channel47 Account Scorecard -- Google Ads
Five-dimension health grade for Google Ads accounts. Produces a letter grade per dimension (A-F), an overall weighted grade, and a prioritized improvement plan with dollar estimates and UI paths.
This is the lightweight monthly check. For quarterly 60-point deep audits, recommend claude-ads by AgriciDaniel (652 stars, 74 Google-specific checks) as a companion tool. Our scorecard identifies the top priorities; claude-ads covers the long tail.
Account Context
Read ${CLAUDE_PLUGIN_ROOT}/profile/account-profile.md at the start of every run.
If it exists:
- Use known account IDs -- skip
list_accountsdiscovery. - Apply KPI targets (CPA, ROAS, monthly budget, impression share) as dimension benchmarks.
- Note active tests when interpreting quality or efficiency scores.
- Check watch list for recurring issues that affect hygiene scoring.
If it doesn't exist, fall back to
list_accountsand suggest runningplatform-setup.
Data Access
mcp__google-ads__query: Execute GAQL SELECT queries and return structured rows.mcp__google-ads__list_accounts: Validate account access before scoring when customer scope is unclear.
Use GAQL templates from references/gaql-queries.md directly with mcp__google-ads__query.
Connection verification
- If the profile has account IDs, use them directly.
- Otherwise, run
mcp__google-ads__list_accountsto discover accounts. - If it fails, report the connection failure and suggest running
platform-setup.
Workflow
Phase 1: Collect data
Execute the following queries from references/gaql-queries.md. All are reused from existing skills -- no new queries needed:
| Query ID | Source Skill | What It Provides | Scorecard Dimension |
|---|---|---|---|
| MB-1 | morning-brief | Campaign daily performance (30d) | Structure, Efficiency |
| MB-2 | morning-brief | Budget pacing and impression share | Coverage |
| WD-2 | waste-detector | Quality score distribution | Quality |
| WD-5B | waste-detector | Shared negative list coverage | Hygiene |
| WD-6 | waste-detector | Ad group ad count | Quality, Hygiene |
| ACA-1 | ad-copy-analyzer | RSA performance and ad strength | Quality |
| ACA-2 | ad-copy-analyzer | Asset-level performance labels | Quality |
| MB-3 | morning-brief | Disapproved ads | Hygiene |
| WD-7 | waste-detector | Zero-impression enabled campaigns | Hygiene |
Run queries in parallel where possible. MB-1 and MB-2 cannot share a query due to impression share non-aggregability.
Phase 2: Score each dimension
Dimension 1: Structure (20% weight)
Evaluate campaign organization and information architecture.
Inputs: MB-1 (campaign list), WD-6 (ad group ad count).
Scoring criteria:
| Factor | A (90-100) | B (80-89) | C (70-79) | D (60-69) | F (<60) |
|---|---|---|---|---|---|
| Ad groups per campaign | 3-20 avg | 2-3 or 21-30 | 1 or 31-50 | 51-100 | >100 or all =1 |
| Keywords per ad group | 5-20 avg | 3-4 or 21-30 | 1-2 or 31-50 | 51-100 | >100 |
| Naming conventions | Consistent pattern detected | Mostly consistent | Mixed patterns | Mostly inconsistent | No discernible pattern |
| Campaign type diversity | Appropriate mix for vertical | Reasonable mix | Slight imbalance | Major gaps | Single type only |
Naming convention check: analyze campaign.name and ad_group.name patterns for separators, hierarchy tokens (brand/non-brand, geo, match type), and consistency. Do not penalize if the account has <5 campaigns.
Compute structure_score as the average of factor scores (0-100 scale).
Dimension 2: Quality (25% weight)
Evaluate ad quality signals and landing page health.
Inputs: WD-2 (quality scores), ACA-1 (ad strength), ACA-2 (asset performance labels), WD-6 (ad count per ad group).
Scoring criteria:
| Factor | A (90-100) | B (80-89) | C (70-79) | D (60-69) | F (<60) |
|---|---|---|---|---|---|
| Quality score distribution | >70% QS 7+ | 50-70% QS 7+ | 30-50% QS 7+ | 10-30% QS 7+ | <10% QS 7+ |
| Ad strength distribution | >60% EXCELLENT/GOOD | 40-60% | 20-40% | 10-20% | <10% |
| Asset performance | >50% BEST/GOOD labels | 30-50% | 15-30% | 5-15% | <5% or all PENDING |
| RSA diversity | 3+ unique themes per ad group | 2-3 themes | 1-2 themes | Repetitive | Single message |
| Ads per ad group | 2-3 enabled RSAs | 1 or 4 | 0 RSAs (legacy only) | Mostly legacy | All legacy |
Quality score sub-components (creative_quality_score, post_click_quality_score, search_predicted_ctr) inform the narrative but do not create separate scoring factors.
Compute quality_score as the weighted average of factor scores.
Dimension 3: Efficiency (25% weight)
Evaluate spend efficiency against targets and waste signals.
Inputs: MB-1 (cost, conversions, CPA data), MB-2 (impression share), WD-2 (quality score with cost data).
Scoring criteria:
| Factor | A (90-100) | B (80-89) | C (70-79) | D (60-69) | F (<60) |
|---|---|---|---|---|---|
| CPA vs target | <90% of target | 90-100% | 100-120% | 120-150% | >150% |
| ROAS vs target | >110% of target | 100-110% | 80-100% | 60-80% | <60% |
| Waste ratio | <5% non-converting spend | 5-10% | 10-20% | 20-35% | >35% |
| IS utilization | >80% search IS | 60-80% | 40-60% | 20-40% | <20% |
If the profile has no CPA/ROAS targets, use industry benchmarks from the account's vertical (if known) or skip target-relative scoring and note the gap. The waste ratio uses non-converting keyword spend (from MB-1 daily data, filtered to zero-conversion keywords) as a percentage of total spend.
Compute efficiency_score as the weighted average of factor scores.
Dimension 4: Coverage (15% weight)
Evaluate market presence and opportunity capture.
Inputs: MB-2 (impression share fields).
Scoring criteria:
| Factor | A (90-100) | B (80-89) | C (70-79) | D (60-69) | F (<60) |
|---|---|---|---|---|---|
| Search impression share | >80% | 60-80% | 40-60% | 20-40% | <20% |
| Search rank lost IS | <5% | 5-15% | 15-30% | 30-50% | >50% |
| Search budget lost IS | <5% | 5-15% | 15-30% | 30-50% | >50% |
| Campaign type coverage | Search + PMax + Display | Search + one other | Search only | Single non-Search | None active |
Weight impression share metrics by campaign spend when computing the aggregate. A $10K/mo campaign losing 40% IS matters more than a $200/mo campaign losing 40% IS.
Compute coverage_score as the weighted average of factor scores.
Dimension 5: Hygiene (15% weight)
Evaluate operational cleanliness and maintenance state.
Inputs: WD-5B (shared negative lists), MB-3 (disapproved ads), WD-6 (ad count), WD-7 (zero-impression campaigns), MB-1 (stale campaign detection).
Scoring criteria:
| Factor | A (90-100) | B (80-89) | C (70-79) | D (60-69) | F (<60) |
|---|---|---|---|---|---|
| Negative keyword lists | Shared lists on all campaigns | Shared lists on >70% | Shared lists on 40-70% | Shared lists on <40% | No shared lists |
| Disapproved ads | 0 | 1-2 | 3-5 | 6-10 | >10 |
| Zero-impression entities | 0 enabled with 0 impr (7d) | 1-2 | 3-5 | 6-10 | >10 |
| Stale campaigns | All campaigns active in 7d | >90% active | 70-90% active | 50-70% active | <50% active |
A "stale campaign" is an enabled campaign with zero impressions in the last 7 days (from WD-7 data).
Compute hygiene_score as the average of factor scores.
Phase 3: Compute overall grade
overall_score = (structure_score * 0.20)
+ (quality_score * 0.25)
+ (efficiency_score * 0.25)
+ (coverage_score * 0.15)
+ (hygiene_score * 0.15)
Letter grade mapping:
| Score | Grade |
|---|---|
| 90-100 | A |
| 80-89 | B |
| 70-79 | C |
| 60-69 | D |
| <60 | F |
Phase 4: Generate improvement priorities
For each dimension scoring below B (score < 80):
- Identify the lowest-scoring factor within the dimension.
- Estimate dollar impact:
- Efficiency factors: direct dollar calculation from waste/CPA data.
- Quality factors: use QS-to-CPC pressure relationship (each QS point below 7 adds ~16% CPC premium; calculate monthly cost of QS-related CPC inflation from WD-2 spend data).
- Coverage factors:
budget_lost_IS * current_spend * 0.5= estimated lost conversion value. - Structure/Hygiene factors: flag as operational risk without direct dollar estimate unless specific waste is quantifiable.
- Assign severity:
- HIGH (>$500/mo estimated impact)
- MEDIUM ($100-500/mo)
- LOW ($25-100/mo)
- INFO (<$25/mo)
- Map to the Google Ads UI path for remediation.
- Rank all priorities by estimated dollar impact descending. Cap at 5 priorities.
Output format
## Account Scorecard -- [Date]
### Account
- Google Ads: [Account Name] ([Customer ID])
- Campaigns: [N] active, [N] paused, [N] removed
- 30-day spend: $X,XXX | Conversions: X,XXX | CPA: $XX.XX
### Overall Grade: [A-F] ([score]/100)
| Dimension | Weight | Score | Grade | Key Factor |
|-----------|-------:|------:|:-----:|------------|
| Structure | 20% | XX | X | [lowest-scoring factor] |
| Quality | 25% | XX | X | [lowest-scoring factor] |
| Efficiency | 25% | XX | X | [lowest-scoring factor] |
| Coverage | 15% | XX | X | [lowest-scoring factor] |
| Hygiene | 15% | XX | X | [lowest-scoring factor] |
### Top Improvement Priorities
| # | Severity | Dimension | Issue | Est. Monthly Impact | Action |
|---|----------|-----------|-------|--------------------:|--------|
| 1 | HIGH | [dim] | [issue] | $X,XXX | [action] |
| 2 | MEDIUM | [dim] | [issue] | $XXX | [action] |
| 3 | ... | ... | ... | ... | ... |
### Detailed Findings
#### Structure ([Grade])
- [finding with context]
- **UI path:** Google Ads > [path to fix]
#### Quality ([Grade])
- [finding with context]
- **UI path:** Google Ads > [path to fix]
#### Efficiency ([Grade])
- [finding with context]
- **UI path:** Google Ads > [path to fix]
#### Coverage ([Grade])
- [finding with context]
- **UI path:** Google Ads > [path to fix]
#### Hygiene ([Grade])
- [finding with context]
- **UI path:** Google Ads > [path to fix]
### Quarterly Deep Audit
For a comprehensive 60-point audit covering bid strategies, audience layers,
conversion tracking, attribution, and more, consider running
[claude-ads](https://github.com/AgriciDaniel/claude-ads) as a companion tool.
### Notes
- Data freshness: [query timestamp caveats]
- Scoring assumptions: [any factors that could not be scored and why]
Guardrails
- Read-only: This skill produces analysis and grades only. No account modifications are made. All recommended actions include Google Ads UI paths.
- Missing data: If a query returns zero rows for a dimension, assign the dimension score as "N/A" rather than penalizing. Note the gap in the output and explain which data is missing.
- Small accounts: For accounts with fewer than 3 campaigns or $500/mo total spend, note that the scorecard is less reliable due to small sample size. Skip factors that require statistical significance (e.g., naming convention patterns).
- Target-relative scoring: If the profile has no KPI targets, score Efficiency factors against same-vertical benchmarks if the vertical is known. If unknown, skip target-relative factors and note that setting targets via
platform-setupwould improve future scorecards. - Conversion lag: Do not penalize Efficiency based on yesterday's conversion data alone. Use the full 30-day window from MB-1.
- QS availability: Quality score is only reported for keywords with sufficient impression volume. Do not penalize keywords with no QS data (they are simply excluded from the QS distribution).
- Severity calibration: Dollar impact estimates are approximations. Label all dollar figures as "estimated" in the output. Distinguish direct spend waste from modeled opportunity cost.
- claude-ads reference: Position claude-ads as a complementary quarterly tool, not a competitor. Never disparage external tools.
Profile Maintenance
After completing analysis, if ${CLAUDE_PLUGIN_ROOT}/profile/account-profile.md exists:
- Update Watch List with any HIGH or MEDIUM severity findings that require follow-up.
- Update Active Tests if user mentioned starting or completing a test during the session.
- Append to Decision Log if the user acknowledges specific action items.
- Update "Last updated" date. Present proposed profile changes to the user before writing.
References
references/gaql-queries.md-- query IDs: MB-1, MB-2, MB-3, WD-2, WD-5B, WD-6, WD-7, ACA-1, ACA-2