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Social Ads Budget Allocator

Analyzes efficiency across social ad platforms (Meta, TikTok, LinkedIn, etc.) and recommends optimal budget reallocation.

Social Media AdsIncludes Sample Data
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Social Ads Budget Allocator
SKILL.md
sample_input.json
expected_output.json
skillssocial-media-adsSKILL.md
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# Social Ads Budget Allocator
 
## Purpose
 
Determine the optimal distribution of advertising budget across:
- Multiple platforms (Meta, TikTok, LinkedIn, Pinterest, Twitter/X, Snapchat)
- Campaigns within each platform
- Objectives and funnel stages
 
**Core Philosophy:** Not all dollars are equal. A dollar spent on an efficient campaign drives 2-3x the results of a dollar spent on an inefficient one. Move money from expensive conversions to cheap ones.
 
---
 
## When to Use This Skill
 
- Monthly/quarterly budget planning
- When adding a new platform to the mix
- When overall performance is declining
- Before scaling total spend
- When one platform significantly outperforms others
- After major creative or audience changes
 
---
 
## Required Context
 
| Data Point | Required | Notes |
|------------|----------|-------|
| Spend by platform (last 30 days minimum) | Yes | Need volume for comparison |
| Conversions/results by platform | Yes | Primary success metric |
| CPA or ROAS by platform | Yes | Efficiency comparison |
| Conversion value (if ROAS model) | If applicable | Revenue-based optimization |
| Total available budget | Yes | Constraint for allocation |
| Business constraints | Helpful | Minimum presence requirements |
| Historical performance (60-90 days) | Helpful | Trend analysis |
| Audience overlap data | Helpful | Cross-platform redundancy |
 
---
 
## The Efficiency Hierarchy
 
### Platform-Level Analysis
 
Rank platforms by efficiency (CPA or ROAS):
 
| Platform | Spend | Conversions | CPA | ROAS | Efficiency Rank |
|----------|-------|-------------|-----|------|-----------------|
| | | | | | |
 
### Efficiency Tiers
 
| Tier | Name | Criteria (CPA Model) | Criteria (ROAS Model) |
|------|------|---------------------|----------------------|
| 1 | **Gold** | CPA < 70% of blended avg | ROAS > 143% of target |
| 2 | **Silver** | CPA 70-100% of blended avg | ROAS 100-143% of target |
| 3 | **Bronze** | CPA 100-130% of blended avg | ROAS 77-100% of target |
| 4 | **Underperformer** | CPA 130-200% of blended avg | ROAS 50-77% of target |
| 5 | **Bleeder** | CPA > 200% of blended avg | ROAS < 50% of target |
 
---
 
## Marginal Efficiency Analysis
 
The key question isn't "what's the average CPA?" but "what will happen if I spend $1 more?"
 
### Marginal CPA/ROAS Estimation
 
For each platform, estimate the marginal cost of the next conversion:
 
```
Marginal CPA = Current CPA ร— Scaling Factor
 
Scaling Factor by Current Spend vs. Saturation:
- < 30% of estimated saturation: 1.0x (linear scaling)
- 30-60% of saturation: 1.1-1.3x (mild diminishing returns)
- 60-80% of saturation: 1.3-1.6x (significant diminishing returns)
- > 80% of saturation: 1.6-2.5x (severe diminishing returns)
```
 
### Saturation Signals
 
| Signal | Indication |
|--------|-----------|
| CPM increasing week over week | Audience exhaustion |
| Frequency > 3.0 | Oversaturating audience |
| CTR declining, CPC rising | Fewer interested users left |
| Same spend, fewer conversions | Diminishing returns active |
 
---
 
## The Reallocation Framework
 
### Step 1: Calculate Current State
 
```
CURRENT ALLOCATION
Total Budget: $________
 
| Platform | $ Spend | % of Total | Conversions | CPA | Efficiency Tier |
|----------|---------|------------|-------------|-----|-----------------|
| Meta | | | | | |
| TikTok | | | | | |
| LinkedIn | | | | | |
| [Other] | | | | | |
| TOTAL | | 100% | | Blended: | |
```
 
### Step 2: Identify Reallocation Opportunities
 
**Move FROM:**
- Tier 4-5 platforms (Underperformers & Bleeders)
- Campaigns with clear saturation signals
- Platforms with declining efficiency trends
 
**Move TO:**
- Tier 1-2 platforms (Gold & Silver) that aren't saturated
- Emerging opportunities with strong early signals
- Campaigns with headroom for growth
 
### Step 3: Calculate Optimal Allocation
 
Use this prioritization formula:
 
```
Allocation Score = (Efficiency Score ร— Volume Potential) / Risk Factor
 
Where:
- Efficiency Score = Blended CPA / Platform CPA (higher = more efficient)
- Volume Potential = 1 - (Current Spend / Estimated Saturation Point)
- Risk Factor = 1.0 (stable) to 2.0 (volatile/untested)
```
 
---
 
## Platform-Specific Considerations
 
### Meta (Facebook/Instagram)
 
| Factor | Consideration |
|--------|---------------|
| **Strengths** | Largest scale, best targeting, mature optimization |
| **Saturation Point** | Typically 2-5% of total addressable audience/month |
| **Minimum Viable Budget** | $20-50/day per ad set for learning |
| **Scaling Limit** | 20-30% budget increase per week max |
| **Watch For** | iOS attribution gaps, frequency creep |
 
### TikTok
 
| Factor | Consideration |
|--------|---------------|
| **Strengths** | Lower CPMs, younger audience, viral potential |
| **Saturation Point** | Faster saturation (3-4 weeks per creative) |
| **Minimum Viable Budget** | $50-100/day per ad group for learning |
| **Scaling Limit** | Can scale faster (30-50%/week) but higher volatility |
| **Watch For** | Creative-dependent, quality score fluctuations |
 
### LinkedIn
 
| Factor | Consideration |
|--------|---------------|
| **Strengths** | B2B targeting, professional intent |
| **Saturation Point** | Lower (smaller professional audiences) |
| **Minimum Viable Budget** | $100/day minimum for meaningful data |
| **Scaling Limit** | Limited by audience size (10-20%/week) |
| **Watch For** | High CPCs, long sales cycles |
 
### Pinterest
 
| Factor | Consideration |
|--------|---------------|
| **Strengths** | High-intent shopping, long content lifespan |
| **Saturation Point** | Slow saturation (evergreen content) |
| **Minimum Viable Budget** | $20-30/day per campaign |
| **Scaling Limit** | Gradual scaling works best |
| **Watch For** | Seasonal variations, attribution window |
 
### Twitter/X
 
| Factor | Consideration |
|--------|---------------|
| **Strengths** | Real-time relevance, conversation targeting |
| **Saturation Point** | Variable by topic/audience |
| **Minimum Viable Budget** | $30-50/day for testing |
| **Scaling Limit** | Event/moment dependent |
| **Watch For** | Brand safety, volatile performance |
 
### Snapchat
 
| Factor | Consideration |
|--------|---------------|
| **Strengths** | Young demographic, AR features |
| **Saturation Point** | Fast creative fatigue |
| **Minimum Viable Budget** | $50/day per ad set |
| **Scaling Limit** | Creative-limited more than budget-limited |
| **Watch For** | Measurement challenges, age skew |
 
---
 
## Reallocation Scenarios
 
### Scenario A: Optimize Existing Mix
Goal: Improve efficiency without adding platforms
 
```
Action: Shift budget from Tier 4-5 to Tier 1-2
Rule: Move up to 30% of underperformer budget in one cycle
Guardrail: Maintain minimum presence if required
```
 
### Scenario B: Scale Total Budget
Goal: Increase spend while maintaining efficiency
 
```
Priority Order:
1. Add to Tier 1 platforms up to saturation signals
2. Add to Tier 2 platforms with headroom
3. Test new platforms with 10-15% of increment
4. Never add to Tier 4-5 without fixing issues first
```
 
### Scenario C: Add New Platform
Goal: Test and integrate new channel
 
```
Test Budget: 10-15% of total, or $3K-$5K minimum
Test Duration: 4-6 weeks minimum
Success Criteria: Beat Tier 3 threshold to continue
Kill Criteria: Worse than Tier 5 for 2+ weeks
```
 
### Scenario D: Cut Total Budget
Goal: Reduce spend while preserving results
 
```
Cut Priority (first to last):
1. Tier 5 (Bleeders) - cut immediately
2. Tier 4 (Underperformers) - reduce by 50%
3. Tier 3 (Bronze) - hold or reduce 20%
4. Tier 1-2 (Gold/Silver) - protect, last to cut
```
 
---
 
## Allocation Constraints
 
### Hard Constraints
- Platform minimums (e.g., LinkedIn needs $100/day for learning)
- Contractual commitments
- Reach/awareness requirements that override efficiency
 
### Soft Constraints
- Diversification (no platform > 70% of spend)
- Testing budget (5-15% for experiments)
- Seasonal adjustments
 
### Constraint Override Rules
```
IF hard constraint conflicts with efficiency optimization:
โ†’ Honor constraint, note efficiency sacrifice
โ†’ Calculate cost of constraint in lost conversions
โ†’ Flag for stakeholder visibility
```
 
---
 
## Projection Modeling
 
Calculate the impact of proposed reallocation:
 
### Conversion Impact Model
 
```
For each platform:
Current Conversions = Spend / CPA
Projected Conversions = New Spend / Projected CPA
 
Where Projected CPA accounts for:
- Scaling factor (diminishing returns)
- Trend direction (improving/declining)
- Seasonal adjustments
 
Net Impact = ฮฃ(Projected Conversions) - ฮฃ(Current Conversions)
```
 
### Scenario Comparison Template
 
| Scenario | Total Spend | Est. Conversions | Blended CPA | vs. Current |
|----------|-------------|------------------|-------------|-------------|
| Current State | | | | - |
| Optimized Mix | | | | +X% |
| Aggressive Shift | | | | +Y% |
| Conservative Shift | | | | +Z% |
 
---
 
## Output Deliverables
 
### 1. Current State Summary
 
```
CROSS-PLATFORM PERFORMANCE SUMMARY
Period: [Date Range]
Total Spend: $[X]
Total Conversions: [X]
Blended CPA: $[X]
 
PLATFORM BREAKDOWN:
| Platform | Spend | % | Conv | CPA | Tier | Trend | Verdict |
|----------|-------|---|------|-----|------|-------|---------|
| | | | | | | | |
 
EFFICIENCY ANALYSIS:
โ”œโ”€โ”€ Gold Tier Spend: $[X] ([X]% of total) - [X] conversions
โ”œโ”€โ”€ Silver Tier Spend: $[X] ([X]% of total) - [X] conversions
โ”œโ”€โ”€ Bronze Tier Spend: $[X] ([X]% of total) - [X] conversions
โ”œโ”€โ”€ Underperformer Spend: $[X] ([X]% of total) - [X] conversions
โ””โ”€โ”€ Bleeder Spend: $[X] ([X]% of total) - [X] conversions
 
WASTE IDENTIFIED: $[X] ([X]% of total spend)
```
 
### 2. Reallocation Recommendation
 
```
RECOMMENDED REALLOCATION
 
| Platform | Current $ | Current % | Recommended $ | Recommended % | Change |
|----------|-----------|-----------|---------------|---------------|--------|
| | | | | | |
| TOTAL | | 100% | | 100% | - |
 
RATIONALE:
- [Platform A]: [Increase/Decrease] because [reason]
- [Platform B]: [Increase/Decrease] because [reason]
```
 
### 3. Impact Projection
 
```
PROJECTED IMPACT
 
| Current | Optimized | Difference |
--------------------|---------|-----------|------------|
Total Spend | $X | $X | - |
Est. Conversions | X | X | +X (+Y%) |
Blended CPA | $X | $X | -$X (-Y%) |
Est. Revenue | $X | $X | +$X |
Est. ROAS | X.Xx | X.Xx | +X.Xx |
```
 
### 4. Implementation Plan
 
```
IMPLEMENTATION ROADMAP
 
WEEK 1:
โ”œโ”€โ”€ [Action 1]
โ”œโ”€โ”€ [Action 2]
โ””โ”€โ”€ [Action 3]
 
WEEK 2:
โ”œโ”€โ”€ [Action 4]
โ””โ”€โ”€ [Action 5]
 
MONITORING CHECKPOINTS:
โ”œโ”€โ”€ Day 7: Check [metric] - abort if [condition]
โ”œโ”€โ”€ Day 14: Check [metric] - adjust if [condition]
โ””โ”€โ”€ Day 30: Full review and next iteration
```
 
### 5. Risk Assessment
 
| Risk | Likelihood | Impact | Mitigation |
|------|------------|--------|------------|
| | | | |
 
---
 
## Decision Guardrails
 
### Before Major Reallocation, Verify:
 
- [ ] At least 30 days of data for each platform
- [ ] Seasonal factors accounted for
- [ ] No major changes pending (creative, audience, offer)
- [ ] Tracking/attribution consistent across platforms
- [ ] Saturation signals checked (not just averages)
- [ ] Marginal CPA estimated (not just historical)
- [ ] Stakeholder constraints documented
 
### Red Flags to Pause Reallocation:
 
- Platform has < 30 conversions (insufficient data)
- Attribution models differ significantly between platforms
- Major creative or offer change in last 14 days
- Seasonal peak/trough in next 30 days
- One platform > 90% of spend (rebalancing should be gradual)
 
---
 
## Integration with Other Skills
 
| Connected Skill | Use Case |
|-----------------|----------|
| meta-ads-creative-ranker | Understand WHY platform is underperforming |
| creative-fatigue-detector | Check if poor performance is creative or budget issue |
| audience-overlap-mapper | Identify cross-platform audience redundancy |
 
---
 
## Limitations
 
- Cannot account for brand awareness effects across platforms
- Attribution models vary; true incrementality unknown
- Assumes current targeting is optimized (budget โ‰  only lever)
- Seasonal patterns may not repeat
- New platform testing carries higher uncertainty
- Cannot predict competitive landscape changes
 
---
 
## Quality Checklist Before Delivery
 
- [ ] All platforms have minimum 30 days of data
- [ ] Efficiency tiers assigned correctly
- [ ] Marginal CPA/scaling factors considered
- [ ] Platform-specific considerations addressed
- [ ] Constraints documented and honored
- [ ] At least 2 scenarios modeled
- [ ] Implementation plan is gradual (not overnight shift)
- [ ] Monitoring checkpoints defined
- [ ] Risks identified with mitigations
Ready
UTF-8Verified

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Frequently Asked Questions

Common questions about Social Ads Budget Allocator and how to use it effectively with Claude.

Social Ads Budget Allocator is a pre-built AI skill for Claude that helps with social media ads tasks. Analyzes efficiency across social ad platforms (Meta, TikTok, LinkedIn, etc.) and recommends optimal budget reallocation. This skill is designed to work seamlessly with Claude Code and other Claude-powered applications, enabling marketers and businesses to automate and enhance their social media ads workflows.

To use Social Ads Budget Allocator, download the SKILL.md file and add it to your Claude project or paste the contents directly into Claude. The skill contains structured prompts and instructions that Claude can follow to help you with social media ads tasks. No additional setup is required.

Yes, Social Ads Budget Allocator comes with sample input data that demonstrates how to structure your requests for optimal results. The sample_input.json file shows the expected data format, and expected_output.json provides an example of what Claude will generate. This helps you understand exactly how to use the skill and what to expect from the output.

Unlike traditional social media ads tools, Social Ads Budget Allocator leverages Claude's advanced AI capabilities to provide intelligent, context-aware assistance. It combines pre-built expertise with Claude's reasoning abilities, allowing for more nuanced and customized outputs. The skill is also free to use, continuously updated, and integrates directly with your existing Claude workflow.

Absolutely. The skill files are fully editable, allowing you to modify the prompts, add your own brand guidelines, or adjust the output format to match your requirements. You can also combine this skill with other Claude skills to create powerful automated workflows tailored to your business needs.

Yes, Social Ads Budget Allocator is completely free to download and use. All InsightfulPipe skills are open source and designed to help marketers and businesses leverage AI more effectively. You can download the skill files, use them in your projects, and even modify them to suit your specific requirements.