ad-spend-allocator

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• SKILL.md

SKILL.md (first lines)

## Process

1. **Collect channel data** — Spend, revenue/conversions, and time period per channel
2. **Calculate core metrics** — MER, channel ROAS, blended CAC
3. **Assess marginal efficiency** — Where is each channel on its diminishing returns curve?
4. **Assign channel roles** — Primary driver, scaling channel, support channel, fill channel
5. **Model reallocation scenarios** — What happens if you shift $X from channel A to B?
6. **Factor incrementality** — Adjust for platform-reported vs actual incremental lift
7. **Build forecast** — Quarterly plan with seasonality and CPM trend context
8. **Recommend moves** — ICE-prioritized action plan with expected outcomes

## Core Metrics Framework

### MER vs Platform ROAS

**Marketing Efficiency Ratio (MER)** = Total Revenue ÷ Total Ad Spend (across ALL channels)

MER is the north star metric. Platform-reported ROAS overcounts because of attribution overlap — Google and Meta both claim credit for the same sale.

| Metric | What It Measures | Use For |
|--------|-----------------|---------|
| **MER** | Blended efficiency across all channels | Strategic planning, board reporting |
| **Platform ROAS** | Platform-attributed revenue ÷ platform spend | Within-platform optimization |
| **Incremental ROAS** | True lift over holdout baseline | Channel investment decisions |
| **Marginal ROAS** | ROAS on the *last* dollar of spend | Allocation decisions |
| **Blended CAC** | Total spend ÷ new customers | Efficiency per new customer |
| **LTV:CAC** | Customer lifetime value ÷ CAC | Long-term channel viability |

**Rule of thumb:** If platform ROAS sums to more than your actual MER, you have attribution overlap. The gap = overclaimed conversions.

### Target MER Benchmarks (Starting Points)

| Business Type | Healthy MER | Notes |
|--------------|-------------|-------|
| DTC ecommerce (physical products) | 3.0-5.0x | Lower for high-margin, higher for low-margin |
| Subscription/SaaS (LTV-driven) | 1.5-3.0x on first purchase | Needs 6-12 month LTV analysis |
| Lead gen (service businesses) | CPL-based, not MER | Use CAC targets instead |
| Luxury/high-AOV | 5.0-10.0x | Can tolerate higher thresholds |

**Confidence note:** MER benchmarks vary significantly by industry, margin structure, and LTV. These are starting points — calibrate to your actual unit economics.

## Marginal ROAS & Diminishing Returns

The key insight: ROAS is not linear with spend. Every channel has a curve.

### The Diminishing Returns Curve

| Zone | Characteristic | Marginal ROAS Behavior | Action |
|------|---------------|----------------------|--------|
| **Under-invested** | Budget capped, high impression share gaps | Marginal ROAS > average ROAS | Scale up aggressively |
| **Efficient frontier** | Sweet spot — spend matches demand | Marginal ROAS ≈ average ROAS | Maintain, test small increments |
| **Saturating** | Diminishing returns setting in | Marginal ROAS < average ROAS but above target | Scale carefully, watch for decay |
| **Over-invested** | Wasting spend on low-intent audiences | Marginal ROAS < target | Cut budget, reallocate |

### Identifying Your Position on the Curve

**Signals of under-investment:**
- Google Search impression share <30% (Lost IS Budget)
- Meta ad set frequency <1.5 (not reaching enough of audience)
- CPA/ROAS stable as spend increased (no efficiency loss)
- Low reach % of eligible audience

**Signals of saturation:**
- Google Search impression share >80-95%
- Meta frequency >4 with rising CPA
- ROAS declining as spend increased
- Diminishing absolute conversion growth per budget dollar

**Practical calculation of marginal ROAS:**
```
Marginal ROAS = (Revenue at spend level B − Revenue at spend level A) ÷ (Spend B − Spend A)
```

Example: Channel grew from $10K/mo ($40K rev, 4.0x ROAS) to $15K/mo ($55K rev, 3.67x ROAS).
- Marginal revenue = $55K − $40K = $15K
- Marginal spend = $5K
- Marginal ROAS = $15K ÷ $5K = **3.0x** (vs 3.67x average)
- Interpretation: The last $5K came at 3.0x. If target is 3.5x, this channel is saturating.

## Channel Role Assignment (Power Pack Thinking)

Not every channel needs to be an efficiency champion. Assign roles:

| Role | Purpose | ROAS Expectation | Example Channels |
|------|---------|-----------------|-----------------|
| **Primary driver** | Bulk of revenue, proven efficient | At or above MER target | Google Search non-brand, Meta Advantage+ Shopping |
| **Scaling channel** | Unlocking new audiences/growth | Slightly below primary, high incremental | Meta prospecting, Google PMax |
| **Support/mid-funnel** | Consideration and warming | Lower direct ROAS, assist-heavy | YouTube, Meta video, Demand Gen |
| **Fill/hygiene** | Brand defense, retargeting | Very high ROAS but low volume | Google brand, retargeting |
| **Experimental** | Testing new channels | Unknown, budget-capped | TikTok, Pinterest, LinkedIn new launch |

**Google Power Pack (2025):** Demand Gen → AI Max → PMax working together. Demand Gen creates interest at the top, AI Max captures it in search, PMax scales at the bottom. Evaluate as a system, not as siloed channels.

**Agency mistake to avoid:** Killing a "low ROAS" channel that's actually feeding your "high ROAS" channel. Run incrementality tests before defunding a suspected assist channel.

## Incrementality-Informed Allocation

Platform attribution lies. Incrementality testing tells the truth.

### Incrementality Hierarchy (highest to lowest evidence quality)

| Method | Evidence Quality | Effort |
|--------|-----------------|--------|
| Geo-based holdout tests | Highest | High — requires geo split |
| Conversion lift tests (Google, Meta native) | High | Medium — use platform tools |
| Marketing Mix Modeling (Google Meridian) | High for strategic | High — needs data science |
| Ghost ads / PSA tests | Medium-High | Medium |
| Pre-post analysis on pause events | Medium | Low (opportunistic) |
| Platform-reported ROAS alone | Low | Zero — but unreliable |

### When Incrementality Testing Is Worth It

- Before major budget shifts ($10K+ reallocations)
- When considering pausing a channel
- When two channels overlap heavily (brand search + Meta retargeting)
- Annually for MMM refresh

**Google Meridian (2025):** Google's open-source Marketing Mix Modeling framework. Incorporates granular video and Search signals. Calibrate with conversion lift tests. Best for organizations with data science capability or larger spenders ($1M+/year).

From narayan-metaflow/metaflow-marketing-skills, MIT.

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