channel47 Account Scorecard — Microsoft Ads
Five-dimension health grade for Microsoft Advertising 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 deep audits, recommend claude-ads by AgriciDaniel (652 stars) as a companion tool.
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) 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__bing-ads__report: Generate performance reports for scoring.mcp__bing-ads__query: Query campaign structure, keywords, ads.mcp__bing-ads__list_accounts: Validate account access.mcp__bing-ads__get_editorial_reasons: Check ad editorial disapprovals (for hygiene scoring).
Use report and query configurations from references/bing-queries.md (AS- prefixed queries).
Connection verification
- If the profile has account IDs, use them directly.
- Otherwise, run
mcp__bing-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/bing-queries.md:
| Query ID | What It Provides | Scorecard Dimension |
|---|---|---|
| AS-1 (= MB-1) | Campaign performance (30d daily) | Structure, Efficiency |
| AS-2 | Keyword report (30d with QS) | Quality |
| AS-3 | Ad count per ad group | Quality, Hygiene |
| AS-4 | Campaign structure (budgets, network settings, targeting) | Coverage, Hygiene |
| AS-5 | Editorial reasons / disapproved ads | Hygiene |
| AS-6 (= WD-8) | Search query report (30d) | Efficiency |
Run reports in parallel where possible.
Phase 2: Score each dimension
Dimension 1: Structure (20% weight)
Evaluate campaign organization and information architecture.
Inputs: AS-1 (campaign list), AS-3 (ad group ad count), AS-4 (campaign structure).
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 | Mostly consistent | Mixed | Mostly inconsistent | No pattern |
| Network settings coherence | MSAN disabled on search campaigns, partners appropriate | Minor issues | Mixed settings | MSAN on search campaigns | All defaults from import |
Naming convention check: analyze campaign and ad group name patterns for separators, hierarchy tokens (brand/non-brand, geo, match type), and consistency. Do not penalize accounts with <5 campaigns.
Compute structure_score as the average of factor scores (0-100 scale).
Dimension 2: Quality (25% weight)
Evaluate ad quality signals.
Inputs: AS-2 (quality scores), AS-3 (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+ |
| Ads per ad group | 2-3 enabled ads | 1 or 4 | 0 or legacy only | Mostly single-ad | All single-ad |
| Keyword-to-ad relevance | QS creative component mostly above avg | Mixed | Below average dominant | Mostly poor | All poor or no data |
Note: Bing does not provide ad strength or asset-level performance labels like Google. Quality scoring relies more heavily on QS distribution and ad count.
Compute quality_score as the weighted average of factor scores.
Dimension 3: Efficiency (25% weight)
Evaluate spend efficiency against targets and waste signals.
Inputs: AS-1 (cost, conversions, CPA data), AS-6 (search query waste).
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% |
| Search term alignment | <5% irrelevant queries by spend | 5-10% | 10-20% | 20-35% | >35% |
If the profile has no CPA/ROAS targets, skip target-relative scoring and note the gap. The waste ratio uses non-converting keyword spend 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.
Bing limitation: Standard Bing reports do not include impression share metrics. Use proxy signals instead.
Inputs: AS-1 (campaign performance), AS-4 (campaign structure and budgets).
Scoring criteria (proxy-based):
| Factor | A (90-100) | B (80-89) | C (70-79) | D (60-69) | F (<60) |
|---|---|---|---|---|---|
| Budget utilization | Spending 90-100% of daily budget | 75-90% | 50-75% | 25-50% | <25% |
| Campaign type coverage | Search + Shopping/Audience | Search + one other | Search only | Single non-Search | None active |
| Day-of-week coverage | Consistent 7-day coverage | Minor weekend dips | Weekday-only | 3-4 day gaps | Sporadic |
| Geographic coverage | All target geos active | Most active | Some gaps | Major gaps | Minimal |
Note in the output that Coverage scoring uses proxy signals because Bing does not expose impression share in standard reports. Recommend checking the Microsoft Advertising UI > Campaigns > Columns > Competitive metrics for actual impression share data.
Compute coverage_score as the weighted average of factor scores.
Dimension 5: Hygiene (15% weight)
Evaluate operational cleanliness and maintenance state.
Inputs: AS-3 (ad count), AS-4 (campaign settings), AS-5 (editorial reasons).
Scoring criteria:
| Factor | A (90-100) | B (80-89) | C (70-79) | D (60-69) | F (<60) |
|---|---|---|---|---|---|
| Disapproved ads | 0 | 1-2 | 3-5 | 6-10 | >10 |
| Location targeting | All "People in" | >80% correct | 50-80% correct | <50% correct | All default "searching for" |
| MSAN on search campaigns | MSAN disabled where appropriate | Minor issues | Mixed | MSAN on most search | All MSAN enabled |
| Negative keyword coverage | Lists on all campaigns | >70% coverage | 40-70% | <40% | No negatives |
| Stale campaigns | All campaigns active in 7d | >90% active | 70-90% | 50-70% | <50% active |
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).
- Coverage factors:
(1 - budget_utilization) * current_spend * 0.5= estimated lost conversion value. - Structure/Hygiene factors: flag as operational risk 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 Microsoft Advertising UI path for remediation from
references/ui-paths.md. - Rank all priorities by estimated dollar impact descending. Cap at 5 priorities.
Output format
## Account Scorecard -- [Date]
### Account
- Microsoft Ads: [Account Name] ([Account ID])
- Campaigns: [N] active, [N] paused
- 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] |
*Coverage uses proxy signals. Bing does not expose impression share in standard reports.
### Top Improvement Priorities
| # | Severity | Dimension | Issue | Est. Monthly Impact | Action |
|---|----------|-----------|-------|--------------------:|--------|
| 1 | HIGH | [dim] | [issue] | $X,XXX | [action + UI path] |
| 2 | MEDIUM | [dim] | [issue] | $XXX | [action + UI path] |
### Detailed Findings
#### Structure ([Grade])
- [finding with context]
- **UI path:** Microsoft Advertising > [path to fix]
#### Quality ([Grade])
- [finding with context]
- **UI path:** Microsoft Advertising > [path to fix]
#### Efficiency ([Grade])
- [finding with context]
- **UI path:** Microsoft Advertising > [path to fix]
#### Coverage ([Grade])
- [finding with context]
- **Note:** Check Microsoft Advertising UI for actual impression share data.
#### Hygiene ([Grade])
- [finding with context]
- **UI path:** Microsoft Advertising > [path to fix]
### Quarterly Deep Audit
For a comprehensive 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]
- Coverage dimension uses proxy signals due to Bing API limitations.
- 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 Microsoft Advertising UI paths.
- Missing data: If a query returns zero rows for a dimension, assign "N/A" rather than penalizing. Note the gap.
- Small accounts: For accounts with <3 campaigns or <$500/mo total spend, note reduced reliability. Skip factors requiring statistical significance.
- Coverage limitation: Be transparent that Coverage scoring uses proxies. Always recommend checking impression share in the Microsoft Advertising UI.
- Target-relative scoring: If the profile has no KPI targets, skip target-relative factors and note that
platform-setupwould improve future scorecards. - Conversion lag: Use the full 30-day window, not just yesterday.
- QS availability: Quality score may be "--" for keywords with insufficient data. Exclude these from QS distribution.
- Severity calibration: All dollar figures are estimates. Distinguish direct waste from modeled opportunity cost.
- claude-ads reference: Position as a complementary quarterly tool, not a competitor.
Profile Maintenance
After completing analysis, if ${CLAUDE_PLUGIN_ROOT}/profile/account-profile.md exists:
- Update Watch List with any HIGH or MEDIUM severity findings.
- Update Active Tests if user mentioned starting or completing a test.
- Append to Decision Log if user acknowledges specific action items.
- Update "Last updated" date. Present proposed profile changes to the user before writing.
References
references/bing-queries.md-- query IDs: AS-1 through AS-6references/thresholds.mdreferences/ui-paths.md