What is ad-spend-optimizer and when should I use it?

It systematically optimizes paid advertising budgets across channels using performance data, attribution models, and ROI targets, grounded in marginal ROI optimization and portfolio theory.

What attribution models does it support?

Last Click, First Click, Linear, Time Decay, Position, and Data-Driven ML-based attribution.

What data do I need to run it effectively?

Channel-level spend, leads, CAC, ROAS, CTR, conversion rate, and ROI targets, plus access to attribution data and performance dashboards.

ad-spend-optimizer

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Ad Spend Optimizer

Systematically optimize paid advertising budget allocation across channels based on performance data, attribution analysis, and ROI targets.

When to Use This Skill

Quarterly budget planning
Channel mix optimization
Performance troubleshooting
Scaling paid acquisition
ROI analysis and reporting

Methodology Foundation

Based on marginal ROI optimization and portfolio theory for marketing, combining:

Channel performance analysis
Attribution modeling
Diminishing returns curves
Test and scale frameworks

What Claude Does vs What You Decide

Claude Does	You Decide
Analyzes channel performance	Budget constraints
Calculates ROI by channel	Risk tolerance
Recommends allocation shifts	Testing budgets
Identifies optimization opportunities	Business priorities
Creates performance dashboards	Platform selection

Instructions

Step 1: Audit Current Performance

Key Metrics by Channel:

Metric	Definition	Target
ROAS	Revenue / Ad Spend	>3:1
CAC	Cost to Acquire Customer	<LTV/3
CPA	Cost per Acquisition	Varies
CTR	Clicks / Impressions	Benchmark
Conv Rate	Conversions / Clicks	Benchmark

Step 2: Attribution Analysis

Attribution Models:

Model	Logic	Best For
Last Click	100% to final touchpoint	Direct response
First Click	100% to first touchpoint	Awareness campaigns
Linear	Equal across all touchpoints	Balanced view
Time Decay	More to recent touchpoints	Shorter cycles
Position	40% first, 40% last, 20% middle	Balanced with emphasis
Data-Driven	ML-based attribution	Sophisticated analysis

Step 3: Identify Optimization Opportunities

Optimization Levers:

Lever	Impact	Effort
Budget reallocation	High	Low
Audience targeting	High	Medium
Creative refresh	Medium	Medium
Bid optimization	Medium	Low
Landing page	High	High
New channel test	Variable	Medium

Step 4: Calculate Optimal Allocation

Marginal ROI Analysis:

Where is next $1 best spent?
Identify diminishing returns
Find underinvested channels
Balance efficiency vs. scale

Step 5: Implement and Monitor

Test Framework:

70% proven channels
20% optimization tests
10% new channel experiments

Examples

Example 1: Full Budget Optimization

Input:

Optimize this ad spend:
- Monthly budget: $100,000
- Channels: Google Ads (50K), Meta (30K), LinkedIn (15K), Other (5K)
- Goals: B2B SaaS, $200 target CAC, need 500 leads/month

Output:

## Ad Spend Optimization Analysis

### Current Performance Audit

| Channel | Spend | Leads | CAC | ROAS | CPL |
|---------|-------|-------|-----|------|-----|
| Google Ads | $50,000 | 180 | $278 | 2.1x | $278 |
| Meta | $30,000 | 150 | $200 | 2.8x | $200 |
| LinkedIn | $15,000 | 45 | $333 | 1.5x | $333 |
| Other | $5,000 | 20 | $250 | 2.0x | $250 |
| **Total** | **$100,000** | **395** | **$253** | **2.2x** | **$253** |

**Current Status:**
- Missing target: 395 leads vs 500 goal (79%)
- Above CAC target: $253 vs $200 goal

---

### Channel Analysis

#### Google Ads ($50K)

| Breakdown | Spend | Leads | CAC | Assessment |
|-----------|-------|-------|-----|------------|
| Brand | $10,000 | 80 | $125 | ✅ Efficient |
| Non-Brand | $25,000 | 70 | $357 | ⚠️ High CAC |
| Display | $15,000 | 30 | $500 | ❌ Cut |

**Findings:**
- Brand is highly efficient but limited scale
- Non-brand has high CAC but scalable
- Display not performing—recommend pause

**Recommendations:**
- Increase brand budget (limited by search volume)
- Optimize non-brand (better audiences, negatives)
- Pause display, reallocate to Meta

---

#### Meta ($30K)

| Breakdown | Spend | Leads | CAC | Assessment |
|-----------|-------|-------|-----|------------|
| Lookalike | $15,000 | 85 | $176 | ✅ Best performer |
| Interest | $10,000 | 45 | $222 | ✅ Good |
| Retargeting | $5,000 | 20 | $250 | ✅ Standard |

**Findings:**
- Lookalike audiences are star performers
- Interest targeting has room to scale
- Strong overall channel

**Recommendations:**
- Increase lookalike budget significantly
- Test new lookalike seeds
- Shift underperforming Google budget here

---

#### LinkedIn ($15K)

| Breakdown | Spend | Leads | CAC | Assessment |
|-----------|-------|-------|-----|------------|
| Sponsored Content | $10,000 | 35 | $286 | ⚠️ High |
| Lead Gen Forms | $5,000 | 10 | $500 | ❌ Very high |

**Findings:**
- Highest CAC channel
- But: LinkedIn leads often higher quality (enterprise)
- Lead Gen Forms underperforming

**Recommendations:**
- Reduce overall LinkedIn spend
- Shift to sponsored content only
- Test LinkedIn for enterprise segment specifically

---

#### Other ($5K)

**Breakdown:** Reddit, Quora, programmatic
**Performance:** Mixed, small sample sizes

**Recommendation:** Continue testing but don't scale yet

---

### Recommended Budget Reallocation

#### Before vs After

| Channel | Current | Proposed | Change |
|---------|---------|----------|--------|
| Google Ads | $50,000 | $35,000 | -$15,000 |
| Meta | $30,000 | $50,000 | +$20,000 |
| LinkedIn | $15,000 | $8,000 | -$7,000 |
| Testing | $5,000 | $7,000 | +$2,000 |
| **Total** | **$100,000** | **$100,000** | - |

---

#### Detailed Allocation

**Google Ads ($35K)**
| Campaign | Budget | Expected Leads | Expected CAC |
|----------|--------|----------------|--------------|
| Brand | $12,000 | 95 | $126 |
| Non-Brand (optimized) | $23,000 | 75 | $307 |
| Display | $0 | 0 | - |
| **Total** | **$35,000** | **170** | **$206** |

**Meta ($50K)**
| Campaign | Budget | Expected Leads | Expected CAC |
|----------|--------|----------------|--------------|
| Lookalike | $30,000 | 170 | $176 |
| Interest | $15,000 | 65 | $231 |
| Retargeting | $5,000 | 20 | $250 |
| **Total** | **$50,000** | **255** | **$196** |

**LinkedIn ($8K)**
| Campaign | Budget | Expected Leads | Expected CAC |
|----------|--------|----------------|--------------|
| Sponsored Content | $8,000 | 28 | $286 |
| Lead Gen | $0 | 0 | - |
| **Total** | **$8,000** | **28** | **$286** |

**Testing ($7K)**
| Test | Budget | Purpose |
|------|--------|---------|
| TikTok | $3,000 | New channel |
| YouTube | $2,500 | Video content |
| Reddit (scale) | $1,500 | Community |

---

### Projected Results

| Metric | Current | Proposed | Change |
|--------|---------|----------|--------|
| Total Leads | 395 | 473 | +20% |
| Blended CAC | $253 | $211 | -17% |
| Goal Achievement | 79% | 95% | +16 pts |
| ROAS | 2.2x | 2.6x | +18% |

---

### Implementation Roadmap

**Week 1:**
- [ ] Pause Google Display campaigns
- [ ] Reduce LinkedIn Lead Gen to $0
- [ ] Increase Meta Lookalike by $5K

**Week 2:**
- [ ] Complete Google to Meta shift
- [ ] Launch TikTok test campaign
- [ ] Set up new Lookalike audiences

**Week 3-4:**
- [ ] Monitor and optimize
- [ ] Scale what's working
- [ ] Report on progress

---

### Monitoring Dashboard

┌────────────────────────────────────────────────────────┐ │ AD SPEND DASHBOARD Week 1 │ ├────────────────────────────────────────────────────────┤ │ │ │ SPEND PACING │ │ Google: ████████░░░░ $8,200/$8,750 (94%) │ │ Meta: █████████░░░ $11,800/$12,500 (94%) │ │ LinkedIn:██████░░░░░░ $1,500/$2,000 (75%) │ │ Testing: █████░░░░░░░ $900/$1,750 (51%) │ │ │ │ LEADS THIS WEEK │ │ Target: 118 | Actual: 124 | Pacing: +5% │ │ │ │ CAC THIS WEEK │ │ Target: $211 | Actual: $195 | Status: ✅ │ │ │ │ ALERTS: │ │ ⚠️ LinkedIn CPC up 15% - monitor │ │ ✅ Meta Lookalike exceeding expectations │ │ │ └────────────────────────────────────────────────────────┘

Example 2: Quick Channel Assessment

Input:

Should we increase spend on this channel?
- Channel: Facebook
- Current spend: $10K/month
- CAC: $150
- Target CAC: $200
- Leads: 67
- Need: More leads

Output:

## Channel Scale Assessment: Facebook

### Current Performance

| Metric | Value | Status |
|--------|-------|--------|
| Spend | $10,000 | Baseline |
| Leads | 67 | - |
| CAC | $150 | ✅ Below target |
| Headroom | $50 | Room to scale |

### Scale Recommendation: YES, but carefully

**Why scale:**
- CAC ($150) is 25% below target ($200)
- Indicates efficiency headroom
- Leads are needed

**How to scale:**

| Scenario | Spend | Expected Leads | Expected CAC |
|----------|-------|----------------|--------------|
| Conservative | $15,000 | 90 | $167 |
| Moderate | $20,000 | 110 | $182 |
| Aggressive | $25,000 | 125 | $200 |

**Recommendation:** Start with moderate (+$10K)

### Scaling Checklist

- [ ] Expand Lookalike audiences
- [ ] Test new interest targets
- [ ] Increase frequency caps gradually
- [ ] Monitor CAC weekly
- [ ] Set alert at $185 CAC

### Warning Signs (Stop Scaling)

- CAC exceeds $200
- CTR drops >20%
- Frequency >3.0
- Negative ROI on increment

Skill Boundaries

What This Skill Does Well

Analyzing channel performance
Recommending budget shifts
Calculating ROI projections
Creating optimization frameworks

What This Skill Cannot Do

Access your ad accounts
Make real-time bid changes
Know your specific creative
Guarantee performance

Iteration Guide

Follow-up Prompts:

"Analyze [specific channel] performance"
"How should we test [new channel]?"
"Create a pacing dashboard for [budget]"
"What's causing [performance issue]?"

References

Google Ads Optimization Guide
Meta Business Suite Best Practices
LinkedIn Marketing Solutions
AdEspresso Budget Allocation

Related Skills

google-ads-expert - Google-specific
aarrr-metrics - Full funnel view
growth-loops - Sustainable growth

Skill Metadata

Domain: Acquisition
Complexity: Intermediate-Advanced
Mode: centaur
Time to Value: 2-3 hours per analysis
Prerequisites: Ad account access, performance data

Source

git clone https://github.com/guia-matthieu/clawfu-skills/blob/main/skills/acquisition/ad-spend-optimizer/SKILL.mdView on GitHub

Overview

Ad Spend Optimizer systematically allocates paid media budgets across channels by analyzing performance data, attribution models, and ROI targets. It blends marginal ROI optimization with portfolio theory to balance efficiency and scale, guiding data-driven budget decisions.

How This Skill Works

The tool audit performance by channel, collects metrics (ROAS, CAC, CPA, CTR, conversion rate), and applies multiple attribution models (Last Click, First Click, Linear, Time Decay, Position, Data-Driven). It then computes marginal ROI per channel to identify underinvested opportunities, prescribes allocation shifts, and uses a structured test framework (70/20/10) to implement and monitor results.

When to Use It

Quarterly budget planning
Channel mix optimization
Performance troubleshooting
Scaling paid acquisition
ROI analysis and reporting

Quick Start

Step 1: Audit current performance by channel and set clear ROAS, CAC, and lead targets.
Step 2: Run attribution models to assess credit distribution and identify optimization opportunities.
Step 3: Compute marginal ROI, implement allocation changes, and monitor results with the 70/20/10 test framework.

Best Practices

Audit current performance with clear metrics and targets (ROAS, CAC, CPA, CTR, Conv Rate) for each channel.
Use multiple attribution models (including data-driven) to compare how credit is assigned across touchpoints.
Apply marginal ROI thinking to identify next best dollar and detect diminishing returns.
Follow a test framework (70% proven channels, 20% optimization tests, 10% new channel experiments).
Build dashboards to monitor ROI, track changes, and adjust allocations in real time.

Example Use Cases

Example 1: Full Budget Optimization — a $100k monthly budget across Google Ads, Meta, LinkedIn, and Other, audits performance, and recommends reallocations to meet 500 leads/month while improving CAC and ROAS.
Example 2: Quarterly planning for a B2B SaaS with CAC target and lead goals; reallocates to scalable channels while respecting budget constraints and risk tolerance.
Example 3: Attribution-driven optimization — compares Last Click vs Data-Driven models to reweight spend toward channels with higher credit across the funnel.
Example 4: Performance troubleshooting — pausing underperforming Display campaigns and shifting funds to high-ROAS Brand and Non-Brand initiatives.
Example 5: ROI reporting dashboard — ongoing ROI analysis with updated allocation plans and progress toward targets, enabling repeatable optimization cycles.

Frequently Asked Questions

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