Geo Performance Optimizer
SKILL.md
HOW_TO_USE.md
sample_input.json
expected_output.json
skillsgoogle-adsSKILL.md
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
# 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
Use the Google Ads MCP `user_location_view` action with this GAQL query:
```
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 pull `geographic_view`:
```
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: 🟢/🟡/🔴
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
────────────────────────────────────────────────────
[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 implement targeting changes
---
## 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 × 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
ReadyGeo Performance Optimizer
MarkdownUTF-8Verified