Multi-channel budget optimization using MER, marginal ROAS, and diminishing returns analysis. Use when pasting multi-channel spend and results data, requesting reallocation recommendations, analyzing budget shift priorities, or optimizing marketing efficiency across Google, Meta, TikTok, and other channels. Platform: Google and Meta.
npx skills add https://github.com/irinabuht12-oss/marketing-skills --skill ad-spend-allocator
Optimize budget distribution across advertising channels using efficiency metrics and diminishing returns analysis.
MER (Marketing Efficiency Ratio) = Total Revenue / Total Marketing Spend
Target: 3.0-5.0x (varies by industry, margin structure)
aMER (Acquisition MER) = New Customer Revenue / Total Ad Spend
Purpose: Isolates new customer acquisition efficiency
Channel ROAS = Channel Revenue / Channel Spend
Use for: Channel comparison, baseline performance
Marginal ROAS = (Revenue at Spend B - Revenue at Spend A) / (Spend B - Spend A)
Purpose: Detect diminishing returns before blended ROAS shows issues
| Tier | Allocation | Criteria |
|------|------------|----------|
| Proven | 70% | Consistent ROAS, predictable results, 3+ months track record |
| Scaling | 20% | Emerging opportunities, positive early signals, testing scale |
| Testing | 10% | New channels, creative experiments, unproven strategies |
## Budget Allocation Analysis
**Total Monthly Spend**: $[X]
**Current MER**: [X]x
**Target MER**: [X]x
### Channel Efficiency Summary
| Channel | Spend | Revenue | ROAS | Marginal ROAS | Status |
|---------|-------|---------|------|---------------|--------|
| | | | | | [Scaling/Saturated/Testing] |
### Diminishing Returns Detection
- [Channel]: [Evidence of saturation]
### Recommended Reallocation
| From | To | Amount | Rationale |
|------|-----|--------|-----------|
### 70-20-10 Current vs Recommended
| Tier | Current | Recommended | Gap |
|------|---------|-------------|-----|
| Proven (70%) | | | |
| Scaling (20%) | | | |
| Testing (10%) | | | |
### Expected Impact
- Projected MER improvement: [X]%
- Confidence: [HIGH/MEDIUM/LOW]
### Monitoring Plan
- Review metrics in [X] days
- Key indicators to watch: [List]
Input: "Google Ads: $50K, $200K revenue. Meta: $30K, $90K revenue. TikTok: $10K, $25K revenue. Seeing Meta CPMs up 35% last month."
Output:
## Budget Allocation Analysis
**Total Monthly Spend**: $90,000
**Current MER**: 3.5x ($315K / $90K)
### Channel Efficiency Summary
| Channel | Spend | Revenue | ROAS | Status |
|---------|-------|---------|------|--------|
| Google | $50K (56%) | $200K | 4.0x | Scaling |
| Meta | $30K (33%) | $90K | 3.0x | ⚠️ Saturated |
| TikTok | $10K (11%) | $25K | 2.5x | Testing |
### Diminishing Returns Detection
- **Meta**: 35% CPM increase signals auction saturation/creative fatigue
### Recommended Reallocation
| From | To | Amount | Rationale |
|------|-----|--------|-----------|
| Meta | Google | $6K (20%) | Higher efficiency, room to scale |
| Meta | TikTok | $3K (10%) | Test scaling opportunity |
### Expected Impact
- Projected MER improvement: 8-12%
- Confidence: MEDIUM (need marginal ROAS data for precision)
### Monitoring Plan
- Review in 14 days
- Watch: Google CPC trends, TikTok conv rate, Meta frequency
This skill works best with live account data. Connect the free Ryze MCP once and Claude reads your Google Ads, Meta Ads, GA4 and Search Console directly:
https://connector.get-ryze.ai/mcpclaude mcp add ryze --transport http https://connector.get-ryze.ai/mcpSetup guide: https://www.get-ryze.ai/how-to-connect-claude-to-google-meta-ads-mcp
Take irinabuht12-oss/ad-spend-allocator from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.