conversion-debug — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited conversion-debug (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Debug conversion tracking across GTM, GA4, Google Ads, and Meta Pixel. Diagnose discrepancies, find duplicate conversions, validate enhanced conversions, audit Consent Mode v2, and run BigQuery validation queries.
Full docs: https://cogny.com/docs/conversion-tracking-debugger
/conversion-debug # Full debugging overview
/conversion-debug duplicate conversions # Diagnose duplicate purchases
/conversion-debug consent mode # Consent Mode v2 audit
/conversion-debug meta vs ga4 # Cross-platform discrepancy analysis
/conversion-debug enhanced conversions # Validate enhanced conversion setup
/conversion-debug server-side tagging # Server-side GTM overview
/conversion-debug value wrong # Debug incorrect conversion valuesYou are a conversion tracking debugging expert. Use this reference and the available MCP tools to help users diagnose and fix conversion tracking issues across GTM, GA4, Google Ads, and Meta Pixel.
When the user asks a question, find the relevant section below and provide precise, actionable answers. When MCP tools are available, use them to pull live data from the user's accounts to validate findings.
If the user provides a specific topic as an argument, focus on that area. Otherwise, ask what platform or issue they need help with.
When MCP tools are available, follow this diagnostic approach:
Understanding data flow is the first step to debugging:
User Action (click, form submit, purchase)
|
v
+-------------------+
| Website / App |
| (dataLayer) |
+-------------------+
|
v
+-------------------+ +-------------------+
| Google Tag | ----> | GA4 |
| Manager (GTM) | | (Measurement |
| | | Protocol) |
| - GA4 Config Tag | +-------------------+
| - GA4 Event Tag | |
| - Google Ads Tag | v
| - Meta Pixel Tag | +-------------------+
+-------------------+ | BigQuery Export |
| | (daily/intraday) |
| +-------------------+
|
+----------> +-------------------+
| | Google Ads |
| | (Conversion |
| | Tracking) |
| +-------------------+
|
+----------> +-------------------+
| Meta Pixel |
| + Conversions |
| API (CAPI) |
+-------------------+Key data flow points:
window.dataLayerGA4 and Google Ads will almost never match. This is expected.
| Factor | GA4 | Google Ads |
|---|---|---|
| Attribution model | Data-driven (cross-channel) | Data-driven (Google channels only) |
| Counting method | Configurable: once per session or once per event | Configurable: one or every conversion |
| Conversion window | Default 30 days | Default 30 days (up to 90 days) |
| Cross-device | Google Signals + User-ID | Google account sign-in |
| View-through | Not counted by default | Included for Display/Video (1-day default) |
| Data freshness | 24-72h processing lag | Conversions appear within hours |
| Modeled conversions | Behavioral modeling for consent gaps | Conversion modeling for unobserved |
Typical variance: 5-20% is normal. Over 20% warrants investigation.
MCP diagnostic approach:
1. GA4: tool_list_conversion_events → check which events are marked as conversions
2. GA4: tool_run_report → pull conversion counts by date
3. Google Ads: tool_execute_gaql → query conversion_action metrics
4. Compare the numbers and identify the gap source| Factor | Meta | GA4 |
|---|---|---|
| Click-through window | 7 days (default) | 30 days (default) |
| View-through | 1-day included by default | Not counted by default |
| Attribution | Last-touch within Meta | Data-driven cross-channel |
| Deduplication | eventID for browser+CAPI dedup | transaction_id parameter |
| Cross-device | Facebook login graph | Google Signals + User-ID |
Why Meta often reports higher: View-through conversions. User sees Meta ad, doesn't click, later converts via Google search. Meta counts it; GA4 attributes to organic/CPC.
google-analytics.com requestsG-XXXXXXX in the tag configThe most expensive tracking bug. Common causes:
eventIDPrevention:
// dataLayer push with transaction_id for deduplication
window.dataLayer.push({
event: 'purchase',
ecommerce: {
transaction_id: 'T-12345', // REQUIRED for dedup
value: 99.99,
currency: 'USD',
items: [/* ... */]
}
});// Prevent duplicate pushes on page reload
if (!window.sessionStorage.getItem('purchase_tracked_T-12345')) {
window.dataLayer.push({
event: 'purchase',
ecommerce: { transaction_id: 'T-12345', value: 99.99, currency: 'USD' }
});
window.sessionStorage.setItem('purchase_tracked_T-12345', 'true');
}Preview Mode Inspection:
Trigger Conditions:
dataLayer.push({ event: '...' }) exactly (case-sensitive)?Variable Values (in Preview mode):
transaction_id populated and unique?value and currency present and correct types?dataLayer Inspection:
// View full dataLayer
console.table(window.dataLayer);
// Filter for purchase events
window.dataLayer.filter(e => e.event === 'purchase');
// Check ecommerce data
window.dataLayer.filter(e => e.event === 'purchase').map(e => e.ecommerce);Network Request Verification (DevTools > Network):
google-analytics.com/g/collect -- GA4googleads.g.doubleclick.net/pagead/conversion -- Google Adsfacebook.com/tr -- Meta PixelMCP diagnostic:
tool_list_tags → see all tags in the container
tool_search_tags → find conversion-related tags
tool_get_tag → inspect specific tag configuration
tool_list_triggers → verify trigger setup
tool_get_workspace_status → check for unpublished changesDebugView:
// Enable debug mode
gtag('config', 'G-XXXXXXX', { debug_mode: true });Then GA4 > Admin > DebugView: verify events, parameters, user properties.
Realtime Reports:
MCP diagnostic:
tool_list_conversion_events → verify which events are marked as conversions
tool_run_realtime_report → check real-time event data
tool_run_report → pull historical conversion data by date
tool_list_data_streams → verify stream configurationBigQuery Validation:
-- Check conversion events in BigQuery
SELECT
event_name,
COUNT(*) as event_count,
COUNT(DISTINCT user_pseudo_id) as unique_users,
MIN(TIMESTAMP_MICROS(event_timestamp)) as earliest,
MAX(TIMESTAMP_MICROS(event_timestamp)) as latest
FROM `project.analytics_PROPERTY_ID.events_*`
WHERE _TABLE_SUFFIX BETWEEN
FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 3 DAY))
AND FORMAT_DATE('%Y%m%d', CURRENT_DATE())
AND event_name IN ('purchase', 'generate_lead', 'sign_up', 'add_to_cart', 'begin_checkout')
GROUP BY event_name
ORDER BY event_count DESCConversion Action Status:
MCP diagnostic:
tool_execute_gaql →
SELECT
conversion_action.name,
conversion_action.status,
conversion_action.type,
conversion_action.tag_snippets,
metrics.conversions,
metrics.conversions_value
FROM conversion_action
WHERE segments.date DURING LAST_7_DAYS
tool_get_gaql_doc → look up conversion_action fieldsGoogle Tag Verification:
// Check gtag is loaded
typeof gtag === 'function' // Should be true
// Check conversion linker cookie
document.cookie.split(';').filter(c => c.includes('_gcl'))Events Manager Test Events:
CAPI Deduplication: Both browser Pixel and CAPI events must share the same eventID:
// Browser-side
fbq('track', 'Purchase', {
value: 99.99,
currency: 'USD',
content_ids: ['SKU-123'],
content_type: 'product'
}, { eventID: 'purchase_T-12345' });# Server-side CAPI
curl -X POST \
"https://graph.facebook.com/v18.0/PIXEL_ID/events?access_token=TOKEN" \
-H "Content-Type: application/json" \
-d '{
"data": [{
"event_name": "Purchase",
"event_time": 1700000000,
"event_id": "purchase_T-12345",
"action_source": "website",
"user_data": {
"em": ["HASHED_EMAIL"],
"client_ip_address": "1.2.3.4",
"client_user_agent": "Mozilla/5.0..."
},
"custom_data": {
"value": 99.99,
"currency": "USD",
"content_ids": ["SKU-123"]
}
}]
}'MCP diagnostic:
tool_get_pixels → verify pixel configuration
tool_get_insights → pull conversion data for comparison with GA4/Google AdsSELECT
PARSE_DATE('%Y%m%d', event_date) as date,
event_name,
COUNT(*) as total_events,
COUNT(DISTINCT user_pseudo_id) as unique_users,
COUNT(DISTINCT
CONCAT(user_pseudo_id, '-',
(SELECT value.int_value FROM UNNEST(event_params) WHERE key = 'ga_session_id'))
) as unique_sessions
FROM `project.analytics_PROPERTY_ID.events_*`
WHERE _TABLE_SUFFIX BETWEEN
FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY))
AND FORMAT_DATE('%Y%m%d', CURRENT_DATE() - 1)
AND event_name IN ('purchase', 'generate_lead', 'sign_up')
GROUP BY 1, 2
ORDER BY 1 DESC, 2Small differences (1-5%) between BigQuery and GA4 UI are normal due to thresholding, sampling, and modeled conversions (present in UI, absent in BigQuery).
WITH purchase_events AS (
SELECT
PARSE_DATE('%Y%m%d', event_date) as date,
user_pseudo_id,
(SELECT value.string_value FROM UNNEST(event_params)
WHERE key = 'transaction_id') as transaction_id,
TIMESTAMP_MICROS(event_timestamp) as event_time,
ecommerce.purchase_revenue_in_usd as revenue
FROM `project.analytics_PROPERTY_ID.events_*`
WHERE _TABLE_SUFFIX BETWEEN
FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY))
AND FORMAT_DATE('%Y%m%d', CURRENT_DATE() - 1)
AND event_name = 'purchase'
)
SELECT
transaction_id,
COUNT(*) as occurrence_count,
COUNT(DISTINCT user_pseudo_id) as distinct_users,
MIN(event_time) as first_occurrence,
MAX(event_time) as last_occurrence,
TIMESTAMP_DIFF(MAX(event_time), MIN(event_time), SECOND) as seconds_between,
SUM(revenue) as total_revenue_recorded,
MAX(revenue) as actual_revenue
FROM purchase_events
WHERE transaction_id IS NOT NULL
GROUP BY transaction_id
HAVING COUNT(*) > 1
ORDER BY occurrence_count DESC-- Find purchases with NULL transaction_id (cannot be deduplicated)
SELECT
PARSE_DATE('%Y%m%d', event_date) as date,
COUNT(*) as purchases_without_txn_id,
SUM(ecommerce.purchase_revenue_in_usd) as revenue_at_risk
FROM `project.analytics_PROPERTY_ID.events_*`
WHERE _TABLE_SUFFIX BETWEEN
FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY))
AND FORMAT_DATE('%Y%m%d', CURRENT_DATE() - 1)
AND event_name = 'purchase'
AND (SELECT value.string_value FROM UNNEST(event_params) WHERE key = 'transaction_id') IS NULL
GROUP BY 1
ORDER BY 1 DESCSELECT
event_name,
param.key,
COUNT(*) as events_with_param,
COUNT(DISTINCT user_pseudo_id) as users_with_param,
COUNTIF(param.value.string_value IS NOT NULL) as has_string_value,
COUNTIF(param.value.int_value IS NOT NULL) as has_int_value,
COUNTIF(param.value.float_value IS NOT NULL) as has_float_value
FROM `project.analytics_PROPERTY_ID.events_*`,
UNNEST(event_params) as param
WHERE _TABLE_SUFFIX BETWEEN
FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY))
AND FORMAT_DATE('%Y%m%d', CURRENT_DATE() - 1)
AND event_name IN ('purchase', 'generate_lead', 'sign_up', 'begin_checkout')
GROUP BY 1, 2
ORDER BY 1, events_with_param DESC-- Check required ecommerce fields on purchase events
SELECT
PARSE_DATE('%Y%m%d', event_date) as date,
COUNT(*) as total_purchases,
COUNTIF(ecommerce.transaction_id IS NOT NULL) as has_transaction_id,
COUNTIF(ecommerce.purchase_revenue_in_usd > 0) as has_revenue,
COUNTIF(ecommerce.total_item_quantity > 0) as has_quantity,
COUNTIF(ARRAY_LENGTH(items) > 0) as has_items,
ROUND(SAFE_DIVIDE(
COUNTIF(
ecommerce.transaction_id IS NOT NULL
AND ecommerce.purchase_revenue_in_usd > 0
AND ARRAY_LENGTH(items) > 0
), COUNT(*)
) * 100, 1) as pct_fully_complete
FROM `project.analytics_PROPERTY_ID.events_*`
WHERE _TABLE_SUFFIX BETWEEN
FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY))
AND FORMAT_DATE('%Y%m%d', CURRENT_DATE() - 1)
AND event_name = 'purchase'
GROUP BY 1
ORDER BY 1 DESCSELECT
PARSE_DATE('%Y%m%d', event_date) as date,
event_name,
COUNT(*) as total_events,
COUNTIF(user_id IS NOT NULL) as has_user_id,
COUNTIF(
(SELECT value.string_value FROM UNNEST(user_properties) WHERE key = 'email') IS NOT NULL
OR (SELECT value.string_value FROM UNNEST(event_params) WHERE key = 'user_data_email') IS NOT NULL
) as has_email_signal,
COUNTIF(
(SELECT value.string_value FROM UNNEST(user_properties) WHERE key = 'phone') IS NOT NULL
OR (SELECT value.string_value FROM UNNEST(event_params) WHERE key = 'user_data_phone') IS NOT NULL
) as has_phone_signal
FROM `project.analytics_PROPERTY_ID.events_*`
WHERE _TABLE_SUFFIX BETWEEN
FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY))
AND FORMAT_DATE('%Y%m%d', CURRENT_DATE() - 1)
AND event_name IN ('purchase', 'generate_lead', 'sign_up')
GROUP BY 1, 2
ORDER BY 1 DESC, 2WITH expected_events AS (
SELECT event_name FROM UNNEST([
'purchase', 'add_to_cart', 'begin_checkout', 'view_item',
'generate_lead', 'sign_up', 'add_payment_info', 'add_shipping_info'
]) as event_name
),
actual_events AS (
SELECT
event_name,
COUNT(*) as event_count,
MAX(TIMESTAMP_MICROS(event_timestamp)) as last_seen
FROM `project.analytics_PROPERTY_ID.events_*`
WHERE _TABLE_SUFFIX BETWEEN
FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY))
AND FORMAT_DATE('%Y%m%d', CURRENT_DATE() - 1)
GROUP BY event_name
)
SELECT
e.event_name,
COALESCE(a.event_count, 0) as event_count,
a.last_seen,
CASE
WHEN a.event_count IS NULL THEN 'MISSING - never received'
WHEN a.last_seen < TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 48 HOUR) THEN 'STALE - no events in 48h'
ELSE 'OK'
END as status
FROM expected_events e
LEFT JOIN actual_events a USING(event_name)
ORDER BY
CASE WHEN a.event_count IS NULL THEN 0 WHEN a.last_seen < TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 48 HOUR) THEN 1 ELSE 2 END,
e.event_nameGoogle Consent Mode v2 (required in EEA since March 2024) controls data collection based on user consent.
Required signals:
| Signal | Controls |
|---|---|
analytics_storage | GA4 cookies and full measurement |
ad_storage | Google Ads cookies and conversion measurement |
ad_user_data | Sending user data to Google for advertising |
ad_personalization | Remarketing and personalized advertising |
Behavior:
granted -- Full data collection, cookies set, user-level data sentdenied -- Cookieless pings, Google models conversions (appear in UI, NOT in BigQuery)// Set defaults BEFORE GTM snippet
gtag('consent', 'default', {
analytics_storage: 'denied',
ad_storage: 'denied',
ad_user_data: 'denied',
ad_personalization: 'denied',
wait_for_update: 500
});
// Update on user choice (called by CMP)
gtag('consent', 'update', {
analytics_storage: 'granted',
ad_storage: 'granted',
ad_user_data: 'granted',
ad_personalization: 'granted'
});gtag('consent', 'update', ...) on user choice?wait_for_update set (500ms recommended)?gcs=G100 in Network tab)?SELECT
PARSE_DATE('%Y%m%d', event_date) as date,
COUNTIF(user_pseudo_id IS NOT NULL) as events_with_user_id,
COUNTIF(user_pseudo_id IS NULL) as events_without_user_id,
COUNTIF(
(SELECT value.int_value FROM UNNEST(event_params) WHERE key = 'ga_session_id') IS NOT NULL
) as events_with_session,
ROUND(SAFE_DIVIDE(
COUNTIF((SELECT value.int_value FROM UNNEST(event_params) WHERE key = 'ga_session_id') IS NULL),
COUNT(*)
) * 100, 1) as pct_likely_consent_denied
FROM `project.analytics_PROPERTY_ID.events_*`
WHERE _TABLE_SUFFIX BETWEEN
FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY))
AND FORMAT_DATE('%Y%m%d', CURRENT_DATE() - 1)
GROUP BY 1
ORDER BY 1 DESCHashed first-party customer data sent alongside conversion tags for better matching.
| Field | Format | Required |
|---|---|---|
| SHA-256 hashed, lowercase, trimmed | At least one of: email, phone, or name+address | |
| Phone | SHA-256 hashed, E.164 format | Optional but recommended |
| First/Last name | SHA-256 hashed, lowercase | Optional |
| Address fields | SHA-256 hashed | Optional |
| Country | ISO 3166-1 alpha-2, unhashed | Optional |
window.dataLayer.push({
event: 'purchase',
user_data: {
email: '[email protected]', // Auto-hashed by GTM
phone_number: '+11234567890',
address: {
first_name: 'John',
last_name: 'Doe',
street: '123 Main St',
city: 'New York',
region: 'NY',
postal_code: '10001',
country: 'US'
}
},
ecommerce: {
transaction_id: 'T-12345',
value: 99.99,
currency: 'USD'
}
});gtag('set', 'user_data', {
email: '[email protected]',
phone_number: '+11234567890',
address: {
first_name: 'John', last_name: 'Doe',
street: '123 Main St', city: 'New York',
region: 'NY', postal_code: '10001', country: 'US'
}
});em= (hashed email)?Browser Server Endpoints
GTM Web Container ---> GTM Server Container ---> GA4 / Google Ads / Meta CAPI
(Cloud Run)
gtm.yourdomain.com// GA4 Config tag: set server container URL
gtag('config', 'G-XXXXXXX', {
server_container_url: 'https://gtm.yourdomain.com'
});# DNS: CNAME to Cloud Run service
gtm.yourdomain.com CNAME your-cloud-run-service.run.appTag installed? --NO--> Install tag. Publish GTM container.
|YES
v
Tag fires in GTM Preview? --NO--> Check trigger: event name exact match?
|YES Conditions met? Blocking triggers?
v
Network request in DevTools? --NO--> Consent Mode blocking?
|YES Ad blocker? CSP blocking?
v
Request returning 200/204? --NO--> Check endpoint URL, measurement ID, credentials.
|YES
v
Platform-specific:
- GA4: Wait 24-48h. Check DebugView. Check data filters.
- Google Ads: Wait 24h. Check conversion action status. Is it "Primary"?
- Meta: Check Events Manager > Test Events. Check event match quality.Difference > 20%? --NO--> Normal variance. Document baseline.
|YES
v
Which platform shows MORE?
- Google Ads > GA4: Counting "every"? View-through? Longer window? Modeled?
- GA4 > Google Ads: All channels vs Google-only? Action set to Primary?
- Meta > GA4: View-through included? iOS modeling?
- GA4 > Meta: Ad blockers? No CAPI? Low match quality?
|
v
Run BigQuery validation queries to get ground truth.Value always 0/NULL? --YES--> Check: 'value' in dataLayer? 'currency' present?
|NO value is number not string? Variable mapping?
v
Value consistently wrong? --YES--> Static value override? Currency conversion?
|NO
v
Value doubled? --YES--> Duplicate events. Multiple tags. Double dataLayer push.
|NO
v
Check: tax/shipping included? cents vs dollars? string formatting? locale?window.dataLayer.push({ ecommerce: null }); // Clear previous
window.dataLayer.push({
event: 'purchase',
ecommerce: {
transaction_id: 'T-12345',
value: 99.99,
tax: 8.50,
shipping: 5.99,
currency: 'USD',
items: [{
item_id: 'SKU-001',
item_name: 'Product Name',
item_brand: 'Brand',
item_category: 'Category',
price: 99.99,
quantity: 1
}]
}
});window.dataLayer.push({
event: 'generate_lead',
value: 50.00,
currency: 'USD',
lead_source: 'contact_form'
});// Google Ads conversion
gtag('event', 'conversion', {
send_to: 'AW-XXXXXXXXX/CONVERSION_LABEL',
value: 99.99,
currency: 'USD',
transaction_id: 'T-12345'
});
// GA4 purchase
gtag('event', 'purchase', {
transaction_id: 'T-12345',
value: 99.99,
currency: 'USD',
items: [{ item_id: 'SKU-001', item_name: 'Product', price: 99.99, quantity: 1 }]
});~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.