|-----------|-----|-----|-----|-----|-----|-----|
| Jan 2024  | 100%| 85% | 78% | 72% | 60% | 45% |
| Feb 2024  | 100%| 82% | 75% | 70% | 58% | -   |
| Mar 2024  | 100%| 88% | 80% | 74% | -   | -   |

Good CAC/LTV Analysis

✓ Segment by acquisition channel
  → Google Search CAC: $150, LTV: $600 (4:1)
  → Meta Ads CAC: $80, LTV: $200 (2.5:1)
  → Action: Shift budget to Google Search

✓ Track cohorts over time
  → 2023 cohorts: 40% 12-month retention
  → 2024 cohorts: 55% 12-month retention
  → LTV improving due to product improvements

✓ Account for payback period
  → 8-month payback with 15-month avg lifespan
  → Healthy, can scale

Bad CAC/LTV Analysis

✗ Blended CAC only
  → Hides that one channel is unprofitable

✗ LTV without gross margin
  → Overstates profitability

✗ Short-term LTV projections
  → Need 12+ months of data minimum

✗ Ignoring acquisition channel
  → Organic and paid customers behave differently

✗ Not updating regularly
  → Markets change, metrics drift

Reporting Dashboard Metrics

Anti-Patterns

• Blended-only thinking — Hides channel inefficiencies
• Projecting LTV too early — Need cohort data, not projections
• Ignoring gross margin — Revenue ≠ profit
• Annual CAC, monthly LTV — Match time periods
• Not segmenting — SMB vs enterprise is different
• Ignoring payback — Cash flow matters
• One-time calculation — Metrics change, update regularly

Conversion Tracking

Impact: HIGH

Without accurate conversion tracking, you're flying blind. Every optimization decision depends on reliable data.

Tracking Architecture

┌─────────────────────────────────────────────────────────┐
│                    USER BROWSER                         │
│  ┌───────────────────────────────────────────────────┐  │
│  │  Client-Side Tracking                             │  │
│  │  • Google Analytics (gtag.js)                     │  │
│  │  • Meta Pixel (fbq)                               │  │
│  │  • LinkedIn Insight Tag                           │  │
│  │  • GTM (Google Tag Manager)                       │  │
│  └───────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────┐
│                    YOUR SERVER                          │
│  ┌───────────────────────────────────────────────────┐  │
│  │  Server-Side Tracking (CAPI)                      │  │
│  │  • Meta Conversions API                           │  │
│  │  • Google Enhanced Conversions                    │  │
│  │  • LinkedIn Conversions API                       │  │
│  │  • Server-side GTM                                │  │
│  └───────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────┘

Conversion Events Hierarchy

Standard Events by Platform

Event Parameters

Include with every conversion:

// Google Analytics 4
gtag('event', 'purchase', {
  transaction_id: 'T12345',
  value: 99.00,
  currency: 'USD',
  items: [{
    item_id: 'SKU123',
    item_name: 'Pro Plan Annual'
  }]
});

// Meta Pixel
fbq('track', 'Purchase', {
  value: 99.00,
  currency: 'USD',
  content_ids: ['SKU123'],
  content_type: 'product'
});

Conversion Value Assignment

Tracking Verification Checklist

• [ ] Pixels loading on all pages
• [ ] Events firing correctly (test with extensions)
• [ ] Conversion values passing accurately
• [ ] Parameters populated (content_id, currency, etc.)
• [ ] Cross-domain tracking working
• [ ] Server-side tracking matching client-side
• [ ] Deduplication in place
• [ ] Test mode events showing in platforms

Debug Tools

Good Tracking Implementation

✓ Enhanced conversion matching
  → User email hashed and sent server-side
  → Recovers 15-25% of missed conversions

✓ Primary + secondary events
  → Purchase as primary optimization event
  → Add-to-cart for funnel visibility

✓ Conversion value with margin
  → Revenue × margin passed as value
  → Enables ROAS optimization

✓ Server-side + client-side
  → Redundancy for accuracy
  → Deduplication by event_id

Bad Tracking Implementation

✗ Thank you page only tracking
  → Misses users who close before page loads

✗ No conversion value
  → Algorithm can't optimize for ROAS

✗ Client-side only
  → Ad blockers hide 20-40% of conversions

✗ No test environment separation
  → Test conversions pollute real data

✗ Missing parameters
  → Can't analyze by content, product, etc.

Server-Side Tracking (CAPI)

Why server-side matters:

• Ad blockers block client-side (20-40% traffic)
• iOS ATT reduces client-side accuracy
• Better data quality and consistency
• Required for some advanced features

Deduplication

When using client-side + server-side, dedupe:

// Generate unique event ID
const event_id = crypto.randomUUID();

// Client-side (Meta)
fbq('track', 'Purchase', {value: 99}, {eventID: event_id});

// Server-side (send same event_id)
// Meta will deduplicate based on event_id

Cross-Domain Tracking

For sites spanning multiple domains:

Offline Conversions

For B2B or phone sales:

1. Capture click ID (gclid, fbclid, li_fat_id)
2. Store with lead record in CRM
3. When lead converts offline, upload to platform
4. Match via click ID or hashed email

Privacy Compliance

Consent Mode (Google)

// Default: denied
gtag('consent', 'default', {
  'ad_storage': 'denied',
  'analytics_storage': 'denied'
});

// After consent
gtag('consent', 'update', {
  'ad_storage': 'granted',
  'analytics_storage': 'granted'
});

Tracking Audit Checklist

Monthly:

• [ ] Verify all pixels firing
• [ ] Check conversion counts match CRM
• [ ] Review server-side event delivery rate
• [ ] Audit UTM parameter consistency
• [ ] Test new pages and flows
• [ ] Check for tracking gaps in funnel

Anti-Patterns

• No server-side tracking — Missing 20-40% of conversions
• Duplicate conversions — Inflates numbers, breaks optimization
• Wrong conversion windows — 7-day vs 28-day changes attribution
• No value tracking — Can't optimize for ROAS
• Ignoring consent — Legal risk, compliance issues
• Set and forget — Site changes break tracking
• Test conversions in production — Pollutes data

Budget Allocation

Impact: MEDIUM-HIGH

Smart budget allocation maximizes return. Dumb allocation wastes money on channels that don't convert.

The 70-20-10 Rule

Budget Allocation by Funnel

Channel Budget Framework

Allocate based on performance data:

Total Budget: $50,000/month

Channel Performance:
┌────────────────┬──────┬──────┬─────────┬───────────┐
│ Channel        │ CAC  │ LTV  │ LTV:CAC │ Recommend │
├────────────────┼──────┼──────┼─────────┼───────────┤
│ Google Search  │ $120 │ $600 │ 5:1     │ Scale ↑   │
│ Meta Ads       │ $80  │ $240 │ 3:1     │ Maintain  │
│ LinkedIn       │ $350 │ $800 │ 2.3:1   │ Test more │
│ Content/SEO    │ $40  │ $500 │ 12.5:1  │ Invest ↑  │
└────────────────┴──────┴──────┴─────────┴───────────┘

Allocation:
• Google Search: $20,000 (40%) — Best ROAS, scale
• Meta Ads: $15,000 (30%) — Profitable, maintain
• Content/SEO: $8,000 (16%) — Best LTV, invest
• LinkedIn: $5,000 (10%) — Testing, optimize
• Experiments: $2,000 (4%) — New channels

Scaling Thresholds

Scaling Strategy

Week 1: $1,000/day (baseline)
Week 2: $1,200/day (+20% if performance stable)
Week 3: $1,400/day (+17% if performance stable)
Week 4: $1,700/day (+21% if performance stable)

If CAC increases >15%: Hold and optimize
If CAC increases >30%: Reduce budget 20%

Budget Pacing

Good Budget Allocation

✓ Data-driven decisions
  → Shift budget based on CAC, not gut feel

✓ Regular rebalancing
  → Monthly review, quarterly strategy

✓ Seasonal adjustment
  → Increase for Q4, pull back in slow periods

✓ Test budget protected
  → 10% always for experimentation

✓ Runway consideration
  → Don't spend faster than you can afford

Bad Budget Allocation

✗ Equal split across channels
  → Ignores performance differences

✗ Set and forget
  → Markets change, performance drifts

✗ All-in on one channel
  → Concentration risk, algorithm dependency

✗ Cutting based on gut
  → Let data drive decisions

✗ Scaling too fast
  → Algorithms need time to optimize

Diminishing Returns

Every channel has a saturation point:

     Conversions
        │
        │        ╭───────────────
        │      ╱
        │    ╱   ← Diminishing returns zone
        │  ╱
        │╱______________________
        └────────────────────────▶
                   Spend

As spend increases:
• Audience exhaustion
• Higher CPM (auctions)
• Lower-quality placements
• Ad fatigue

Signs You've Hit Diminishing Returns

• CAC increasing despite no changes
• CTR declining
• Frequency > 3 for same audience
• CPM rising significantly
• Conversion rate dropping

Budget Optimization Levers

Monthly Budget Review

Quarterly Budget Planning

1. Review performance by channel
   └── CAC, LTV:CAC, conversion volume

2. Analyze trends
   └── Improving, stable, or declining

3. Set targets
   └── Volume, efficiency, new channels

4. Allocate budget
   └── Based on LTV:CAC and goals

5. Plan experiments
   └── New channels, audiences, creative

6. Document assumptions
   └── CPM trends, seasonality, market changes

Anti-Patterns

• Peanut buttering — Equal budget across all channels
• Chasing volume — More spend doesn't mean more profit
• Ignoring LTV:CAC — Revenue focus misses efficiency
• Scaling too fast — Algorithms need learning time
• No test budget — Growth channels come from experiments
• Reactive cuts — Week-over-week swings are normal
• Platform default budgets — Often too high for new campaigns

Ad Copy Best Practices

Impact: CRITICAL

Your ad copy has 1-3 seconds to stop the scroll. Every word must earn its place.

The Ad Copy Formula

Hook (Attention) → Value (Interest) → Proof (Trust) → CTA (Action)

Platform-Specific Formats

Hook Formulas That Work

Good Ad Copy

✓ Google Search Ad:
Headline 1: "Automate Your Secrets Management"
Headline 2: "Start Free, No Credit Card"
Headline 3: "SOC 2 Compliant"
Description: "Stop hardcoding API keys. Sync secrets across environments in minutes. Trusted by 10,000+ developers."

✓ Meta Ad (Primary Text):
"I used to spend 3 hours every Monday updating .env files across 12 repos.

Now it takes 30 seconds.

SecretStash syncs your secrets automatically. One source of truth. Zero copy-paste.

→ Free for teams under 5"

✓ LinkedIn Ad:
"Engineering leaders: Your developers are storing secrets in Slack.

We surveyed 500 teams. 73% had credentials in chat history.

Here's how to fix it without slowing down your team:
[Link]"

Bad Ad Copy

✗ "We're excited to announce our revolutionary platform!"
  → Nobody cares about your excitement

✗ "Best-in-class solution for enterprise needs"
  → Empty jargon, no specifics

✗ "Click here to learn more"
  → Vague CTA, no value proposition

✗ "SecretStash | Secrets Management | API Keys | .env"
  → Keyword stuffing, not copywriting

✗ "Our award-winning team has developed..."
  → Talks about you, not them

CTA Hierarchy (Commitment Level)

Copy Testing Priority

Test these elements in order: 1. Hook/Opening line — Highest impact on CTR 2. Value proposition — What makes you different 3. CTA — What you want them to do 4. Social proof — Numbers, logos, testimonials 5. Offer — Free trial vs demo vs buy

Character Count Cheat Sheet

Emotional Triggers

Anti-Patterns

• Feature-first copy — Lead with benefit, not feature
• We/us language — They care about themselves
• Multiple CTAs — One ad, one action
• Clever over clear — Wordplay that confuses
• Lying or exaggerating — Destroys trust permanently
• Ignoring platform norms — LinkedIn ≠ TikTok ≠ Search

Visual Creative Guidelines

Impact: CRITICAL

Visuals stop the scroll before copy can convert. Strong creative can improve performance 2-5x.

Platform Image Specifications

Video Specifications

Creative Principles

Creative Types That Work

Good Creative Examples

✓ Static Image:
- Clear headline (4-7 words max)
- Product screenshot or illustration
- Brand colors, no clutter
- CTA button visible

✓ Video:
- Hook in first 2 seconds
- Problem statement by second 5
- Solution demo seconds 5-20
- CTA and social proof at end
- Captions/subtitles throughout

✓ Carousel:
- Slide 1: Hook + curiosity gap
- Slides 2-4: Value/benefit/proof
- Final slide: CTA + offer

Bad Creative Examples

✗ Wall of text on image
  → Unreadable on mobile, low engagement

✗ Generic stock photos
  → Blend in with organic content

✗ Slow video intro with logo
  → Lost audience before message

✗ Multiple CTAs in one creative
  → Confuses the action

✗ Tiny product screenshots
  → Can't see on mobile

✗ Off-brand creative
  → Damages brand recognition

Text-on-Image Rules

Creative Testing Framework

Mobile Optimization Checklist

• [ ] Text readable at 50% size
• [ ] Key message visible without full-screen
• [ ] CTAs fingertip-sized (min 44×44px)
• [ ] Loads fast (<3 seconds)
• [ ] Looks good in feed preview
• [ ] Sound-off friendly (captions for video)

Anti-Patterns

• Stock photo syndrome — Generic images blend into feed
• Logo-first design — Nobody cares about your logo
• Desktop-designed — 85%+ traffic is mobile
• Too much information — One message per creative
• Ignoring safe zones — Platform UI covers edges
• No variation — Same creative fatigues fast

Landing Page Optimization

Impact: CRITICAL

A great ad with a bad landing page is burning money. Your landing page must continue the conversation your ad started.

The Message Match Principle

Landing Page Anatomy

┌────────────────────────────────────────────────────┐
│                    ABOVE THE FOLD                  │
│  ┌──────────────────────────────────────────────┐  │
│  │ Headline (Value Prop)                        │  │
│  │ Subhead (Clarify + Benefit)                  │  │
│  │ [CTA Button]  [Secondary CTA]                │  │
│  │ Social Proof (logos, numbers)                │  │
│  └──────────────────────────────────────────────┘  │
├────────────────────────────────────────────────────┤
│                    BELOW THE FOLD                  │
│  • Problem/Agitation                               │
│  • Solution/How it works                           │
│  • Features with benefits                          │
│  • Social proof (testimonials, case studies)       │
│  • FAQ/Objection handling                          │
│  • Final CTA                                       │
└────────────────────────────────────────────────────┘

Above-the-Fold Checklist

• [ ] Clear value proposition (what do you do?)
• [ ] Specific benefit (why should I care?)
• [ ] Primary CTA visible without scrolling
• [ ] Social proof indicator (logos, numbers, ratings)
• [ ] Relevant hero image or video
• [ ] No navigation distractions

Page Speed Requirements

Conversion Rate Benchmarks

Good Landing Page Elements

✓ Headline:
"Stop Hardcoding Secrets. Ship Faster."
  → Clear benefit, action-oriented

✓ Subhead:
"SecretStash syncs your API keys, tokens, and credentials across all environments in one click."
  → Explains the what, reinforces the benefit

✓ CTA:
"Start Free — No Credit Card Required"
  → Specific action + objection removed

✓ Social Proof:
"Trusted by 10,000+ developers at Stripe, Notion, and Vercel"
  → Specific number + recognizable logos

Bad Landing Page Elements

✗ Headline: "Welcome to SecretStash"
  → Says nothing about value

✗ Subhead: "A next-generation platform for modern teams"
  → Generic, could be any product

✗ CTA: "Learn More"
  → Vague, no value proposition

✗ No social proof visible
  → Missed trust opportunity

✗ Video autoplays with sound
  → Annoying, increases bounce

Form Optimization

Form Best Practices

• [ ] Only ask for what you need now
• [ ] Use single-column layout
• [ ] Show inline validation
• [ ] Use smart defaults
• [ ] Mobile keyboard optimization (email → email keyboard)
• [ ] Clear error messages
• [ ] Progress indicator for multi-step

CTA Button Optimization

Friction Reducers

Mobile Optimization

• [ ] Thumb-friendly CTAs (44px minimum)
• [ ] Collapsible navigation or no nav
• [ ] Font size 16px+ for body text
• [ ] No horizontal scroll
• [ ] Sticky CTA button option
• [ ] Click-to-call phone numbers
• [ ] Auto-zoom disabled on form fields

Testing Priority (Impact Order)

1. Headline — Biggest impact on engagement 2. CTA copy — Direct impact on conversions 3. Hero image/video — First impression 4. Social proof placement — Trust building 5. Form length — Conversion friction 6. Page layout — Information hierarchy

Anti-Patterns

• Navigation links — Every link is an exit opportunity
• Multiple offers — One page, one goal
• Slow loading — Speed is a feature
• No mobile optimization — Majority of traffic is mobile
• Generic stock photos — Feel inauthentic
• Walls of text — Scannable beats readable
• Hidden CTA — Make the action obvious
• Missing social proof — Trust must be earned

Paid Ads Strategy

Impact: CRITICAL

Strategic paid advertising is about putting the right message in front of the right audience at the right time. Get this wrong, and no amount of optimization saves you.

Channel Selection Framework

Campaign Structure

Account
├── Campaign (Objective + Budget)
│   ├── Ad Set/Group (Audience + Placement)
│   │   ├── Ad (Creative + Copy)
│   │   └── Ad (Variation)
│   └── Ad Set/Group (Different Audience)
└── Campaign (Different Objective)

Audience Targeting Hierarchy

Good Campaign Strategy

✓ Start with bottom-of-funnel audiences
  → Highest intent, fastest learnings

✓ Use separate campaigns for prospecting vs retargeting
  → Different budgets, different optimization goals

✓ Match campaign objective to business goal
  → Conversions for sales, Reach for awareness

✓ Test one variable at a time
  → Audience OR creative, not both simultaneously

Bad Campaign Strategy

✗ Running all traffic to homepage
  → No message match, kills conversion rate

✗ Targeting "all ages, all interests"
  → Algorithm has nothing to optimize toward

✗ One campaign for everything
  → Can't allocate budget properly

✗ Copying competitor campaigns exactly
  → Their audience isn't your audience

✗ Starting with lookalikes before having seed data
  → Need 1,000+ conversions for quality lookalikes

Budget Allocation by Funnel Stage

Campaign Launch Checklist

• [ ] Clear objective defined (conversions, leads, awareness)
• [ ] Audience research completed
• [ ] Conversion tracking verified
• [ ] Landing page live and tested
• [ ] Creative assets prepared (multiple variations)
• [ ] Budget set with daily/lifetime caps
• [ ] Bid strategy selected (manual vs auto)
• [ ] Exclusions set (competitors, existing customers)
• [ ] UTM parameters configured
• [ ] Reporting dashboard ready

Anti-Patterns

• Premature scaling — Algorithm needs 50+ conversions/week to optimize
• Audience overlap — Campaigns competing against each other
• Wrong objective — Optimizing for clicks when you want purchases
• No exclusions — Paying to advertise to existing customers
• Platform default settings — Audience Network/Display placements drain budget

Retargeting Strategy

Impact: MEDIUM-HIGH

Retargeting converts warm audiences at 3-10x the rate of cold. It's your most efficient spend—when done right.

Retargeting Funnel

┌─────────────────────────────────────────────────────────┐
│                    ALL VISITORS                         │
│  ┌───────────────────────────────────────────────────┐  │
│  │             Homepage Visitors (30 days)           │  │
│  │  ┌─────────────────────────────────────────────┐  │  │
│  │  │        Feature Page Visitors (14 days)      │  │  │
│  │  │  ┌───────────────────────────────────────┐  │  │  │
│  │  │  │      Pricing Page Visitors (7 days)   │  │  │  │
│  │  │  │  ┌─────────────────────────────────┐  │  │  │  │
│  │  │  │  │   Cart Abandoners (3 days)      │  │  │  │  │
│  │  │  │  │  ┌───────────────────────────┐  │  │  │  │  │
│  │  │  │  │  │ Trial Starters (7 days)   │  │  │  │  │  │
│  │  │  │  │  └───────────────────────────┘  │  │  │  │  │
│  │  │  │  └─────────────────────────────────┘  │  │  │  │
│  │  │  └───────────────────────────────────────┘  │  │  │
│  │  └─────────────────────────────────────────────┘  │  │
│  └───────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────┘
          Higher Intent = More Valuable = Bid Higher

Audience Segments

Recency Strategy

Sequential Messaging

Day 1-2: Reminder
"You left something behind"
→ Product/feature they viewed

Day 3-5: Social Proof
"Join 10,000+ teams using [Product]"
→ Testimonials, case studies

Day 6-10: Objection Handler
"Have questions? We've got answers"
→ FAQ, comparison, support

Day 11-14: Incentive
"Your exclusive offer expires soon"
→ Discount, extended trial, bonus

Platform-Specific Setup

Exclusion Strategy

Always exclude:

Frequency Capping

Good Retargeting

✓ Segmented by intent
  → Pricing page visitors get different message than blog readers

✓ Sequential messaging
  → Story evolves over time, not same ad repeatedly

✓ Frequency capped
  → 3-5 impressions per week max

✓ Exclusions in place
  → Customers excluded from acquisition

✓ Dynamic creative
  → Show products/features they viewed

Bad Retargeting

✗ Everyone in one audience
  → Cart abandoners need different message than homepage bouncers

✗ Same ad for 30 days
  → Ad fatigue, annoyance, brand damage

✗ No frequency cap
  → Stalker ads damage brand

✗ No recency windows
  → Showing ads 60 days later is wasted spend

✗ Retargeting customers with acquisition ads
  → "Sign up for free trial" to paying customers

Dynamic Retargeting

Show users the specific products/content they viewed:

Cross-Platform Retargeting

User visits website (Google Ads)
    ↓
Captured by all pixels (Meta, LinkedIn, Google)
    ↓
Sees retargeting on Meta (social browsing)
    ↓
Sees retargeting on YouTube (video content)
    ↓
Returns via Google Search ad
    ↓
Converts

Retargeting Budget Split

Measurement

Anti-Patterns

• No segmentation — All visitors treated the same
• No frequency cap — Creepy, annoying, wasteful
• No exclusions — Advertising to customers
• Static creative — Same ad for weeks
• Too long windows — 60+ day audiences are cold again
• Ignoring iOS changes — Server-side tracking needed
• No sequential messaging — Miss opportunity to build story
• Retargeting only — Need cold traffic to retarget

A/B Testing Framework

Impact: HIGH

A/B testing without a framework is just guessing with data. Structured testing compounds learnings over time.

The Testing Hierarchy

Test in this order for maximum impact:

Statistical Significance

Minimum Detectable Effect (MDE)

Test Hypothesis Template

We believe [change] will [improve metric] because [rationale].
We will know this is true when [metric] increases by [X%] with [confidence]%.

Good Test Hypotheses

✓ "We believe adding customer logos above the fold will increase
   form submissions by 15% because social proof reduces uncertainty."

✓ "We believe changing the CTA from 'Learn More' to 'Start Free Trial'
   will increase clicks by 25% because it's more specific and actionable."

✓ "We believe reducing form fields from 5 to 3 will increase
   completions by 30% because it reduces friction."

Bad Test Hypotheses

✗ "We want to test a new button color"
  → No expected outcome, no rationale

✗ "Let's see if this new design performs better"
  → Vague, unmeasurable

✗ "The CEO thinks we should try this"
  → Not data-informed, HiPPO decision

Testing Checklist (Pre-Launch)

• [ ] Hypothesis documented
• [ ] Single variable being tested
• [ ] Sample size calculated
• [ ] Test duration planned
• [ ] Primary metric defined
• [ ] Tracking verified
• [ ] QA on all variations
• [ ] Mobile/desktop checked
• [ ] Audience split randomized

Testing Checklist (During)

• [ ] Don't peek early (peeking inflates false positives)
• [ ] Monitor for technical issues only
• [ ] Don't stop at first significance
• [ ] Run for full business cycle (week minimum)
• [ ] Document any external factors

Testing Checklist (Post)

• [ ] Statistical significance confirmed
• [ ] Segment results analyzed
• [ ] Learnings documented
• [ ] Winner implemented
• [ ] Next test planned

Common Test Ideas

Segment Analysis

Don't just look at overall results. Check:

False Positive Prevention

Documentation Template

## Test: [Name]
**Date:** [Start] - [End]
**Hypothesis:** [Statement]
**Primary Metric:** [Metric]
**Sample Size:** [N per variation]

### Variations
- Control: [Description]
- Treatment: [Description]

### Results
| Variation | Visitors | Conversions | CVR | Lift |
|-----------|----------|-------------|-----|------|
| Control   |          |             |     | -    |
| Treatment |          |             |     |      |

**Statistical Significance:** [Yes/No, p-value]
**Confidence:** [%]

### Key Learnings
- [Learning 1]
- [Learning 2]

### Next Steps
- [Action item]

Anti-Patterns

• Testing too many things — Can't isolate what caused change
• Stopping early — False positives from peeking
• Running too long — Novelty effect wears off
• Ignoring segments — Average hides important differences
• No documentation — Same mistakes repeated
• Copying others' tests — Context matters, test for your audience
• HiPPO decisions — Highest Paid Person's Opinion overrules data
• Winner-take-all thinking — Learn from losers too