--- 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" --- # 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 attribution: Cody-W-Tucker Original project: https://github.com/Cody-W-Tucker/skills/blob/main/skills/ad-spend-optimizer/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: Cody-W-Tucker Original project: https://github.com/Cody-W-Tucker/skills/blob/main/skills/ad-spend-optimizer/SKILL.md Attribution basis: Source repository record Fields of work: Marketing, Sales Roles: Advertising Specialist, Growth Marketer, Marketing Analyst, Product Marketer, Sales Development Rep (SDR) Specialties: API Tools: Google Ads, Meta Ads Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 02147ca9fe07088f66c89968b020481aa578790b89d764696d070486bbc3ce1b