Ad spend optimizer skill

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

by guia-matthieu·MIT license·GitHub ↗

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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 <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.

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
  • google-ads-expert — Google-specific campaign optimization
  • aarrr-metrics — Full funnel view beyond paid acquisition
  • growth-loops — Sustainable growth beyond paid channels
1---
2name: ad-spend-optimizer
3description: "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."
4license: MIT
5metadata:
6 author: ClawFu
7 version: 1.1.0
8 mcp-server: "@clawfu/mcp-skills"
9---
10 
11# Ad Spend Optimizer
12 
13> Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC.
14 
15## When to Use This Skill
16 
17- **Quarterly budget planning** — reallocate spend based on performance data
18- **Channel mix optimization** — find the right balance across platforms
19- **Performance troubleshooting** — diagnose why CAC is rising or ROAS declining
20- **Scaling decisions** — determine if a channel has headroom to scale
21- **New channel testing** — structure test budgets with clear success criteria
22 
23## Methodology Foundation
24 
25| Aspect | Details |
26|--------|---------|
27| **Source** | Marginal ROI optimization + portfolio theory for marketing |
28| **Core Principle** | Allocate each dollar where the marginal return is highest — shift spend from diminishing-returns channels to underspent ones |
29| **Framework** | 70/20/10 — 70% proven channels, 20% optimization tests, 10% new channel experiments |
30 
31## What Claude Does vs What You Decide
32 
33| Claude Does | You Decide |
34|-------------|------------|
35| Calculates ROAS, CAC, and CPL per channel and campaign | Total budget constraints |
36| Identifies diminishing returns and reallocation opportunities | Risk tolerance for new channels |
37| Models projected outcomes for different allocation scenarios | Business priorities and brand considerations |
38| Creates monitoring dashboards and alert thresholds | Platform selection and creative direction |
39 
40## Instructions
41 
42### Step 1: Audit Current Performance
43 
44Collect these metrics per channel and campaign:
45 
46| Metric | Formula | Healthy Range |
47|--------|---------|---------------|
48| **ROAS** | Revenue ÷ Ad Spend | >3:1 for most B2B/B2C |
49| **CAC** | Ad Spend ÷ New Customers | <LTV ÷ 3 |
50| **CPL** | Ad Spend ÷ Leads | Varies by industry |
51| **CTR** | Clicks ÷ Impressions | >1% search, >0.5% social |
52| **Conv Rate** | Conversions ÷ Clicks | >2% landing pages |
53 
54**Validation checkpoint:** If data is missing for any channel, flag it — incomplete data leads to wrong reallocations.
55 
56### Step 2: Attribution Analysis
57 
58Choose the model that matches the business:
59 
60| Model | Best For | Trade-off |
61|-------|----------|-----------|
62| Last Click | Direct response, short cycles | Ignores awareness |
63| First Click | Awareness campaigns | Ignores conversion assist |
64| Linear | Balanced multi-touch view | Dilutes signal |
65| Time Decay | Shorter sales cycles | Biases toward bottom-funnel |
66| Position-Based | Balanced with emphasis | May miss mid-funnel |
67| Data-Driven | Sophisticated, enough data | Requires volume |
68 
69### Step 3: Calculate Marginal ROI
70 
71For each channel, answer: **Where does the next $1 produce the most return?**
72 
73| Signal | Meaning | Action |
74|--------|---------|--------|
75| CAC well below target | Headroom to scale | Increase spend 50%, monitor weekly |
76| CAC at target | Optimized | Maintain, test creative |
77| CAC above target | Diminishing returns | Reduce spend, reallocate |
78| Low volume, good CAC | Underinvested | Scale cautiously (2x) |
79| High volume, rising CAC | Hitting ceiling | Cap spend, diversify |
80 
81### Step 4: Model Reallocation Scenarios
82 
83Build 3 scenarios (conservative, moderate, aggressive) showing projected leads, CAC, and ROAS at each budget level. Include:
84 
85- **Per-channel breakdowns** with expected performance
86- **Warning thresholds** — CAC levels that trigger spend cuts
87- **Implementation timeline** — weekly changes, not all at once
88 
89### Step 5: Implement and Monitor
90 
91**Weekly monitoring checklist:**
92- [ ] Spend pacing vs. plan
93- [ ] CAC by channel vs. target
94- [ ] Lead volume vs. forecast
95- [ ] Any channel crossing warning threshold?
96 
97**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%.
98 
99## Examples
100 
101### Example: B2B SaaS Budget Reallocation
102 
103**Input:** $100K/month — Google ($50K), Meta ($30K), LinkedIn ($15K), Other ($5K). Target: $200 CAC, 500 leads/month. Current: 395 leads, $253 CAC.
104 
105**Diagnosis:**
106- Google Display ($15K → 30 leads, $500 CAC) — cut entirely
107- Meta Lookalike ($15K → 85 leads, $176 CAC) — star performer, scale
108- LinkedIn Lead Gen ($5K → 10 leads, $500 CAC) — cut
109 
110**Proposed reallocation:**
111 
112| Channel | Current | Proposed | Expected CAC |
113|---------|---------|----------|-------------|
114| Google Ads | $50K | $35K | $206 |
115| Meta | $30K | $50K | $196 |
116| LinkedIn | $15K | $8K | $286 |
117| Testing | $5K | $7K | Variable |
118 
119**Projected result:** 473 leads (+20%), $211 CAC (-17%).
120 
121## Skill Boundaries
122 
123### What This Skill Does Well
124- Analyzing multi-channel ad performance from provided data
125- Recommending budget shifts based on marginal ROI
126- Modeling reallocation scenarios with projected outcomes
127- Creating monitoring frameworks with alert thresholds
128 
129### What This Skill Cannot Do
130- Access ad platform accounts or pull live data
131- Make real-time bid adjustments or campaign changes
132- Evaluate creative quality (headlines, images, video)
133- Account for brand lift or offline conversion effects
134 
135## References
136 
137- Google Ads Optimization Guide
138- Meta Business Suite Best Practices
139- LinkedIn Marketing Solutions
140- Common Thread Collective — ad spend allocation methodology
141 
142## Related Skills
143 
144- `google-ads-expert` — Google-specific campaign optimization
145- `aarrr-metrics` — Full funnel view beyond paid acquisition
146- `growth-loops` — Sustainable growth beyond paid channels
147 

Discussion

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