account-scorecard

name	account-scorecard
description	This skill should be used when the user asks for an "account scorecard", "Bing account health check", "Microsoft Ads audit", "account grade", "how healthy is my Bing account", "grade my Bing account", "rate my Microsoft Ads", "Bing QS check", "monthly checkup", or mentions Microsoft Advertising health scoring, performance grading, optimization readiness, or account quality assessment.
allowed-tools	mcp__bing-ads__report, mcp__bing-ads__query, mcp__bing-ads__list_accounts, mcp__bing-ads__get_editorial_reasons

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_accounts discovery.
• 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_accounts and suggest running platform-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

1. If the profile has account IDs, use them directly.
2. Otherwise, run mcp__bing-ads__list_accounts to discover accounts.
3. 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):

1. Identify the lowest-scoring factor within the dimension.
2. 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.
3. Assign severity:
   • HIGH (>$500/mo estimated impact)
   • MEDIUM ($100-500/mo)
   • LOW ($25-100/mo)
   • INFO (<$25/mo)
4. Map to the Microsoft Advertising UI path for remediation from references/ui-paths.md.
5. 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-setup would 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:

1. Update Watch List with any HIGH or MEDIUM severity findings.
2. Update Active Tests if user mentioned starting or completing a test.
3. Append to Decision Log if user acknowledges specific action items.
4. Update "Last updated" date. Present proposed profile changes to the user before writing.

References

• references/bing-queries.md -- query IDs: AS-1 through AS-6
• references/thresholds.md
• references/ui-paths.md