Ad Spend Optimizer

Ad Spend Optimizer

> Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC.

When to Use This Skill

• Quarterly budget planning — reallocate spend based on performance data
• Channel mix optimization — find the right balance across platforms
• Performance troubleshooting — diagnose why CAC is rising or ROAS declining
• Scaling decisions — determine if a channel has headroom to scale
• New channel testing — structure test budgets with clear success criteria

Methodology Foundation

| Aspect | Details | |--------|---------| | Source | Marginal ROI optimization + portfolio theory for marketing | | Core Principle | Allocate each dollar where the marginal return is highest — shift spend from diminishing-returns channels to underspent ones | | Framework | 70/20/10 — 70% proven channels, 20% optimization tests, 10% new channel experiments |

What Claude Does vs What You Decide

| Claude Does | You Decide | |-------------|------------| | Calculates ROAS, CAC, and CPL per channel and campaign | Total budget constraints | | Identifies diminishing returns and reallocation opportunities | Risk tolerance for new channels | | Models projected outcomes for different allocation scenarios | Business priorities and brand considerations | | Creates monitoring dashboards and alert thresholds | Platform selection and creative direction |

Instructions

Step 1: Audit Current Performance

Collect these metrics per channel and campaign:

| Metric | Formula | Healthy Range | |--------|---------|---------------| | ROAS | Revenue ÷ Ad Spend | >3:1 for most B2B/B2C | | CAC | Ad Spend ÷ New Customers | 1% search, >0.5% social | | Conv Rate | Conversions ÷ Clicks | >2% landing pages |

Validation checkpoint: If data is missing for any channel, flag it — incomplete data leads to wrong reallocations.

Step 2: Attribution Analysis

Choose the model that matches the business:

| Model | Best For | Trade-off | |-------|----------|-----------| | Last Click | Direct response, short cycles | Ignores awareness | | First Click | Awareness campaigns | Ignores conversion assist | | Linear | Balanced multi-touch view | Dilutes signal | | Time Decay | Shorter sales cycles | Biases toward bottom-funnel | | Position-Based | Balanced with emphasis | May miss mid-funnel | | Data-Driven | Sophisticated, enough data | Requires volume |

Step 3: Calculate Marginal ROI

For each channel, answer: Where does the next $1 produce the most return?

| Signal | Meaning | Action | |--------|---------|--------| | CAC well below target | Headroom to scale | Increase spend 50%, monitor weekly | | CAC at target | Optimized | Maintain, test creative | | CAC above target | Diminishing returns | Reduce spend, reallocate | | Low volume, good CAC | Underinvested | Scale cautiously (2x) | | High volume, rising CAC | Hitting ceiling | Cap spend, diversify |

Step 4: Model Reallocation Scenarios

Build 3 scenarios (conservative, moderate, aggressive) showing projected leads, CAC, and ROAS at each budget level. Include:

• Per-channel breakdowns with expected performance
• Warning thresholds — CAC levels that trigger spend cuts
• Implementation timeline — weekly changes, not all at once

Step 5: Implement and Monitor

Weekly monitoring checklist:

• [ ] Spend pacing vs. plan
• [ ] CAC by channel vs. target
• [ ] Lead volume vs. forecast
• [ ] Any channel crossing warning threshold?

Scaling rule: If CAC stays 15%+ below target for 2 consecutive weeks, increase spend by 25%. If CAC exceeds target for 2 weeks, reduce by 25%.

Examples

Example: B2B SaaS Budget Reallocation

Input: $100K/month — Google ($50K), Meta ($30K), LinkedIn ($15K), Other ($5K). Target: $200 CAC, 500 leads/month. Current: 395 leads, $253 CAC.

Diagnosis:

• Google Display ($15K → 30 leads, $500 CAC) — cut entirely
• Meta Lookalike ($15K → 85 leads, $176 CAC) — star performer, scale
• LinkedIn Lead Gen ($5K → 10 leads, $500 CAC) — cut

Proposed reallocation:

| Channel | Current | Proposed | Expected CAC | |---------|---------|----------|-------------| | Google Ads | $50K | $35K | $206 | | Meta | $30K | $50K | $196 | | LinkedIn | $15K | $8K | $286 | | Testing | $5K | $7K | Variable |

Projected result: 473 leads (+20%), $211 CAC (-17%).

Skill Boundaries

What This Skill Does Well

• Analyzing multi-channel ad performance from provided data
• Recommending budget shifts based on marginal ROI
• Modeling reallocation scenarios with projected outcomes
• Creating monitoring frameworks with alert thresholds

What This Skill Cannot Do

• Access ad platform accounts or pull live data
• Make real-time bid adjustments or campaign changes
• Evaluate creative quality (headlines, images, video)
• Account for brand lift or offline conversion effects

References

• Google Ads Optimization Guide
• Meta Business Suite Best Practices
• LinkedIn Marketing Solutions
• Common Thread Collective — ad spend allocation methodology

Related Skills

• google-ads-expert — Google-specific campaign optimization
• aarrr-metrics — Full funnel view beyond paid acquisition
• growth-loops — Sustainable growth beyond paid channels

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: guia-matthieu
• Source: guia-matthieu/clawfu-skills
• License: MIT
• Homepage: https://clawfu.com

Install and usage instructions live in the source repository linked above.