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aaron-he-zhu
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attribution-reconciler

Compare and normalize ad platform conversions with GA4 order-ID data.

2.5k|345|Updated Dec 18, 2025
Search Tags:#advertising#paid ads#attribution modeling#conversion reconciliation#incrementality analysis
One-click install
npx skills add https://github.com/aaron-he-zhu/aaron-marketing-skills --skill attribution-reconciler
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: attribution-reconciler
Source: https://github.com/aaron-he-zhu/aaron-marketing-skills/tree/main/ad/scale/attribution-reconciler
Command: npx skills add https://github.com/aaron-he-zhu/aaron-marketing-skills --skill attribution-reconciler

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

The skill solves the issue of discrepancies between platform-reported conversions and those in GA4 or e-commerce, addressing cases of double-counting by Meta and Google, and normalizing attribution windows and currency across platforms.

Core Features & Use Cases

  • Conversions Reconciliation: Compares and matches platform-reported conversions against GA4 or e-commerce order-IDs to prevent double counting.
  • Normalization: Standardizes attribution windows and currencies to ensure accurate comparisons.
  • Incrementality Analysis: Provides insight into incrementality using geo/holdout test data.
  • Use Case: For a monthly reconciliation workbook that deduplicates credit, normalizes attribution windows, compares models, and reads incrementality.

Quick Start

Use the attribution-reconciler skill with the GA4 order-ID export, platform conversion exports, and any holdout test data to de-duplicate and compare conversions across platforms.

Frequently Asked Questions about attribution-reconciler

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I reconcile paid ad conversions with GA4 or e-commerce order-ID data?▼

Conversions reconciliation compares and matches platform-reported conversions against GA4 or e-commerce order-IDs to detect double-counting. This process normalizes attribution windows and currencies to ensure accurate cross-platform comparisons.

Why does Meta or Google report more conversions than my e-commerce backend?▼

Platform-reported conversions often exceed e-commerce backend totals due to double-counting by Meta and Google. Reconciling these metrics against order-IDs prevents double counting and normalizes attribution windows for accurate comparisons.

How do I perform an incrementality analysis using geo and holdout test data?▼

Incrementality analysis provides insight into true media impact by comparing reconciled platform conversions with geo or holdout test data. This identifies the actual lift generated by paid ads rather than simply matching reported metrics.

Can I use attribution modeling to standardize attribution windows across multiple ad platforms?▼

Attribution modeling standardizes attribution windows and currencies across multiple ad platforms to ensure accurate comparisons. This normalization is essential for deduplicating credit and maintaining a reliable monthly reconciliation workbook.

What is the best way to prevent double counting in paid ads and GA4?▼

Preventing double counting in paid ads and GA4 requires comparing platform conversion exports against e-commerce order-IDs. This deduplicates credit, normalizes attribution windows, and ensures only verified conversions are counted.

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