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:

*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

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:

2C: Funnel Stage Coverage

Phase 3: Reallocation Recommendations

3A: Budget Shift Table

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

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"