attribution-reconciler

Cross-Platform Attribution Reconciler

Purpose

Help advertisers understand and reconcile the conversion discrepancies they see between Meta, Google Ads, GA4, TikTok, and LinkedIn. This is the #1 pain point across all advertising platforms - different platforms report different numbers for the same conversions.

When to Use This Skill

Invoke when user mentions:

• Discrepancies: "Why do Meta and GA4 show different numbers?"
• Trust questions: "Which platform's data should I believe?"
• Budget decisions: "How do I allocate budget across channels?"
• Reconciliation: "How do I reconcile attribution across channels?"
• Specific gaps: "What's causing the 30% discrepancy I'm seeing?"

Required Tools

Use these MCP tools to pull live data when diagnosing attribution discrepancies:

Tool	Purpose
ga4_run_report	Pull GA4 Key Events (conversions) as neutral baseline
meta_query	Pull Meta campaign conversions by attribution window
google_ads_run_gaql	Pull Google Ads conversion data for comparison
tiktok_get_report	Pull TikTok conversion data
linkedin_get_analytics	Pull LinkedIn conversion data

Recommended diagnostic sequence:

1. ga4_run_report(property_id="...", start_date="2026-03-08", end_date="2026-04-05", metrics=["keyEvents"], dimensions=["date"])
2. meta_get_insights(account_id="act_...", level="campaign", date_preset="last_28d", fields=["spend","actions","impressions"])
3. google_ads_run_gaql(customer_id="...", query="SELECT campaign.name, metrics.conversions, metrics.cost_micros FROM campaign WHERE segments.date DURING LAST_28_DAYS")

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Quick Reference: Expected Discrepancies

These discrepancy ranges are normal and don't necessarily indicate a problem:

Platform Comparison	Expected Difference	Primary Cause
Meta vs GA4	Meta +15-30% higher	View-through + modeled conversions
Google Ads vs GA4	Google +10-25% higher	Enhanced Conversions + modeling
TikTok vs GA4	TikTok +20-40% higher	VTA attribution (30% of conversions)
LinkedIn vs GA4	LinkedIn +15-35% higher	Long B2B cycles, cross-device
GA4 vs All	GA4 -18-35% lower	Cookie blocking, consent mode

Decision Framework: When to Investigate

                         What's the discrepancy level?
                                    |
        +---------------------------+---------------------------+
        |                           |                           |
        v                           v                           v
     <15%                       15-35%                       >35%
   ─────────                   ─────────                   ─────────
        |                           |                           |
        v                           v                           v
   NORMAL                    EXPECTED                    INVESTIGATE
   No action                 Check attribution           Check implementation
   needed                    windows first               issues

Investigation Checklist (>35% Discrepancy)

Step 1: Technical Implementation

• Is the pixel/tag firing correctly? (Test with browser dev tools)
• Is server-side tracking (CAPI) set up?
• Are event IDs deduplicated properly?
• Are UTM parameters consistent across all ad URLs?
• Is Consent Mode v2 implemented? (Required for EEA since March 2024 — includes ad_user_data and ad_personalization parameters)

Step 2: Attribution Settings

Platform	Where to Check	Default Setting
Meta	Settings > Attribution	7-day click, 1-day view
Google Ads	Tools > Conversions > Settings	Last-click (or DDA)
GA4	Admin > Attribution Settings	Cross-channel DDA (reports Key Events, not "Conversions")
TikTok	Assets > Events > Attribution	7-day click, 1-day view
LinkedIn	Account Settings > Attribution	30-day click, 7-day view

Step 3: Common Technical Issues

Issue	Symptom	Fix
Broken pixel	GA4 shows 0, platform shows conversions	Reinstall/verify pixel
Missing UTM	Traffic shows as "direct" in GA4	Add UTM parameters
Event mismatch	Different event names across platforms	Standardize naming
Time zone	Conversions on different days	Align time zones
Currency	Revenue doesn't match	Use same currency code

Which Number to Use: Decision Framework

                    What's your use case?
                            |
    +---------------+-------+-------+---------------+---------------+
    |               |               |               |               |
    v               v               v               v               v
OPTIMIZING      CROSS-CHANNEL    REPORTING      STRATEGIC       TRUE
SINGLE          BUDGET           TO             PLANNING        INCREMENTAL
PLATFORM        ALLOCATION       STAKEHOLDERS                   VALUE
    |               |               |               |               |
    v               v               v               v               v
Use             Use unified      Use GA4 as     Use MMM         Run
platform's      BI dashboard     source of      outputs         incrementality
own data        or GA4           truth                          tests

Detailed Guidance by Use Case

1. Optimizing a Single Platform

Use: Platform's own data

The platform's algorithm optimizes based on its own signals. Optimizing Meta campaigns based on GA4 data means fighting the algorithm.

Example: Meta reports 100 conversions, GA4 shows 70. Optimize within Meta Ads Manager using Meta's 100.

2. Cross-Channel Budget Allocation

Use: Unified BI or GA4 Key Events (with caveats)

Need apples-to-apples comparison. GA4 uses consistent attribution across channels.

Terminology note: GA4 renamed "Conversions" to Key Events in 2024. In GA4 reports, look for "Key events" (formerly conversions). Google Ads still shows "Conversions" — those are imported from GA4 Key Events or set up via Google Ads conversion tracking. Don't confuse the two.

Caveats:

• GA4 underreports by 18-35%
• Add modeling factor: multiply GA4 by 1.2-1.4
• Or use MMM for strategic allocation

3. Reporting to Stakeholders

Use: GA4 Key Events as single source of truth + context

Consistency matters for trust. Explain discrepancies upfront.

Script: "We use GA4 Key Events as our source of truth for cross-channel comparison. Note that GA4 captures ~70-80% of true conversions due to cookie restrictions. Platform dashboards show higher numbers due to view-through attribution and modeling."

4. Strategic Planning

Use: Marketing Mix Modeling (MMM)

MMM accounts for upper-funnel impact, offline conversions, and cross-channel effects.

Best for: Budget >$50K/month, multiple channels, brand + performance mix.

5. Understanding True Incremental Value

Use: Incrementality testing

Only way to know what would be lost if a channel were turned off.

Methods:

• Geo holdouts (recommended)
• Ghost ads
• Pre/post analysis

Attribution Window Comparison

What Each Platform Captures

User Journey: Ad View (Day 0) -> Website Visit (Day 3) -> Purchase (Day 5)

Platform Attribution:
─────────────────────

META (7-day click + 1-day view):
├─ Day 0: View ──────────────────────────────► ✓ Counted (view-through)
├─ Day 3: Click ─────────────────────────────► ✓ Counted (7-day click)
└─ Day 5: Purchase

GOOGLE ADS (Last-click):
├─ Day 0: View ──────────────────────────────► ✗ Not counted
├─ Day 3: Click (if Google was last) ────────► ✓ Counted
└─ Day 5: Purchase

GA4 (Cross-channel DDA):
├─ Day 0: View ──────────────────────────────► Partial credit
├─ Day 3: Click ─────────────────────────────► Partial credit
└─ Day 5: Purchase (only if cookie persists) ─► May undercount

TIKTOK (7-day click + optional VTA):
├─ Day 0: View ──────────────────────────────► ✓ If VTA enabled (+30%)
├─ Day 3: Click ─────────────────────────────► ✓ Counted
└─ Day 5: Purchase

Recommended Window Alignment

Platform	Recommended Setting	Notes
Meta	7-day click, 1-day view	Default is fine
Google Ads	Data-driven attribution	Better than last-click
GA4	Cross-channel DDA	Default is fine
TikTok	7-day click, 1-day view	Match Meta
LinkedIn	30-day click (B2B needs longer)	Don't shorten

Privacy Impact Assessment

iOS 14+ Impact by Platform

Platform	Data Loss	Mitigation	Recovery
Meta	30-50%	CAPI, AEM, broad targeting	~70% with CAPI
Google	15-25%	Enhanced Conversions, Consent Mode	~85% with EC
TikTok	20-35%	Events API, SKAN	~75% with Events API
LinkedIn	10-20%	Insight Tag, CAPI (beta)	~90% with proper setup

Consent Mode Impact

With Consent Mode V2 properly implemented:

• Consented users: Full tracking
• Non-consented users: Modeled conversions (60-80% accuracy)
• Net effect: ~10-15% undercount vs pre-privacy world

🇪🇺 EU/GDPR-Specific Impact (DDMA 2025 Data)

European advertisers face additional signal loss due to GDPR compliance:

Metric	EU	US	Delta	Impact
Cookie consent opt-in	~50%	~78% (Meta)	-28pp	Smaller addressable audiences
CTR impact	-2.1%	Baseline	-2.1pp	Lower campaign efficiency
Conversion rate	-5.4%	Baseline	-5.4pp	Higher CPAs
Revenue per click	-5.7%	Baseline	-5.7pp	Reduced ROAS
Acquisition costs	+25%	Baseline	+25%	SMB impact especially

EU Consent Rates by Country

Country	Consent Rate	Implication
Germany	62%	Best EU performance
Netherlands	~55%	Above EU average
France	38%	Lowest major market
EU Average	~50%	vs 32% US opt-in rate

Critical Insight: EU advertisers should expect:

• +20-35% higher attribution discrepancies vs US benchmarks
• Meta underreports by additional 10-15% in low-consent markets (France)
• GA4 modeling less accurate (60-70% vs 80% in US)

Privacy Signal Loss Compensation Recommendations

For EU Advertisers (High Privacy Impact)

Tier 1: Essential (Do First)

1. Implement Consent Mode V2

   • Recovers 60-80% of lost signals via Google modeling
   • Required for Google Ads in EEA since March 2024

2. Deploy Server-Side Tracking (CAPI)

   • Meta CAPI: Recovers ~70% of ATT signal loss
   • Google Enhanced Conversions: Recovers ~85%
   • TikTok Events API: Recovers ~75%

3. Configure Event ID Deduplication

   • Prevents double-counting between browser + server

Tier 2: Advanced (Strong Signal Recovery)

1. First-Party Data Matching

   • Meta Advanced Matching (email, phone hashes)
   • Google Customer Match
   • LinkedIn Matched Audiences

2. Zero-Party Data Collection

   • Progressive profiling in forms
   • Quiz/survey data for segmentation

3. Probabilistic Modeling

   • Meta Modeled Conversions
   • Google Enhanced Attribution

Tier 3: Strategic (Long-Term)

1. Marketing Mix Modeling (MMM)

   • Accounts for untracked conversions
   • Best for budgets >€50K/month

2. Incrementality Testing

   • Geo holdouts quarterly
   • Ghost ads for validation

3. CDP Implementation

   • Unified customer view
   • Privacy-compliant audience building

EU Attribution Adjustment Factors

Apply these multipliers when comparing EU platforms to GA4:

Platform	Standard Adjustment	EU Adjustment (Low Consent)
Meta	+15-30%	+25-40%
Google Ads	+10-25%	+15-30%
TikTok	+20-40%	+30-50%
LinkedIn	+15-35%	+20-45%

Example: If Meta reports 100 conversions and GA4 reports 60 in Germany (62% consent), the 67% difference is likely within normal range for EU.

Validation Approaches

Method 1: Geo Holdout Test (Recommended)

Setup:
1. Select 2-4 similar geographic regions
2. Turn off ads in half (control)
3. Run normally in others (test)
4. Measure conversion difference

Analysis:
- Incremental lift = Test conversions - Control conversions
- True ROAS = Incremental revenue / Ad spend

Timeline: 4-6 weeks minimum
Budget: ~10% of total to holdout

Method 2: Cross-Reference with Source of Truth

Compare platform conversions to:
- CRM closed deals (ultimate truth)
- E-commerce platform orders (Shopify, etc.)
- Payment processor (Stripe, PayPal)

Calculate platform accuracy:
Platform Accuracy = Platform Reported / Verified Conversions

Method 3: Triangulation

Example:
- Meta: 100 conversions
- Google: 80 conversions
- GA4: 60 conversions
- CRM: 85 actual deals

Then:
- Meta overcounts by ~18%
- Google undercounts by ~6%
- GA4 undercounts by ~29%

Apply these factors to future reporting.

Practical Recommendations by Budget

For Small Advertisers ($10K-50K/month)

1. Accept discrepancies exist - don't chase perfect attribution
2. Use GA4 for unified reporting - explain undercount to stakeholders
3. Optimize within each platform - use platform data for platform optimization
4. Run simple holdout test - turn off one channel for 2 weeks quarterly

For Mid-Market ($50K-200K/month)

1. Implement server-side tracking - CAPI, Enhanced Conversions
2. Build unified dashboard - BigQuery/Looker with blended data
3. Apply platform accuracy factors - adjust based on CRM validation
4. Consider lightweight MMM - tools like Adinton, Marketing Evolution

For Enterprise ($200K+/month)

1. Full MMM implementation - Meridian, Marketing Evolution, custom
2. Regular incrementality testing - quarterly geo holdouts
3. Multi-touch attribution - Northbeam, Triple Whale, Ruler
4. Privacy-first infrastructure - CDP, server-side, first-party data

Online-to-Offline (O2O) Attribution

For retail, hospitality, and service businesses with physical locations, online ads often drive offline conversions. This is a critical blind spot in digital attribution.

O2O Attribution Methods by Platform

Google Ads Store Visits

Best for: Retailers, restaurants, service providers with physical locations

Requirement	Details
Minimum locations	10+ in most countries
Minimum conversions	~100K ad clicks, ~thousands of store visits/month
Location data	Google My Business linked
Privacy	Based on aggregated, anonymized Location History

How to Enable:

1. Link Google My Business to Google Ads
2. Enable location extensions
3. Reach volume threshold (~30 days)
4. Store Visit conversions auto-populate

Accuracy: ~70-80% (Google claim: 99% confidence)

Meta Offline Conversions

Best for: Any business with CRM/POS data

Method	Setup Complexity	Accuracy
Offline Conversions API	Medium	High (hashed match)
Partner Integrations (Square, Lightspeed)	Low	Medium
Manual Upload	High	Variable

How to Enable:

1. Navigate to Events Manager > Offline Events
2. Create Offline Event Set
3. Upload CRM/POS data with event_time, email/phone hashes, value
4. Match window: 1-28 days (default: 7-day click)

Key Fields for Upload:

event_time, event_name, value, currency,
match_keys: {email, phone, fn, ln, zip, country}

LinkedIn Offline Conversions

Best for: B2B with long sales cycles

Feature	Details
Match rate	Email-based, typically 30-50%
Sales cycle	Supports 90-day lookback
CRM integration	Salesforce, HubSpot, Marketo native

How to Enable:

1. Campaign Manager > Account Assets > Offline Conversions
2. Connect CRM or upload CSV
3. Map fields (email required, company optional)
4. Set attribution window (default: 30-day)

TikTok Offline Events

Status: Limited availability (2025)

Capability	Status
Events API	Available (requires developer setup)
Direct integrations	Shopify, Salesforce (beta)
Store visits	Not available

O2O Attribution Decision Framework

                Do you have physical locations?
                            |
              +-------------+-------------+
              |                           |
             YES                          NO
              |                     Use online-only
    +---------+---------+              attribution
    |                   |
  10+ locations?    <10 locations?
    |                   |
    v                   v
Google Store        Use Offline
Visits eligible     Conversions API
    |               (Meta, LinkedIn)
    v
Enable Store
Visits + Offline
Conversions for
triangulation

O2O Reconciliation: Expected Discrepancies

Comparison	Expected Gap	Reason
Online Conv vs Store Visits	Store +20-50%	Many online-influenced visits untracked
Platform O2O vs POS data	Platform -30-50%	Match rate limitations
Google Store Visits vs Meta O2O	Variable	Different methodologies

Geo Holdout for O2O Validation

The gold standard for O2O attribution validation:

Setup:
1. Select matched metro areas (similar demographics, store density)
2. Split: 50% test (ads on), 50% control (ads off)
3. Run for 4-6 weeks minimum
4. Measure: Store traffic + sales in both groups

Calculation:
Incremental Store Visits = Test Area Visits - Control Area Visits
Incremental Revenue = Test Area Revenue - Control Area Revenue
True O2O ROAS = Incremental Revenue / Ad Spend (Test Area)

Example:
- Test area: 10,000 store visits, €500K revenue, €50K ad spend
- Control area: 7,500 store visits, €375K revenue, €0 ad spend
- Incremental: 2,500 visits, €125K revenue
- True O2O ROAS: €125K / €50K = 2.5x

Retail-Specific Recommendations

Budget Level	O2O Attribution Approach
<€10K/month	Manual POS correlation (pre/post analysis)
€10-50K/month	Meta/LinkedIn Offline Conversions
€50-200K/month	+ Google Store Visits + quarterly geo holdouts
€200K+/month	Full MMM with O2O modeling

Quick Reference Card

When Platforms Disagree

Scenario	Likely Cause	Action
Meta >> GA4	View-through attribution	Normal if <30% gap
Google >> GA4	Enhanced Conversions modeling	Normal if <25% gap
TikTok >> GA4	VTA + in-app browser	Normal if <40% gap
GA4 >> Platform	Unlikely (GA4 usually lower)	Check for tracking issue on platform
All platforms way off	Technical issue	Audit tracking implementation

Rule of Thumb

Trust overlap, not outliers.

If three platforms agree on directional trends (up/down, better/worse), trust that signal even if absolute numbers differ.

Output Template

When diagnosing attribution issues, provide:

## Attribution Diagnosis

### Discrepancy Analysis
- Platform A vs Platform B: X% difference
- Expected range: Y-Z%
- Status: Normal / Investigate
- Region: [EU/US/Global] - affects expected ranges

### 🇪🇺 EU Privacy Context (if applicable)
- Consent rate estimate: [Country-specific %]
- Additional signal loss: [+X% vs US baseline]
- EU-adjusted expected range: [Y-Z%]

### Likely Causes (Ranked)
1. [Most likely cause]
2. [Second most likely]
3. [Third most likely]

### O2O Attribution (if retail/offline)
- Store Visits available: [Yes/No]
- Offline Conversions setup: [Yes/No]
- Estimated O2O contribution: [X% of total conversions]
- Validation: [Geo holdout recommended / POS correlation]

### Recommendations

**Immediate Actions:**
- [Action 1]
- [Action 2]

**Technical Improvements:**
- [Improvement 1]
- [Improvement 2]

**Privacy Signal Recovery (Priority Order):**
1. [Highest impact action - e.g., CAPI if not implemented]
2. [Second priority]
3. [Third priority]

**Which Number to Use:**
- For [use case]: Use [platform]
- For [use case]: Use [platform]
- For O2O: Use [triangulation method]

### Validation Approach
- [Recommended validation method]
- For O2O: [Geo holdout / POS correlation recommended]

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Based on 2025-2026 attribution research across Meta, Google, LinkedIn, TikTok, and GA4. EU benchmarks from DDMA Privacy Monitor 2025, IAB Europe 2026.