ad-spend-allocator

--- name: ad-spend-allocator description: Multi-channel budget optimization using MER, marginal ROAS, and diminishing returns analysis. Use when pasting multi-channel spend and results data, requesting reallocation recommendations, analyzing budget shift priorities, or optimizing marketing efficiency across Google, Meta, TikTok, and other channels. Platform: Google and Meta. metadata: platform: Google and Meta --- # Ad Spend Allocator Optimize budget distribution across advertising channels using efficiency metrics and diminishing returns analysis. ## Process 1. **Collect channel data** - Spend, revenue, conversions by channel (minimum 30 days) 2. **Calculate efficiency metrics** - MER, aMER, channel ROAS, marginal ROAS 3. **Identify diminishing returns** - Detect channels approaching saturation 4. **Apply allocation framework** - 70-20-10 rule as baseline 5. **Recommend shifts** - 10-20% increments with monitoring periods ## Key Formulas ``` MER (Marketing Efficiency Ratio) = Total Revenue / Total Marketing Spend Target: 3.0-5.0x (varies by industry, margin structure) aMER (Acquisition MER) = New Customer Revenue / Total Ad Spend Purpose: Isolates new customer acquisition efficiency Channel ROAS = Channel Revenue / Channel Spend Use for: Channel comparison, baseline performance Marginal ROAS = (Revenue at Spend B - Revenue at Spend A) / (Spend B - Spend A) Purpose: Detect diminishing returns before blended ROAS shows issues ``` ## 70-20-10 Budget Allocation Rule | Tier | Allocation | Criteria | |------|------------|----------| | **Proven** | 70% | Consistent ROAS, predictable results, 3+ months track record | | **Scaling** | 20% | Emerging opportunities, positive early signals, testing scale | | **Testing** | 10% | New channels, creative experiments, unproven strategies | ## Diminishing Returns Indicators - Higher CPC with same targeting (auction saturation) - Frequency increasing on Meta (audience exhaustion) - Conversion rate declining while impressions increase - CPM inflation without response improvement - Marginal ROAS dropping below blended ROAS ## Reallocation Decision Framework 1. Calculate marginal ROAS by channel (compare week-over-week at different spend levels) 2. Identify channels approaching saturation (marginal ROAS < blended ROAS) 3. Shift 10-20% at a time to unsaturated channels 4. Monitor for 7-14 days before further changes 5. Document and track impact ## Output Format ``` ## Budget Allocation Analysis **Total Monthly Spend**: $[X] **Current MER**: [X]x **Target MER**: [X]x ### Channel Efficiency Summary | Channel | Spend | Revenue | ROAS | Marginal ROAS | Status | |---------|-------|---------|------|---------------|--------| | | | | | | [Scaling/Saturated/Testing] | ### Diminishing Returns Detection - [Channel]: [Evidence of saturation] ### Recommended Reallocation | From | To | Amount | Rationale | |------|-----|--------|-----------| ### 70-20-10 Current vs Recommended | Tier | Current | Recommended | Gap | |------|---------|-------------|-----| | Proven (70%) | | | | | Scaling (20%) | | | | | Testing (10%) | | | | ### Expected Impact - Projected MER improvement: [X]% - Confidence: [HIGH/MEDIUM/LOW] ### Monitoring Plan - Review metrics in [X] days - Key indicators to watch: [List] ``` ## Example **Input**: "Google Ads: $50K, $200K revenue. Meta: $30K, $90K revenue. TikTok: $10K, $25K revenue. Seeing Meta CPMs up 35% last month." **Output**: ``` ## Budget Allocation Analysis **Total Monthly Spend**: $90,000 **Current MER**: 3.5x ($315K / $90K) ### Channel Efficiency Summary | Channel | Spend | Revenue | ROAS | Status | |---------|-------|---------|------|--------| | Google | $50K (56%) | $200K | 4.0x | Scaling | | Meta | $30K (33%) | $90K | 3.0x | ⚠️ Saturated | | TikTok | $10K (11%) | $25K | 2.5x | Testing | ### Diminishing Returns Detection - **Meta**: 35% CPM increase signals auction saturation/creative fatigue ### Recommended Reallocation | From | To | Amount | Rationale | |------|-----|--------|-----------| | Meta | Google | $6K (20%) | Higher efficiency, room to scale | | Meta | TikTok | $3K (10%) | Test scaling opportunity | ### Expected Impact - Projected MER improvement: 8-12% - Confidence: MEDIUM (need marginal ROAS data for precision) ### Monitoring Plan - Review in 14 days - Watch: Google CPC trends, TikTok conv rate, Meta frequency ``` ## Guidelines - Never recommend >20% shifts at once (too disruptive) - If marginal ROAS data unavailable, note this and use blended metrics with lower confidence - Account for seasonality - compare year-over-year if possible - Flag if total spend seems misaligned with business size ## Data access (Ryze MCP) This skill works best with live account data. Connect the free Ryze MCP once and Claude reads your Google Ads, Meta Ads, GA4 and Search Console directly: - claude.ai / Claude Desktop: Settings → Connectors → Add custom connector → `https://connector.get-ryze.ai/mcp` - Claude Code: `claude mcp add ryze --transport http https://connector.get-ryze.ai/mcp` - Cursor: Settings → MCP → add the same URL Setup guide: https://www.get-ryze.ai/how-to-connect-claude-to-google-meta-ads-mcp

1. **Gather Data**: Export spend and results from Google Ads, Meta Ads, TikTok Ads, and LinkedIn Ads for your chosen time period (e.g., last 30/90 days). Include columns for spend, impressions, clicks, conversions, revenue, and cost per acquisition (CPA). 2. **Format Inputs**: Paste the data into a structured format (CSV/Excel) or share as a table. Ensure columns are labeled clearly (e.g., 'Channel', 'Spend', 'Conversions', 'Revenue'). 3. **Define Parameters**: Specify your KPI (e.g., ROAS, CPA, or revenue volume) and threshold for marginal ROAS (e.g., 1.5x). Mention any constraints (e.g., minimum spend per channel). 4. **Run Analysis**: Use the prompt template to generate recommendations. For advanced users, export the data to a spreadsheet and apply diminishing returns formulas (e.g., log-log regression) to validate AI suggestions. 5. **Implement Gradually**: Apply budget shifts in 10-20% increments over 2-4 weeks to monitor performance. Use A/B tests for high-risk channels (e.g., LinkedIn).