ad-spend-allocator

Ad Spend Allocator

Take performance data from multiple ad channels and figure out where your next dollar should go. This skill compares channels on equal terms, identifies where you're over-spending vs under-spending relative to results, and produces a concrete budget reallocation plan.

Core principle: Most startups either spread budget too thin across channels (no channel gets enough to learn) or dump everything into one channel (missing cheaper opportunities elsewhere). This skill finds the right distribution.

When to Use

• "How should I split my ad budget?"
• "Should I spend more on Google or Meta?"
• "Reallocate my ad spend across channels"
• "Where am I getting the best return?"
• "I have $X/month for ads — how should I distribute it?"

Phase 0: Intake

1. Total monthly ad budget — Current or planned
2. Channels currently running — Google Ads, Meta Ads, LinkedIn Ads, Twitter/X Ads, TikTok Ads, other
3. Performance data per channel — For each active channel:
   • Monthly spend
   • Impressions
   • Clicks / CTR
   • Conversions (and conversion type: demo, trial, purchase)
   • CPA or CAC
   • Revenue attributed (if available)
   • ROAS (if available)
4. Primary conversion goal — Demos / Trials / Purchases / MQLs
5. Funnel data (if available):
   • Lead → MQL rate
   • MQL → SQL rate
   • SQL → Close rate
   • Average deal size
6. Channels you're considering but haven't tried — Want to test new channels?
7. Constraints — Minimum spend on any channel? Platform you must stay on?

Phase 1: Channel Normalization

Apples-to-Apples Comparison

Normalize all channels to the same metrics:

Channel	Monthly Spend	Impressions	Clicks	CTR	CPC	Conversions	Conv Rate	CPA	ROAS	CAC*
Google Search	$[X]	[N]	[N]	[X%]	$[X]	[N]	[X%]	$[X]	[X]	$[X]
Google Display	...
Meta (FB/IG)	...
LinkedIn	...
[Other]	...
Total	$[X]					[N]		$[X] avg	[X] avg	$[X] avg

*CAC = Full customer acquisition cost if funnel data provided (CPA × close-rate adjustment)

Funnel-Adjusted CAC (If Funnel Data Available)

Channel CAC = CPA ÷ (MQL rate × SQL rate × Close rate)

This reveals which channels produce leads that actually close, not just convert.

Phase 2: Channel Efficiency Analysis

2A: Efficiency Ranking

Rank	Channel	CPA	Funnel-Adj CAC	Share of Spend	Share of Conversions	Efficiency Index
1	[Channel]	$[X]	$[X]	[X%]	[X%]	[Conv share ÷ Spend share]

Efficiency Index:

• > 1.0 = Under-invested (getting more than its share of conversions)
• = 1.0 = Proportional (fair share)
• < 1.0 = Over-invested (getting less than its share)

2B: Marginal Return Analysis

For each channel, estimate if additional spend would yield proportional returns:

Channel	Current CPA	Impression Share / Saturation Signal	Marginal Return Estimate
Google Search	$[X]	[X%] impression share — room to grow	Likely positive
Meta	$[X]	Frequency [X] — audience may be saturated	Diminishing
LinkedIn	$[X]	Low volume — limited targeting pool	Ceiling soon

2C: Funnel Stage Coverage

Funnel Stage	Channels Covering It	Current Spend	Gap?
Awareness (top)	[Meta Display, YouTube]	$[X]	[Yes/No]
Consideration (mid)	[Google Search, Meta retargeting]	$[X]	[Yes/No]
Decision (bottom)	[Google Brand, Google Search]	$[X]	[Yes/No]
Retargeting	[Meta, Google Display]	$[X]	[Yes/No]

Phase 3: Reallocation Recommendations

3A: Budget Shift Table

Channel	Current Spend	Recommended Spend	Change	Reasoning
Google Search	$[X]	$[Y]	+$[Z]	[Lowest CPA, room to scale]
Meta	$[X]	$[Y]	-$[Z]	[Audience saturation, frequency too high]
LinkedIn	$[X]	$[Y]	$0	[Maintain — niche but valuable]
[New channel]	$0	$[Y]	+$[Y]	[Test budget — competitors succeeding here]
Total	$[X]	$[X]	$0	Budget-neutral reallocation

3B: Scenario Modeling

Scenario 1: Conservative shift (+/- 20%)

• Expected conversions: [N] (currently [N]) = [X%] improvement
• Expected blended CPA: $[X] (currently $[X])
• Risk: Low

Scenario 2: Aggressive shift (+/- 40%)

• Expected conversions: [N] = [X%] improvement
• Expected blended CPA: $[X]
• Risk: Medium — less data on scaled channels

Scenario 3: Budget increase to $[Y]/mo

• Recommended allocation: [table]
• Expected conversions: [N]
• New channels to test: [list]

Phase 4: Output Format

# Ad Spend Allocation — [Product/Client] — [DATE]

Total monthly budget: $[X]
Active channels: [list]
Period analyzed: [date range]

---

## Current State

| Channel | Spend | % of Budget | Conversions | CPA | Efficiency |
|---------|-------|------------|-------------|-----|-----------|
| [Channel] | $[X] | [X%] | [N] | $[X] | [Over/Under/Fair] |

**Blended CPA:** $[X]
**Total conversions:** [N]

---

## Recommended Reallocation

| Channel | Current | Recommended | Change | Why |
|---------|---------|------------|--------|-----|
| [Channel] | $[X] | $[Y] | [+/-$Z] | [1-line reason] |

**Projected impact:**
- Conversions: [N] → [N] (+[X%])
- Blended CPA: $[X] → $[Y] (-[X%])

---

## Funnel Stage Coverage

[Coverage map with gaps identified]

---

## New Channel Recommendations

### [Channel Name]
- **Why test:** [Reasoning]
- **Recommended test budget:** $[X]/mo for [X weeks]
- **Success criteria:** CPA < $[X]
- **Competitors using it:** [Yes/No — who]

---

## Implementation Plan

### Week 1: Quick Shifts
- [ ] Reduce [Channel] from $[X] to $[Y]
- [ ] Increase [Channel] from $[X] to $[Y]
- [ ] Set up [New Channel] test campaign

### Week 2-4: Monitor
- [ ] Track CPA shifts on scaled channels
- [ ] Watch for diminishing returns signals
- [ ] Evaluate new channel performance

### Month 2: Re-evaluate
- [ ] Run this analysis again with new data
- [ ] Adjust allocations based on actual results

Save to clients/<client-name>/ads/spend-allocation-[YYYY-MM-DD].md.

Cost

Component	Cost
Data analysis	Free (LLM reasoning)
Statistical modeling	Free
Total	Free

Tools Required

• No external tools needed — pure reasoning skill
• User provides multi-channel performance data

Trigger Phrases

• "How should I allocate my ad budget?"
• "Should I spend more on Google or Meta?"
• "Reallocate my ad spend"
• "Where am I getting the best ROAS?"
• "Optimize my multi-channel ad budget"