What Is Meta Conversion Attribution? Why Meta and Google Analytics Differ

Conversion attribution determines which advertising interactions are associated with an outcome such as a purchase or inquiry. A person may see an ad, search later, visit on another device and buy. Meta and Google Analytics see different parts of this journey and may apply different rules. Reports failing to match exactly does not automatically indicate a setup error. However, treating every difference as normal can also hide genuine measurement problems.

Separate the event, window and model

The event is the measured behavior, such as a purchase sent after successful payment. The attribution window determines how long before the outcome an eligible advertising interaction can occur. The attribution model determines how credit is allocated. The same purchase can be assigned differently under different windows or models. Ask “What is counted?”, then “Which interactions are eligible?” and finally “How is credit allocated?” Use the options in your campaign settings instead of assuming one window applies to every account.

Read Meta's current interaction definitions

In a March 2026 announcement, Meta said click-through attribution for website and in-store conversions would be limited to link clicks. It also announced an engage-through distinction for other social interactions. Check metric descriptions in your own account because rollout and interface differences can occur. Examine outcomes associated with views, link clicks and other interactions separately using available comparison options. Do not automatically carry an older report's definition of “click” into a newer period; record the change date in reporting notes.

Google Analytics may ask a different question

GA4 user acquisition, traffic acquisition and advertising attribution reports do not all have the same scope. One may show session source while another shows how credit for a key event is allocated. Data-driven and last-click approaches can also produce different results. Check GA4's selected reporting model and lookback window. Do not assume that GA4 observes impressions inside Meta in the same way Meta does. Similar column names are insufficient grounds for comparison.

A simple customer journey example

Suppose someone clicks a link in a Meta ad on Monday, returns through an email on Wednesday and purchases. If the selected Meta window permits it, the sale may be associated with the ad. A selected last-click rule in GA4 may credit email, while a data-driven model may distribute credit differently. The business still has one order. Adding the platforms' attributed sales and counting two sales is a mistake. This example illustrates a possible rule difference, not a guarantee of how every account reports.

Check the technical setup backwards from the order

First verify that the actual order exists. Check that purchase events fire only after successful payment and that value and currency are correct. Investigate repeated events on refresh and browser/server double counting. In GA4, review transaction identifiers, event definitions and key event settings. Use consistent UTM parameters and test that redirects preserve them. Consent choices, browser restrictions and device changes can affect visibility. Do not use methods designed to bypass measurement permissions.

Build a reconciliation table

In weekly reporting, put net orders and revenue from the order system beside Meta's attributed outcomes and the selected GA4 report. Define each column. Use matching currency, time zone and dates, and allow for conversion delays. Also check whether dates refer to interactions or conversions. A narrower click-focused comparison may explain some differences without eliminating them all. The aim is to make the measurement behind a decision understandable rather than force numbers to match.

When should you investigate further?

A sudden increase in a previously stable difference, purchases appearing without real orders or values jumping after a currency change require technical investigation. Compare new payment infrastructure, tag edits, consent-management updates and redirects with change logs. Attribution does not prove causality; appropriate controlled experiments can assess additional advertising impact. Combining order data, customer quality and platform reports provides a stronger basis for channel budget decisions.

Save a copy of the reconciliation file when a period closes. The difference between initial and later-updated results can reveal how conversion delays affect decisions. Avoiding an attribution-model change on the same day as a new integration makes differences easier to investigate. If simultaneous changes are necessary, record each date and expected effect. This helps distinguish sales growth from reporting changes in the following week.

Final checklist

• Are event and revenue definitions comparable across reports?
• Have model, window and interaction types been recorded?
• Have dates, time zones and conversion delays been checked?
• Are actual orders, unique events and currencies verified?
• Have UTM and consent flows been tested?
• Are platform results being added in a way that double counts sales?