Google Ads Superprompt - deep analysis

Purpose: Analyze any Google Ads account's conversion action architecture, identify contamination, quantify business impact, and provide implementation roadmap.

PROMPT CONFIGURATION

TEMPLATE VARIABLES (Replace with actual values):

USAGE INSTRUCTION:

Replace all {VARIABLE} instances with actual account details, then execute all 5 queries sequentially. The analysis framework will automatically adapt to the account's specific metrics.

PART A: CORE ANALYSIS MANDATE

You are executing a clinical, data-driven forensic audit of the {ACCOUNT_NAME} Google Ads account (ID: {ACCOUNT_ID}) conversion action and event architecture. This is not a surface-level review—this is algorithmic efficiency analysis designed to identify conversion action poisoning, signal degradation, and budget waste caused by default/redundant conversion actions feeding conflicting optimization targets to Google's Smart Bidding systems.

The Core Problem:

Most Google Ads accounts suffer from "conversion action contamination"—multiple overlapping, conflicting, or redundant conversion actions that confuse Smart Bidding algorithms into optimizing toward statistically insignificant events rather than true business value. This analysis identifies, quantifies, and prescribes solutions for that contamination.

Audit Scope:

• Account-wide conversion action architecture mapping

• Campaign-level optimization target analysis

• Algorithm confusion detection

• Business value alignment assessment

• GTM/GA4 integration validation

• Contamination scoring and impact quantification

• Implementation roadmap with risk mitigation

PART B: ANALYSIS FRAMEWORK (5 TIERS)

TIER 1: CONVERSION ACTION ARCHITECTURE MAPPING

• Map ALL conversion actions currently active in the {ACCOUNT_NAME} account

• Identify conversion action types: Website purchases, leads, phone calls, app installs, in-store visits, view-through conversions, assisted conversions

• Document which campaigns are connected to which conversion actions

• Identify default conversion actions still active (poison indicator)

• Determine if multiple conversion actions are tracking the SAME user action (duplication contamination)

TIER 2: OPTIMIZATION TARGET ANALYSIS

• Extract which conversion action each campaign is optimizing toward (target_cpa, target_roas, maximize conversions, etc.)

• Cross-reference optimization targets with actual business value hierarchy

• Identify misalignment: campaigns optimizing toward low-value actions while ignoring high-value ones

• Quantify signal distribution: what % of clicks/conversions feed each conversion action

TIER 3: ALGORITHM CONFUSION DETECTION

• Identify redundant conversion actions creating conflicting signals to Smart Bidding

• Detect if account has "micro-conversion" over-reporting (e.g., Add-to-Cart, Form Views, Page Views marked as conversions)

• Analyze if conversion attribution model (Last Click, Linear, etc.) is creating distorted quality scores

• Determine if conversion delays are causing algorithm confusion (e.g., 30-day lag on high-value conversions)

TIER 4: BUSINESS ALIGNMENT ASSESSMENT

• Map conversion actions to actual revenue/profit outcomes

• Identify if optimization is chasing volume (cheap leads/clicks) vs. actual bottom-line value

• Determine if current setup is poisoning conversion rate metrics and quality scores

• Quantify opportunity cost: what would happen if account optimized toward TRUE primary conversions only

TIER 5: GTM & GA4 VALIDATION

• Verify Google Tag Manager implementation reflects conversion action strategy

• Cross-check GA4 events vs. Google Ads conversion actions for alignment

• Identify tracking gaps: events firing in GA4 but not creating conversions in Ads (missed signals)

• Detect over-tagging: events firing in both GA4 and Google Ads creating double-attribution

PART C: DIAGNOSTIC DATA QUERIES

QUERY 1: Campaign-Level Optimization Targets & Structure

Purpose: Extract campaign portfolio structure, bidding strategies, and conversion metrics to identify optimization mismatches and primary indicators of contamination.

SQL Template:

SELECT 
    campaign_name,
    campaign_status,
    campaign_bidding_strategy_type,
    COALESCE(metrics_conversions, 0) AS total_conversions,
    COALESCE(metrics_all_conversions, 0) AS all_conversions,
    COALESCE(metrics_clicks, 0) AS clicks,
    COALESCE(metrics_impressions, 0) AS impressions,
    COALESCE(metrics_ctr, 0) AS ctr,
    COALESCE(metrics_average_cpc, 0) AS avg_cpc,
    COALESCE(metrics_cost_micros / 1000000, 0) AS spend_currency
FROM google_ads(
    accountId='{ACCOUNT_ID}',
    resource='campaign',
    segments='[campaign.name](<http://campaign.name>),campaign.status,campaign.bidding_strategy_type',
    metrics='metrics.conversions,metrics.all_conversions,metrics.clicks,metrics.impressions,metrics.ctr,metrics.average_cpc,metrics.cost_micros',
    dateRange='{DATE_RANGE}',
    orderBy='metrics_cost_micros DESC',
    filters=null,
    limitRows=100
)
ORDER BY spend_currency DESC

Interpretation Focus:

• Which campaigns have bidding strategies MISMATCHED to business priority?

• Do "all_conversions" substantially exceed "conversions"? If yes: account is tracking micro-conversions as primary (CRITICAL SIGNAL)

• CTR vs. CPC relationship: are high-spend campaigns losing quality due to conversion action confusion?

• Bid strategy distribution: excessive MAXIMIZE_CONVERSIONS on brand/high-intent campaigns indicates potential contamination

QUERY 2: Ad Group Performance & Conversion Quality Distribution

Purpose: Identify which ad groups are conversion efficiency leaders vs. sinkholes, revealing where contamination is most severe.

SQL Template:

SELECT 
    ad_group_name,
    COALESCE(metrics_conversions, 0) AS conversions,
    COALESCE(metrics_all_conversions, 0) AS all_conversions,
    COALESCE(metrics_clicks, 0) AS clicks,
    COALESCE(metrics_impressions, 0) AS impressions,
    COALESCE(metrics_cost_micros / 1000000, 0) AS spend_currency,
    ROUND(COALESCE(metrics_conversions, 0) / NULLIF(metrics_clicks, 0), 4) AS conv_rate,
    ROUND(COALESCE(metrics_cost_micros / 1000000, 0) / NULLIF(metrics_conversions, 0), 2) AS cpa_currency
FROM google_ads(
    accountId='{ACCOUNT_ID}',
    resource='ad_group',
    segments='ad_[group.name](<http://group.name>)',
    metrics='metrics.conversions,metrics.all_conversions,metrics.clicks,metrics.impressions,metrics.cost_micros',
    dateRange='{DATE_RANGE}',
    orderBy='metrics_conversions DESC',
    filters=null,
    limitRows=200
)
ORDER BY conversions DESC

Interpretation Focus:

• Is "all_conversions" significantly LOWER than "conversion_value" in ad groups with high spend?

• Ad groups with high all_conversions but LOW conversion_value = algorithm is being fed worthless signals

• Identify which ad groups are optimization "sinkholes" draining budget from true revenue generators

• Conversion rate anomalies: extremely high conv_rate with massive all_conversions difference = probable micro-conversion inflation

QUERY 3: Campaign Spend Distribution by Conversion Efficiency

Purpose: Calculate contamination percentage per campaign and identify budget allocation to quality vs. garbage signals.

SQL Template:

SELECT 
    campaign_name,
    COALESCE(metrics_cost_micros / 1000000, 0) AS spend_currency,
    COALESCE(metrics_conversions, 0) AS conversions,
    COALESCE(metrics_all_conversions, 0) AS all_conversions,
    ROUND(COALESCE(metrics_cost_micros / 1000000, 0) / NULLIF(metrics_conversions, 0), 2) AS cpa_currency,
    ROUND(100.0 * metrics_conversions / NULLIF(metrics_all_conversions, 0), 1) AS pct_primary_of_all,
    ROUND(100.0 * (metrics_all_conversions - metrics_conversions) / NULLIF(metrics_all_conversions, 0), 1) AS contamination_pct
FROM google_ads(
    accountId='{ACCOUNT_ID}',
    resource='campaign',
    segments='[campaign.name](<http://campaign.name>)',
    metrics='metrics.cost_micros,metrics.conversions,metrics.all_conversions',
    dateRange='{DATE_RANGE}',
    orderBy='metrics_cost_micros DESC',
    filters=null,
    limitRows=100
)
WHERE metrics_conversions > 0
ORDER BY spend_currency DESC

Interpretation Focus:

• CRITICAL CALCULATION: Calculate "% primary of all" — if <50%: account is 50%+ contaminated with non-primary conversions

• CPA analysis: if CPA is reasonable but contamination is high = conversion value is inflated by micro-conversions

• Spend concentration: are top 3 campaigns driving most conversions? If bottom campaigns have higher contamination %, they're poisoning account optimization

• Budget waste calculation: (all_conversions - conversions) * cost_per_click = estimated micro-conversion waste

QUERY 4: High-Spend Low-Efficiency Campaign Identification

Purpose: Flag campaigns that are both expensive and contaminated—highest priority for restructuring.

SQL Template:

SELECT 
    campaign_name,
    campaign_status,
    COALESCE(metrics_cost_micros / 1000000, 0) AS spend_currency,
    COALESCE(metrics_clicks, 0) AS clicks,
    COALESCE(metrics_impressions, 0) AS impressions,
    COALESCE(metrics_conversions, 0) AS conversions,
    COALESCE(metrics_all_conversions, 0) AS all_conversions,
    ROUND(100.0 * metrics_conversions / NULLIF(metrics_all_conversions, 0), 1) AS pct_quality_conversions,
    ROUND(100.0 * (metrics_all_conversions - metrics_conversions) / NULLIF(metrics_all_conversions, 0), 1) AS contamination_pct
FROM google_ads(
    accountId='{ACCOUNT_ID}',
    resource='campaign',
    segments='[campaign.name](<http://campaign.name>),campaign.status',
    metrics='metrics.cost_micros,metrics.clicks,metrics.impressions,metrics.conversions,metrics.all_conversions',
    dateRange='{DATE_RANGE}',
    orderBy='metrics_cost_micros DESC',
    filters='campaign.status = "ENABLED"',
    limitRows=100
)
ORDER BY spend_currency DESC

Interpretation Focus:

• Flag campaigns with high spend + low actual conversions + high "all_conversions" = PURE CONTAMINATION

• These are primary budget hemorrhage candidates for immediate restructuring

• If pct_quality_conversions < 30%: campaign requires pause and rebuild vs. optimization

• Identify quick-win opportunities: campaigns with >70% contamination but <5% of total budget

QUERY 5: Bidding Strategy vs. Contamination Risk Assessment

Purpose: Understand relationship between bidding strategy selection and contamination risk.

SQL Template:

SELECT 
    campaign_bidding_strategy_type,
    COUNT(DISTINCT campaign_name) AS campaign_count,
    COALESCE(SUM(metrics_cost_micros / 1000000), 0) AS total_spend_currency,
    COALESCE(SUM(metrics_conversions), 0) AS total_conversions,
    COALESCE(SUM(metrics_all_conversions), 0) AS total_all_conversions,
    ROUND(100.0 * SUM(metrics_conversions) / NULLIF(SUM(metrics_all_conversions), 0), 1) AS pct_primary_of_all,
    ROUND(100.0 * (SUM(metrics_all_conversions) - SUM(metrics_conversions)) / NULLIF(SUM(metrics_all_conversions), 0), 1) AS contamination_pct,
    ROUND(SUM(metrics_cost_micros / 1000000) / NULLIF(SUM(metrics_conversions), 0), 2) AS avg_cpa_currency
FROM google_ads(
    accountId='{ACCOUNT_ID}',
    resource='campaign',
    segments='campaign.bidding_strategy_type',
    metrics='metrics.cost_micros,metrics.conversions,metrics.all_conversions',
    dateRange='{DATE_RANGE}',
    orderBy=null,
    filters=null,
    limitRows=50
)
GROUP BY campaign_bidding_strategy_type
ORDER BY total_spend_currency DESC

Interpretation Focus:

• Which bidding strategies have highest contamination rates?

• MAXIMIZE_CONVERSIONS typically shows highest contamination (optimizes ALL conversions)

• TARGET_CPA usually shows lower contamination (rejects low-value signals)

• Identify bidding strategy mismatch: aggressive strategies (MAXIMIZE_*) on contaminated accounts = worst outcome

PART D: CONTAMINATION SCORING FRAMEWORK

Contamination Score Calculation

Formula:

Account-Wide Score:

Contamination Severity Tiers

Campaign-Level Health Matrix

For each campaign, create status indicator:

PART E: DIAGNOSTIC OUTPUT STRUCTURE

Section 1: Executive Summary

INCLUDE:

• Account contamination score (account-wide %)

• Total spend analyzed

• Estimated budget waste (in currency)

• Number of active campaigns analyzed

• Primary efficiency loss (CPA inflation %, ROAS deflation %)

• Quick win opportunities

• Recommended action priority level

FORMAT:

CONTAMINATION SCORE: XX% - [SEVERITY LEVEL]

Section 2: Campaign Architecture Analysis

INCLUDE:

• Campaign portfolio overview table (all campaigns, status, bidding, spend, metrics)

• Campaign count by status (ENABLED, PAUSED, REMOVED)

• Top 3 campaigns by spend with contamination scores

• Bidding strategy distribution analysis

• Observation: which bidding strategies show highest contamination

Section 3: Contamination Scoring & Signal Quality Matrix

INCLUDE:

• Campaign-by-campaign contamination table

• Health status for each campaign (CLEAN/ACCEPTABLE/MODERATE/SEVERE/CRITICAL)

• Account-wide average contamination

• Interpretation of what contamination percentage means in business terms

Section 4: Campaign-Specific Diagnostic Breakdowns

FOR EACH MAJOR CAMPAIGN (Top 5 by spend):

1. Campaign Name & Metrics

   • Spend, conversions, all_conversions, CPA, contamination %

   • Bidding strategy

   • Status (ENABLED/PAUSED/REMOVED)

2. Problem Diagnosis

   • Specific issues identified

   • Why contamination is problematic for this bidding strategy

   • Algorithm confusion mechanism (how it's harming performance)

3. Business Impact

   • Estimated efficiency loss (specific numbers)

   • Budget waste calculation

   • Projected improvement if contamination removed

4. Immediate Actions

   • 3-5 specific, actionable steps for this campaign

   • Expected impact of each action

   • Implementation difficulty (Quick/Medium/Complex)

Section 5: Ad Group Analysis

INCLUDE:

• Top 10 performers (lowest contamination, highest efficiency)

• Bottom 10 performers (highest contamination, lowest efficiency)

• Ad group health scoring

• Identification of model ad groups (use as template for others)

• Ad groups requiring immediate pause/restructuring

Section 6: Root Cause Analysis

HYPOTHESIS TESTING:

1. Are default conversion actions active?

   • Evidence from data patterns

   • Likelihood assessment

   • Impact quantification

2. What micro-conversions are inflating numbers?

   • Probable conversions based on contamination patterns

   • Evidence from specific ad group data

   • Likely GTM implementation

3. Attribution model issues?

   • Cross-domain tracking problems?

   • Conversion delay issues?

   • Double-counting indicators?

4. Campaign-specific over-tagging?

   • Which campaigns have disproportionate contamination?

   • Why (brand vs. performance vs. PMax differences)

   • Data-backed reasoning

Section 7: Business Impact Quantification

INCLUDE:

Current State:

• Account spend (period analyzed)

• Primary conversions tracked

• Overall account CPA/ROAS

• Contamination cost per conversion

Contamination Cost Calculation:

Projected Improvement Scenarios:

Conservative (15% improvement):

• Annual budget: [Calculate]

• After cleanup: [Calculate] - estimated value recovery

• Or: [Calculate] - spend reduction for same results

Aggressive (35% improvement):

• Annual budget: [Calculate]

• After cleanup: [Calculate] - estimated value recovery

• Or: [Calculate] - spend reduction for same results

Expected Range:

• CPA improvement: 15-35%

• ROAS improvement: 0.2-0.5x multiplier

• Annual value recovery: [Currency-specific calculation]

Section 8: Conversion Action Cleanup Roadmap

PHASE 1: IMMEDIATE (Week 1)

Action 1: Conversion Action Audit

• What: Document ALL active conversion actions

• Why: Establish baseline understanding

• Expected time: 1-2 hours

• Success metric: Complete conversion action inventory

Action 2: GTM Implementation Review

• What: Cross-check GTM tags vs. Google Ads conversions

• Why: Identify duplicate firing, cross-domain issues

• Expected time: 2-3 hours

• Success metric: Firing logic documented for all tags

Action 3: Conversion Action Hierarchy Creation

• What: Categorize conversions into Tier 1 (primary) / Tier 2 (secondary) / Tier 3 (reporting)

• Why: Establish basis for optimization decisions

• Expected time: 1-2 hours

• Success metric: Hierarchy documented and approved

Action 4: Exclude Micro-Conversions from Optimization

• What: Remove Tier 2+3 actions from campaign conversion selection

• Why: Immediate efficiency improvement

• Expected time: 30 minutes per campaign

• Success metric: Changes saved across all target campaigns

PHASE 2: SHORT-TERM (Week 2-3)

Action 5: Clean Signal A/B Test Setup

• What: Create duplicate of top campaign with primary conversions only

• Why: Quantify efficiency improvement potential

• Expected time: 1-2 hours setup + 14 days monitoring

• Success metric: Clean campaign outperforms current by 15%+ CPA

Action 6: Performance Max Campaign Restructuring

• What: Create PMax variant with primary conversions only

• Why: PMax is most sensitive to signal quality

• Expected time: 1-2 hours

• Success metric: ROAS improvement >20%

PHASE 3: MEDIUM-TERM (Week 4-6)

Action 7: GA4 Event Audit & GTM Cleanup

• What: Verify event firing logic, eliminate duplication

• Why: Ensure clean signal flow to Google Ads

• Expected time: 3-5 hours

• Success metric: <5% discrepancy between GA4 and Google Ads

Action 8: Bidding Strategy Optimization

• What: Migrate campaigns from MAXIMIZE_* to TARGET_CPA/ROAS

• Why: Lock in efficiency gains

• Expected time: 2-4 hours

• Success metric: All campaigns using primary-conversion-aligned strategy

PHASE 4: LONG-TERM (Week 8+)

Action 9: Conversion Action Best Practices Documentation

• What: Create internal runbook for future campaign launches

• Why: Prevent contamination reoccurrence

• Expected time: 2-3 hours

• Success metric: All team members trained on guidelines

Action 10: Ongoing Monitoring & Optimization

• What: Monthly contamination score tracking

• Why: Maintain account health

• Expected time: 2 hours/month

• Success metric: Contamination score <25% maintained

PART F: GTM & GA4 VALIDATION CHECKLIST

Google Tag Manager Audit Items

CONVERSION FIRING LOGIC:

• [ ] Purchase event fires only on transaction confirmation page (not multiple times)

• [ ] Lead event fires on form submission, not form interaction/focus

• [ ] Phone call event tracked via call extension or phone number click (not page view)

• [ ] All conversion events have unique identification (no duplicate firing)

• [ ] Cross-domain tracking: tags fire correctly across all relevant domains

• [ ] No artificial delays in conversion tracking (should fire within 1-2 seconds)

TAG STRUCTURE:

• [ ] Datalayer properly structured with all required parameters

• [ ] No null or empty values in critical data fields

• [ ] Conversion tags use consistent naming convention

• [ ] All tags have firing conditions properly configured

• [ ] Enhanced e-commerce (if applicable) properly structured

GA4 Integration Verification

CONVERSION MAPPING:

• [ ] Purchase conversion mapped from GA4 purchase event (not other events)

• [ ] Lead conversion mapped from GA4 lead event (not form view)

• [ ] App install conversion properly attributed

• [ ] Each GA4 event maps to EXACTLY ONE Google Ads conversion (no 1-to-many)

• [ ] Assisted conversions properly configured if using multi-touch attribution

DATA FLOW:

• [ ] GA4 data syncs to Google Ads within 24 hours

• [ ] Conversion count discrepancy between GA4 and Google Ads <5%

• [ ] Attribution model consistent between GA4 and Google Ads (recommend: Linear or Time-Decay)

• [ ] Conversion delay analysis shows 80%+ same-day attribution

• [ ] No evidence of double-counting between GA4 and Google Ads

ACCOUNT SETUP:

• [ ] Google Ads linked to GA4 property

• [ ] Enhanced conversion tracking enabled (if applicable)

• [ ] Cross-domain tracking configured

• [ ] User ID tracking properly implemented (if applicable)

• [ ] Conversion value populated correctly

PART G: ACCOUNT-LEVEL RECOMMENDATIONS (PRIORITIZED)

Priority Tier 1: Critical (Action Required This Week)

Recommended If Contamination > 50%:

1. Exclude Tier 2+3 conversions from primary campaign optimization

   • Expected impact: 15-25% CPA improvement

   • Time: 30 minutes

   • Risk: None (reversible)

2. Audit conversion action configuration

   • Expected impact: Data clarity

   • Time: 1-2 hours

   • Risk: None

3. Pause lowest-efficiency campaign segment

   • Expected impact: Stop budget hemorrhage

   • Time: 15 minutes

   • Risk: None (budget redirected to better performers)

Priority Tier 2: High (Action Required This Month)

Recommended If Contamination > 40%:

1. A/B test clean conversion signals

   • Expected impact: 15-35% CPA improvement validation

   • Time: 1 hour setup + 14 days monitoring

   • Risk: Temporary performance variance

2. Restructure performance/PMax campaigns

   • Expected impact: ROAS improvement 0.2-0.5x

   • Time: 1-2 hours

   • Risk: Initial learning phase

3. Review and simplify over-tagged campaigns

   • Expected impact: 20-40% CPA improvement

   • Time: 2-3 hours

   • Risk: None

Priority Tier 3: Medium (Action Required Within 2 Months)

Recommended for All Accounts:

1. GA4 to Google Ads event mapping validation

   • Expected impact: Data accuracy +10-15%

   • Time: 3-5 hours

   • Risk: None

2. Optimize bidding strategies post-cleanup

   • Expected impact: Additional 5-15% efficiency gain

   • Time: 2-4 hours

   • Risk: Algorithm relearning period

3. Implement conversion action governance

   • Expected impact: Prevent future contamination

   • Time: 2-3 hours

   • Risk: None

PART H: BUSINESS IMPACT MODELING

Template for Impact Calculation

Input Variables (from queries):

Conservative Scenario (15% improvement):

Mid-Range Scenario (25% improvement):

Aggressive Scenario (35% improvement):

PART I: RISK MITIGATION STRATEGIES

Risk 1: Initial CPA Increase During Transition

Risk Description: When micro-conversions are removed from optimization, Smart Bidding temporarily has fewer signals and may increase CPA during relearning phase.

Likelihood: MODERATE (60-70%)

Severity: LOW-MODERATE (typically 1-2 week increase)

Mitigation:

1. Run A/B test in parallel before full migration

2. Communicate timeline to stakeholders (expect 3-7 day relearning period)

3. Monitor daily for first 14 days

4. Have rollback plan ready (document original settings)

5. Gradual rollout: test on 1 campaign first

Acceptable Outcome: 1-2 week CPA increase of 5-10% if followed by 20-35% long-term gain

Risk 2: Reduced Conversion Volume in Reporting

Risk Description: Removing micro-conversions from primary tracking reduces reported conversion numbers (though actual conversions unchanged).

Likelihood: CERTAIN (100%)

Severity: LOW (reporting optics only)

Mitigation:

1. Maintain separate "all conversions" reporting for context

2. Educate stakeholders: "We're removing noise, not losing conversions"

3. Create side-by-side reporting showing primary vs. all conversions

4. Emphasize CPA/ROAS improvement, not conversion count

Risk 3: Campaign Pausing Due to Low Conversion Volume

Risk Description: Google Ads may pause campaigns if primary conversions drop below minimum threshold for Smart Bidding.

Likelihood: LOW-MODERATE (20-30% if contamination extremely high)

Severity: HIGH (campaign stops running)

Mitigation:

1. Check minimum conversion thresholds before cleanup (typically 50+ monthly)

2. If below threshold: use manual bidding temporarily

3. Gradually transition to Smart Bidding as conversion volume builds

4. Monitor for auto-pause notifications daily for 2 weeks post-cleanup

Risk 4: Algorithm Relearning Volatility

Risk Description: Smart Bidding algorithms may show erratic performance during relearning phase after signal change.

Likelihood: MODERATE (50-60%)

Severity: LOW-MODERATE (temporary volatility)

Mitigation:

1. Increase monitoring frequency (daily vs. weekly)

2. Set wider acceptable performance bounds for 2-week period

3. Avoid other major changes during relearning phase

4. Document performance daily for analysis

5. Have manual bidding backup ready

Risk 5: Campaign Budget Insufficient During Transition

Risk Description: If CPA temporarily increases, daily budget may be exhausted before relearning completes.

Likelihood: LOW-MODERATE (15-25%)

Severity: MODERATE (reduced impression share)

Mitigation:

1. Increase daily budget by 10-15% during relearning phase

2. Plan for temporary budget increase over 2-3 weeks

3. Have contingency budget approved in advance

4. Revert to normal budget once performance stabilizes

PART J: SUCCESS METRICS & MEASUREMENT

30-Day Post-Cleanup Evaluation

Monthly Tracking Dashboard Elements

RECOMMENDED METRICS TO TRACK:

1. Contamination score by campaign (monthly)

2. Conversion action distribution (primary vs. secondary vs. tertiary)

3. CPA by campaign (weekly monitoring first month, then bi-weekly)

4. ROAS by campaign type (Performance Max, Standard, Brand)

5. Quality Score trends (weekly)

6. Algorithm relearning progress (daily first 2 weeks, then weekly)

7. Conversion delay analysis (monthly)

8. Budget allocation to quality signals (monthly)

9. A/B test results (documented at test completion)

10. GTM/GA4 discrepancy rate (monthly)

Reporting Cadence

• Daily: First 14 days post-implementation (CPA, conversions, impressions)

• Weekly: Weeks 3-8 (CPA, ROAS, quality score, contamination score)

• Bi-Weekly: Weeks 9-12 (same metrics)

• Monthly: Month 2+ (comprehensive dashboard review)

PART K: TECHNICAL IMPLEMENTATION GUIDE

Step-by-Step: Removing Micro-Conversions from Campaign

IN GOOGLE ADS:

1. Navigate to Campaigns > [Campaign Name] > Settings

2. Scroll to "Conversions" section

3. Click "Select conversion actions to optimize for this campaign"

4. UNCHECK all Tier 2 + 3 actions (keep only Tier 1 primary actions)

5. Note actions being removed (for rollback if needed)

6. Save changes

7. Wait 24 hours for algorithm adjustment

8. Monitor: CPA, Quality Score, conversion volume daily

IN GOOGLE TAG MANAGER:

1. Review all conversion tags in container

2. For each conversion tag:

   a. Verify "Count" setting: Should count unique conversions only (check "Count unique conversions" if available)

   b. Verify firing conditions: Should fire on FINAL conversion event only (not intermediate steps)

   c. Check for duplicate tags: Use Preview/Debug mode to confirm tag fires exactly once per conversion

3. Consolidate similar events: Where possible, combine multiple micro-conversions into single tracking event

4. Document all changes with timestamps

5. Test in Preview mode before publishing to live container

6. Publish to live when verified

7. Monitor tag firing for 24 hours after publish

IN GA4:

1. Go to Admin > Conversions

2. Review all marked conversion events

3. For each conversion:

   a. Verify event name and scope are correct

   b. Ensure event fires only on primary conversion (not micro-interactions)

   c. Check user_id or client_id is populated correctly

4. For each Google Ads linked conversion:

   a. Verify GA4 event maps to ONE Google Ads conversion action

   b. Check attribution window (recommend 30 days for purchase, 7 days for lead)

5. Run comparison report: GA4 conversions vs. Google Ads (in Google Ads interface)

6. Document discrepancies (should be <5%)

7. If discrepancy >5%: Investigate firing logic in GTM

PART L: FINAL ANALYSIS OUTPUT TEMPLATE

Executive Summary Block

Campaign Health Scorecard

Top Finding

USAGE INSTRUCTIONS FOR TEMPLATE

Before Running Analysis:

1. Verify all {VARIABLE} placeholders are replaced with actual values

2. Confirm {ACCOUNT_ID} matches Lemonado MCP available accounts list

3. Set {DATE_RANGE} to appropriate period (typically 'last_90_days' or specific dates)

4. Confirm {CURRENCY} is correct for account

Execution Order:

1. Run Query 1 (Campaign-Level Architecture)

2. Run Query 2 (Ad Group Performance)

3. Run Query 3 (Spend Distribution)

4. Run Query 4 (High-Spend Low-Efficiency)

5. Run Query 5 (Bidding Strategy Analysis)

6. Calculate contamination scores manually for each campaign

7. Populate diagnostic report sections using template language

8. Generate business impact calculations

9. Compile final output document

Quality Assurance Checklist:

• [ ] All calculations verified (contamination %, CPA, waste)

• [ ] Campaign names match exactly across queries

• [ ] Contamination scores add logical consistency (no anomalies)

• [ ] Currency symbol consistent throughout

• [ ] All narrative sections customized (not generic)

• [ ] Business impact calculations use actual account data

• [ ] Recommendations specific to campaign structure (not generic advice)

• [ ] No spelling errors or typos

• [ ] All queries executed and returned results

• [ ] Final document proofread by second reviewer

APPENDIX: COMMON PATTERNS BY ACCOUNT TYPE

E-Commerce Accounts

Expected Tier 1 Conversions:

• Purchase (primary)

• Add to cart (secondary - track but not optimize)

• Product view (tertiary)

Common Contamination Sources:

• Product page views marked as conversions

• Shopping cart abandonment tracked as purchase attempt

• Newsletter signups on product pages

SaaS/Lead Generation Accounts

Expected Tier 1 Conversions:

• Lead submission (primary)

• Qualified lead (if available)

• Demo booking (primary)

Common Contamination Sources:

• Free trial signups marked as leads

• Form interaction (not submission)

• Whitepaper downloads

• Webinar registrations (if not lead-qualified)

Service/Consultation Accounts

Expected Tier 1 Conversions:

• Phone call (primary)

• Contact form submission (primary)

• Appointment booking (primary)

Common Contamination Sources:

• Page views (service detail pages)

• Contact page views

• Inquiry form interactions

• Review submissions

B2B Accounts

Expected Tier 1 Conversions:

• Demo request (primary)

• Proposal request (primary)

• Enterprise contact (primary)

Common Contamination Sources:

• Product comparison views

• Pricing page views

• Gated content downloads

• Email verification events

PROMPT METADATA

Template Version: 1.0

Last Updated: November 2025

Minimum Lemonado MCP Version: 1.0+

Estimated Execution Time: 30-45 minutes (5 queries + analysis)

Output Document Length: 20-30 pages (final report)

Required Skills: Google Ads, GTM, GA4, SQL basics

Accuracy Level: 95%+ (based on Lemonado data)

FOR FUTURE ITERATIONS:

This template can be enhanced with:

• Automated contamination scoring calculations

• Pre-built Looker/Data Studio dashboard templates

• GA4 BigQuery export integration

• Scheduled report generation

• ML-based anomaly detection for new contamination sources

• END OF UNIVERSAL TEMPLATE