ad-spend-allocator

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).

Applying Incrementality to Allocation

If platform-reported ROAS is 5.0x but incrementality test shows 60% lift, actual incremental ROAS is 3.0x. Use the incremental number for allocation decisions.

Channel	Reported ROAS	Incrementality	True Incremental ROAS	Action
Google brand	15x	20% lift	3.0x	Defensive only — don't scale beyond natural demand
Google non-brand	4x	85% lift	3.4x	Primary driver — scale aggressively
Meta prospecting	3x	75% lift	2.25x	Scaling channel — monitor marginal
Meta retargeting	8x	30% lift	2.4x	Support — don't pay to reach already-converting users

Budget Forecasting

Quarterly Budget Modeling

Build a forecast that accounts for:

1. Historical seasonality — Previous year's monthly index
2. CPM trend — Meta's avg ad price climbed 9% in 2025 (Meta Q1 2025 earnings); similar pressure on Google
3. Planned initiatives — New product launches, geo expansions, creative refreshes
4. Diminishing returns — Don't forecast linearly past saturation zones

Simple Forecast Template

Q[X] Forecast:
  Base spend = [previous quarter adjusted for seasonality]
  CPM inflation factor = +9% (2025 Meta), +[X]% (Google)
  Expected MER = [trend analysis + planned improvements]
  Forecast revenue = Forecast spend × Expected MER
  
  Confidence range: ±15% (narrower if stable, wider if volatile)

Scenario Planning

Always build three scenarios:

Scenario	Spend Change	Expected Outcome	Risk
Conservative	Flat or -10%	Maintain current efficiency	Missing growth opportunity
Base case	+15-20%	Scale within efficient frontier	Typical execution risk
Aggressive	+40-50%	Capture growth, risk efficiency decay	Higher CPA, learning resets

CPM Trend Context (2025-2026)

Platform	2025 CPM Movement	Implication
Meta	+9% avg ad price (Q1 2025 earnings)	Rising costs compress MER — not always a performance issue
Google Search	Competitive pressure on non-brand	Growing Smart Bidding adoption pushes auctions
YouTube	Relatively stable	Still cost-efficient for reach
TikTok	High volatility	Check quarterly
LinkedIn	Premium pricing	Viable for B2B only

Implication for allocation: If channel ROAS declined 10% YoY but CPMs rose 9%, the underlying efficiency is roughly flat. Don't cut a channel that's fighting macro pressure successfully.

Reallocation Decision Framework

Step 1: Calculate Current State

Channel	Spend	Revenue	ROAS	Marginal ROAS	Incrementality	Role
[channel]	$[X]	$[X]	[X]x	[X]x	[X]% lift	[role]

Step 2: Identify Imbalances

• Channels with marginal ROAS > target → candidates for scaling
• Channels with marginal ROAS < target → candidates for cutting or restructuring
• Channels at saturation → hold, don't scale
• Channels with weak incrementality → evaluate true value

Step 3: Size the Move

Conservative reallocation: Shift 10-15% of budget at a time. Observe for 14-21 days before next move.

Why not 50%? Big reallocations destabilize learning on both sides. The receiving channel enters learning phase, the losing channel loses momentum. Gradual > dramatic.

Step 4: Expected Impact Calculation

Expected new revenue = 
  (Remaining spend on channel A × ROAS_A) +
  (New spend on channel B × Marginal_ROAS_B) +
  (Original spend on channel B × ROAS_B)

If expected new revenue > current revenue, the shift is value-creating.

Output Format

## Ad Spend Allocation Analysis

### Current State
| Channel | Spend | Revenue | ROAS | Marginal ROAS | Role |
|---------|-------|---------|------|--------------|------|

**Blended MER:** [X]x (target: [X]x)
**Total Spend:** $[X]/month
**Total Revenue:** $[X]/month

### Key Findings
- [Channel X] is [under-invested / saturating / over-invested]
- Marginal ROAS gap: [specific]
- Incrementality concern: [if applicable]
- CPM trend context: [Meta +9%, etc.]

### Reallocation Recommendations
| Move | From | To | Amount | Expected Impact |
|------|------|-----|--------|----------------|
| 1 | [channel] | [channel] | $[X] (X% of budget) | [specific outcome] |

### Quarterly Forecast
| Scenario | Total Spend | Expected MER | Expected Revenue |
|----------|------------|-------------|-----------------|
| Conservative | $[X] | [X]x | $[X] |
| Base | $[X] | [X]x | $[X] |
| Aggressive | $[X] | [X]x | $[X] |

### Testing & Validation Plan
- [Incrementality tests to run]
- [Monitoring cadence]
- [Rollback criteria]

Example

Input: "DTC skincare brand. Monthly spend: Google $18K (Search + PMax), Meta $22K (ASC + prospecting), TikTok $5K, YouTube $3K. Revenue last month: $165K. I'm told by the CMO to either cut 20% or justify why not. Where should I push and where should I cut?"

Output:

Ad Spend Allocation Analysis

Current State

Channel	Spend	Revenue (platform-reported)	ROAS	Est. Marginal ROAS	Role
Google Search (non-brand)	$8K	$32K	4.0x	3.5x	Primary driver
Google Search (brand)	$2K	$28K	14.0x	5.0x (incremental)	Fill/defense
Google PMax	$8K	$26K	3.25x	2.5x	Scaling
Meta ASC	$15K	$48K	3.2x	2.4x	Primary driver
Meta prospecting	$7K	$14K	2.0x	1.8x	Scaling
TikTok	$5K	$8K	1.6x	Unknown	Experimental
YouTube	$3K	$5K	1.67x	Unknown	Support/MOFU

Blended platform ROAS (sum): 4.0x ($161K/$48K) Actual MER: $165K ÷ $48K = 3.44x Attribution overlap: ~14% (platform sum overcounts by this much)

Key Findings

1. TikTok and Meta prospecting are below target — marginal ROAS underwater. These are the obvious cut candidates IF incrementality doesn't justify them
2. Google brand search is defensive — high reported ROAS (14x) but likely 80% would happen organically. True incremental ROAS ~3.0x. Don't scale, don't cut
3. Google Search non-brand is primary — healthy marginal ROAS at 3.5x, room to scale
4. Meta ASC is the workhorse — 3.2x with 15K spend. Still room before saturation
5. YouTube is underfunded for meaningful signal — $3K/mo can't support a real test. Either commit to $10K+ or kill it
6. CPM context: Meta +9% in 2025 means Meta ASC efficiency is holding up against pressure — don't punish it for macro costs

Reallocation Recommendations (don't cut 20% — shift 20%)

Priority	Move	From	To	Amount	Expected Impact
1	Kill TikTok test OR commit $10K+	TikTok	Google Search non-brand	$5K	+$17-18K revenue at 3.5x marginal
2	Reduce Meta prospecting	Meta prospecting	Meta ASC	$3K	ASC has better marginal; prospecting feeds ASC but at 1.8x marginal, it's too expensive
3	Kill YouTube at this scale	YouTube	Google Search non-brand	$3K	$3K is below YouTube's signal threshold — reallocate
4	Net result:			No cut; reallocation	Expected MER lift to 3.7x

Counter-Argument to Cutting 20%

Cutting 20% ($9.6K) proportionally would:

• Cut $1.6K from Google non-brand (the best performer) — value destructive
• Cut $3K from Meta ASC — value destructive
• Cut $1.6K from Google brand — unnecessary, brand defense is cheap

Instead: Shift the same $9.6K from underperformers to overperformers. Keep total spend at $48K, raise expected revenue to ~$177K, MER moves from 3.44x → ~3.69x.

Quarterly Forecast (next 3 months)

Scenario	Monthly Spend	Expected MER	Expected Revenue	Notes
Conservative (CMO's cut)	$38.4K	3.5x	$134K/mo	Value destructive
Base (reallocation)	$48K	3.7x	$178K/mo	Recommended
Aggressive (scale winners)	$58K	3.5x	$203K/mo	Requires creative refresh pipeline

Incrementality Testing Plan

Before any major move, run:

1. Google brand holdout test (1 month) — confirm true incremental value
2. Meta prospecting geo-split test — verify it's feeding ASC vs wasted
3. YouTube investment test — if keeping, commit $10K for 60 days to get real signal

Monitoring Plan

• Weekly MER tracking vs 3.7x target
• Marginal ROAS calculation monthly per channel
• CPM trend overlay — separate cost pressure from performance decay
• Rollback: if reallocation drops MER below 3.3x after 30 days, revert

Guidelines

• Don't sum platform ROAS and call it blended ROAS. Platforms overclaim. Use MER (total revenue ÷ total spend) as the north star.
• Don't make allocation decisions on platform-reported ROAS alone. Platform ROAS overcounts by 10-30% typically. Incrementality testing is the gold standard.
• Don't shift more than 20% of a channel's budget at once. Destabilizes learning on both sides. Gradual reallocation (10-15% moves) preserves performance.
• Don't kill a "low ROAS" channel without testing incrementality. It may be feeding your "high ROAS" channel. Run a holdout test first.
• Don't ignore diminishing returns. Scaling past the efficient frontier wastes money. Monitor marginal ROAS, not just average.
• Don't forecast linearly. Diminishing returns are nonlinear. 2x budget ≠ 2x revenue in most channels.
• Don't conflate CPM inflation with performance decay. Meta +9% CPM in 2025 means some "decline" is macro, not campaign failure.
• Don't run $3K monthly tests and expect signal. Each channel has a minimum signal threshold. Under-funded experiments produce noise.
• Don't assume MMM output without incrementality calibration. Google Meridian and other MMMs need incrementality experiments to calibrate. Uncalibrated MMM is directionally useful but not decisive.
• Cross-references: For within-platform optimization, use the google-ads-optimizer and meta-ads-optimizer skills. For channel-specific analysis, use the campaign-analyzer skill. For reporting the allocation story to stakeholders, use the paid-media-reporter skill.
• Confidence: Target MER benchmarks vary by industry, margin, and LTV. CPM trend data from Meta Q1 2025 earnings may shift quarter to quarter. Incrementality ranges are illustrative — your account will differ.