google-ads-expert
Build profitable Google Ads campaigns by applying Perry Marshall's 80/20 principles to paid search optimization Use when: **Setting up a new Google Ads…
Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad budget allocation, diagnosing underperforming ad channels, deciding whether to scale spend on a channel, calculating marginal
$ npx -y skills add guia-matthieu/clawfu-skills --skill ad-spend-optimizer --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/ad-spend-optimizerContext preview
The summary Claude sees to decide when to auto-load this skill.
Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad budget allocation, diagnosing underperforming ad channels, deciding whether to scale spend on a channel, calculating marginal
name: ad-spend-optimizer description: "Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad budget allocation, diagnosing underperforming ad channels, deciding whether to scale spend on a channel, calculating marginal ROI across Google Ads, Meta, LinkedIn, or TikTok, rebalancing media mix after performance shifts, or setting up a test-and-scale framework for new channels." license: MIT metadata: author: ClawFu version: 1.1.0 mcp-server: "@clawfu/mcp-skills"
> Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC.
| 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 |
| 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 |
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 | <LTV ÷ 3 | | **CPL** | Ad Spend ÷ Leads | Varies by industry | | **CTR** | Clicks ÷ Impressions | >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.
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 |
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 |
Build 3 scenarios (conservative, moderate, aggressive) showing projected leads, CAC, and ROAS at each budget level. Include:
**Weekly monitoring checklist:**
**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%.
**Input:** $100K/month — Google ($50K), Meta ($30K), LinkedIn ($15K), Other ($5K). Target: $200 CAC, 500 leads/month. Current: 395 leads, $253 CAC.
**Diagnosis:**
**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%).
175 expert marketing methodologies for AI agents. Free. Open source. MIT licensed. Dunford on positioning. Schwartz on copywriting. Cialdini on persuasion. Ogilvy on advertising. Hormozi on offers. Voss on negotiation.
Repo: guia-matthieu/clawfu-skills
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