Geo Performance Optimizer
SKILL.md
HOW_TO_USE.md
sample_input.json
expected_output.json
skillsgoogle-adsSKILL.md
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# Geo Performance Optimizer
Find where your ads are killing it and where they're dying. Optimize bids by location to get more conversions from the same budget.
## Core Philosophy
**Location is a hidden lever.** Most advertisers set geo targeting and forget it. The smart ones constantly adjust bids based on where conversions actually come from.
**The Geo Questions:**
1. Which locations are driving efficient conversions?
2. Which locations are bleeding money?
3. Where should I increase/decrease bids?
4. Are there locations I'm missing?
---
## MCP Integration
### Required Data Pull
Run this GAQL query with the Google Ads MCP `gaql` action (resource: `user_location_view`):
```
SELECT
campaign.id,
campaign.name,
user_location_view.country_criterion_id,
user_location_view.targeting_location,
metrics.impressions,
metrics.clicks,
metrics.cost_micros,
metrics.conversions,
metrics.conversions_value,
metrics.ctr,
metrics.average_cpc,
metrics.cost_per_conversion
FROM user_location_view
WHERE segments.date BETWEEN '{start_date}' AND '{end_date}'
AND metrics.impressions > 0
ORDER BY metrics.cost_micros DESC
```
### Supplementary Query (Geographic View)
For country-level aggregation, also query `geographic_view` with the `gaql` action:
```
SELECT
campaign.id,
campaign.name,
geographic_view.country_criterion_id,
geographic_view.location_type,
metrics.impressions,
metrics.clicks,
metrics.cost_micros,
metrics.conversions
FROM geographic_view
WHERE segments.date BETWEEN '{start_date}' AND '{end_date}'
```
### Data Requirements
**From MCP Response:**
- `user_location_view.targeting_location` - Whether location is targeted
- `user_location_view.country_criterion_id` - Location identifier
- `metrics.cost_micros`, `metrics.conversions`, `metrics.clicks`
- `metrics.cost_per_conversion` - CPA by location
- `metrics.average_cpc` - Avg CPC by location
**User Should Provide:**
- Target CPA or ROAS
- Date range (default: last 30 days)
- Service area constraints (if any)
- Current geo bid adjustments (if known)
---
## Analysis Framework
### Tier 1: Location Performance Ranking
**Performance Buckets:**
| Tier | CPA vs Target | Volume | Action |
|------|---------------|--------|--------|
| Star | <75% of target | High | Increase bid +15-25% |
| Solid | 75-100% of target | Any | Maintain or slight increase |
| Average | 100-125% of target | Any | Monitor, no change |
| Underperformer | 125-175% of target | Any | Decrease bid -15-25% |
| Bleeder | >175% of target | Any | Decrease bid -30-50% |
| Dead Zone | High spend, 0 conv | Any | Exclude or -50% bid |
---
### Tier 2: Volume-Weighted Analysis
**Not all locations are equal.** A location with 2 conversions at $40 CPA is less reliable than one with 50 conversions at $45 CPA.
**Statistical Confidence:**
| Conversions | Confidence | Action Threshold |
|-------------|------------|------------------|
| 1-2 | Low | Don't adjust, monitor |
| 3-5 | Medium | Adjust if trend clear |
| 6-10 | Good | Make moderate adjustments |
| 11+ | High | Full adjustment confidence |
**Spend Significance:**
- Minimum $50 spend to evaluate
- Minimum $200 spend for confident adjustment
- High volume = more reliable CPA signal
---
### Tier 3: Bid Adjustment Calculation
**Bid Modifier Formula:**
```
Target CPA: $50
Location CPA: $40
Efficiency Ratio = Target / Actual = 50 / 40 = 1.25 (25% more efficient)
Recommended Bid Modifier = +25%
Location CPA: $75
Efficiency Ratio = 50 / 75 = 0.67 (33% less efficient)
Recommended Bid Modifier = -33%
```
**Adjustment Caps:**
- Maximum increase: +50% (avoid overspending before validation)
- Maximum decrease: -50% (keep some presence for data)
- Zero conversion locations: -50% or exclude
---
### Tier 4: Expansion Opportunities
**Signals for Location Expansion:**
- Adjacent markets to top performers
- Similar demographic profiles
- Underserved areas with low competition
**Signals for Location Exclusion:**
- Consistent 0 conversions with $100+ spend
- CPA consistently 3x+ target
- Outside service area
---
## Output Format
### Executive Summary
```
GEO PERFORMANCE HEALTH: š¢/š”/š“
Locations Analyzed: XXX
Period: [dates]
Top Performing Region: [location] (CPA: $XX)
Worst Performing Region: [location] (CPA: $XX)
Estimated Monthly Savings from Optimization: $X,XXX
```
---
### Top Performers (Increase Bids)
| Rank | Location | Spend | Conv | CPA | vs Target | Rec. Bid Adj |
|------|----------|-------|------|-----|-----------|--------------|
| 1 | [City/State] | $X,XXX | XX | $XX | -XX% | +XX% |
| 2 | [City/State] | $X,XXX | XX | $XX | -XX% | +XX% |
| 3 | [City/State] | $X,XXX | XX | $XX | -XX% | +XX% |
**Why These Locations Win:**
- [Observation about top performers]
- [Pattern or demographic insight]
---
### Underperformers (Decrease Bids)
| Rank | Location | Spend | Conv | CPA | vs Target | Rec. Bid Adj |
|------|----------|-------|------|-----|-----------|--------------|
| 1 | [City/State] | $X,XXX | X | $XXX | +XX% | -XX% |
| 2 | [City/State] | $X,XXX | X | $XXX | +XX% | -XX% |
**Why These Locations Struggle:**
- [Observation about underperformers]
- [Possible reasons: competition, intent mismatch, etc.]
---
### Dead Zones (Exclude or Minimize)
| Location | Spend | Conv | Clicks | Recommendation |
|----------|-------|------|--------|----------------|
| [City/State] | $XXX | 0 | XX | Exclude |
| [City/State] | $XXX | 0 | XX | -50% bid |
---
### Bid Adjustment Implementation
**Campaign: [Campaign Name]**
| Location | Current Adj | Recommended | Change |
|----------|-------------|-------------|--------|
| [Location] | +0% | +25% | +25% |
| [Location] | +0% | -30% | -30% |
| [Location] | +10% | +20% | +10% |
**Copy-Paste for Bulk Edit:**
```
Location, Bid Adjustment
[Location 1], +25%
[Location 2], -30%
[Location 3], +20%
```
---
### Geographic Distribution
```
REGION SPEND CONV CPA % of TOTAL
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
[Region 1] $X,XXX XX $XX XX%
[Region 2] $X,XXX XX $XX XX%
[Region 3] $X,XXX XX $XX XX%
National Avg $X,XXX XX $XX 100%
```
---
### Expansion Recommendations
**Consider Adding:**
- [Location] - Adjacent to top performer [X], similar demographics
- [Location] - Underserved market, low competition signals
**Consider Excluding:**
- [Location] - 90-day 0 conversions, $XXX wasted
- [Location] - Consistently 3x+ target CPA
---
### Impact Projection
**If All Recommendations Implemented:**
| Metric | Current | Projected | Change |
|--------|---------|-----------|--------|
| Total Spend | $XX,XXX | $XX,XXX | - |
| Conversions | XXX | XXX | +XX% |
| Blended CPA | $XX | $XX | -XX% |
**Confidence Level:** High / Medium / Low
**Assumptions:**
- Bid increases don't exhaust efficient inventory
- CPA patterns hold with bid changes
- No major market shifts
---
## Composability
**Chains from:**
- `budget-allocation-optimizer` - Geo insights inform budget decisions
**Chains to:**
- `scaling-roadmap-builder` - Top geos feed expansion strategy
- MCP `add_location_targeting` - Can add target locations (a write action: confirm with the user first; it needs a Read & Write connection with the action enabled)
---
## Limitations
**I can assess:**
- Location-level CPA and efficiency
- Bid adjustment recommendations
- Spend distribution by geography
- Expansion/exclusion candidates
**I cannot assess:**
- User location vs targeted location nuances
- Local competitive landscape
- Offline conversion attribution by location
- Seasonality patterns by region
**For deeper geo analysis:**
- Review auction insights by location (Google Ads UI)
- Check device Ć location cross-segments
- Validate with backend conversion quality data
---
## Quality Checklist
Before delivering analysis:
- [ ] Locations ranked by CPA efficiency
- [ ] Volume/confidence noted for each
- [ ] Bid adjustments calculated with formula
- [ ] Dead zones identified with spend impact
- [ ] Copy-paste format provided for implementation
- [ ] Expansion opportunities noted
- [ ] Impact projection included
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# How to Use This Skill
Hey Claude, I just added the "geo-performance-optimizer" skill. Can you analyze which locations are performing best and recommend bid adjustments?
## Example Invocations
**Example 1: Full Geo Analysis**
Hey Claude, I just added the "geo-performance-optimizer" skill. Pull location data for my Google Ads account and show me which cities/states are most efficient. My target CPA is $50.
**Example 2: Find Wasted Geo Spend**
Hey Claude, I just added the "geo-performance-optimizer" skill. Which locations are wasting my budget with no conversions?
**Example 3: Bid Adjustment Recommendations**
Hey Claude, I just added the "geo-performance-optimizer" skill. Give me specific geo bid adjustments I should make for each location.
**Example 4: Expansion Analysis**
Hey Claude, I just added the "geo-performance-optimizer" skill. Based on my top performing locations, where else should I consider targeting?
## What to Provide
- Account to analyze (or I'll list available accounts)
- Target CPA or ROAS
- Date range (default: last 30 days)
- Service area constraints (which states/regions you can serve)
- Current geo bid adjustments if known
## What You'll Get
- Locations ranked by efficiency
- Top performers with scale opportunity
- Underperformers and dead zones
- Specific bid adjustment percentages
- Copy-paste format for implementation
- Expansion/exclusion recommendations
- Impact projection
## MCP Data Used
This skill pulls live data from Google Ads via:
- `gaql` action querying `user_location_view` - User's actual location performance
- `gaql` action querying `geographic_view` (optional) - Geographic targeting view
- Metrics: impressions, clicks, cost, conversions, CPA by location
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{
"workspace_id": 101,
"brand_id": 69,
"account_id": "9382431689",
"date_range": {
"start_date": "2025-01-01",
"end_date": "2025-01-31"
},
"target_cpa": 50,
"service_area": [
"Georgia",
"Florida",
"Alabama",
"South Carolina"
],
"mcp_response": {
"status": "success",
"data": [
{
"campaign": {
"id": "123",
"name": "Brand Campaign"
},
"user_location_view": {
"country_criterion_id": "2840",
"targeting_location": true
},
"location_name": "Atlanta, Georgia",
"metrics": {
"impressions": 15000,
"clicks": 450,
"cost_micros": 9000000000,
"conversions": 25,
"conversions_value": 5000,
"ctr": 0.03,
"average_cpc": 2000000,
"cost_per_conversion": 36000000
}
},
{
"campaign": {
"id": "123",
"name": "Brand Campaign"
},
"user_location_view": {
"country_criterion_id": "2840",
"targeting_location": true
},
"location_name": "Miami, Florida",
"metrics": {
"impressions": 12000,
"clicks": 360,
"cost_micros": 7200000000,
"conversions": 8,
"conversions_value": 1600,
"ctr": 0.03,
"average_cpc": 2000000,
"cost_per_conversion": 90000000
}
},
{
"campaign": {
"id": "123",
"name": "Brand Campaign"
},
"user_location_view": {
"country_criterion_id": "2840",
"targeting_location": true
},
"location_name": "Birmingham, Alabama",
"metrics": {
"impressions": 5000,
"clicks": 150,
"cost_micros": 3000000000,
"conversions": 0,
"conversions_value": 0,
"ctr": 0.03,
"average_cpc": 2000000,
"cost_per_conversion": 0
}
},
{
"campaign": {
"id": "123",
"name": "Brand Campaign"
},
"user_location_view": {
"country_criterion_id": "2840",
"targeting_location": true
},
"location_name": "Savannah, Georgia",
"metrics": {
"impressions": 3000,
"clicks": 90,
"cost_micros": 1800000000,
"conversions": 5,
"conversions_value": 1000,
"ctr": 0.03,
"average_cpc": 2000000,
"cost_per_conversion": 36000000
}
}
]
}
}
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{
"executive_summary": {
"health_score": "yellow",
"locations_analyzed": 4,
"period": "2025-01-01 to 2025-01-31",
"top_performer": {
"location": "Atlanta, Georgia",
"cpa": 36
},
"worst_performer": {
"location": "Miami, Florida",
"cpa": 90
},
"estimated_monthly_savings": 516
},
"top_performers": [
{
"rank": 1,
"location": "Atlanta, Georgia",
"spend": 900,
"conversions": 25,
"cpa": 36,
"vs_target": "-28%",
"recommended_bid_adjustment": "+25%",
"confidence": "high"
},
{
"rank": 2,
"location": "Savannah, Georgia",
"spend": 180,
"conversions": 5,
"cpa": 36,
"vs_target": "-28%",
"recommended_bid_adjustment": "+20%",
"confidence": "medium"
}
],
"underperformers": [
{
"rank": 1,
"location": "Miami, Florida",
"spend": 720,
"conversions": 8,
"cpa": 90,
"vs_target": "+80%",
"recommended_bid_adjustment": "-35%",
"confidence": "good"
}
],
"dead_zones": [
{
"location": "Birmingham, Alabama",
"spend": 300,
"conversions": 0,
"clicks": 150,
"recommendation": "Decrease bid -50% or exclude"
}
],
"bid_adjustments": {
"campaign": "Brand Campaign",
"adjustments": [
{
"location": "Atlanta, Georgia",
"current": "+0%",
"recommended": "+25%",
"change": "+25%"
},
{
"location": "Savannah, Georgia",
"current": "+0%",
"recommended": "+20%",
"change": "+20%"
},
{
"location": "Miami, Florida",
"current": "+0%",
"recommended": "-35%",
"change": "-35%"
},
{
"location": "Birmingham, Alabama",
"current": "+0%",
"recommended": "-50%",
"change": "-50%"
}
]
},
"geographic_distribution": [
{
"region": "Atlanta, Georgia",
"spend": 900,
"conversions": 25,
"cpa": 36,
"percent_of_total": "43%"
},
{
"region": "Miami, Florida",
"spend": 720,
"conversions": 8,
"cpa": 90,
"percent_of_total": "34%"
},
{
"region": "Birmingham, Alabama",
"spend": 300,
"conversions": 0,
"cpa": null,
"percent_of_total": "14%"
},
{
"region": "Savannah, Georgia",
"spend": 180,
"conversions": 5,
"cpa": 36,
"percent_of_total": "9%"
}
],
"expansion_recommendations": {
"consider_adding": [
"Augusta, Georgia - Adjacent to Atlanta success, similar market",
"Charleston, South Carolina - In service area, underserved"
],
"consider_excluding": [
"Birmingham, Alabama - 0 conversions with $300 spend"
]
},
"impact_projection": {
"current": {
"total_spend": 2100,
"conversions": 38,
"blended_cpa": 55.26
},
"projected": {
"total_spend": 2100,
"conversions": 43,
"blended_cpa": 48.84
},
"change": {
"conversions": "+13%",
"cpa": "-12%"
},
"confidence": "medium"
}
}
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