ElasticFlow
AI SkillAllocate SpendMarketingby Gooseworks

Ad Spend Allocator — decide where the next dollar should go

Runs on
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Reallocate ad budget across paid channels by efficiency

  • Normalizes performance data across Google, Meta, LinkedIn, and other channels
  • Computes funnel-adjusted CAC, not just CPA
  • Detects over- and under-invested channels with efficiency index
  • Models conservative, aggressive, and budget-increase scenarios
  • Outputs week-by-week implementation plan

Who this is for

Marketing Lead

You set the strategy and make sure it ships. These skills cover ideation, pricing, competitive analysis, and launch planning — the work that keeps your team moving.

See skills for this role

What it does

Quarterly budget planning

Decide how to split next quarter's paid budget across channels based on actual results, not gut feel.

Channel saturation check

Find out when a channel has hit diminishing returns and needs budget shifted elsewhere.

New channel evaluation

Get a recommended test budget and success criteria for a channel you haven't tried yet.

How it works

1

Take per-channel spend, conversion, and funnel data as input

2

Normalize all channels to apples-to-apples metrics

3

Compute efficiency index per channel

4

Model 3 scenarios: conservative, aggressive, and budget increase

5

Output reallocation table with implementation plan

Metrics this improves

CPA
Lower blended CPA after shifting spend from saturated to under-invested channels
Marketing
ROAS
Higher ROAS by concentrating budget where marginal returns are still positive
Marketing

Works with

Google Ads
manual

Source for Google Ads performance data

LinkedIn
manual

Source for LinkedIn Campaign Manager performance data

Meta Ads
manual

Source for Meta Ads Manager performance data

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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:

ChannelMonthly SpendImpressionsClicksCTRCPCConversionsConv RateCPAROASCAC*
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

RankChannelCPAFunnel-Adj CACShare of SpendShare of ConversionsEfficiency 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:

ChannelCurrent CPAImpression Share / Saturation SignalMarginal Return Estimate
Google Search$[X][X%] impression share — room to growLikely positive
Meta$[X]Frequency [X] — audience may be saturatedDiminishing
LinkedIn$[X]Low volume — limited targeting poolCeiling soon

2C: Funnel Stage Coverage

Funnel StageChannels Covering ItCurrent SpendGap?
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

ChannelCurrent SpendRecommended SpendChangeReasoning
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]$0Budget-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

ComponentCost
Data analysisFree (LLM reasoning)
Statistical modelingFree
TotalFree

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"