# Marketing attribution models: how conversion credit is assigned

> Compare attribution rules on one customer journey and see what assigned credit can and cannot show.


**A marketing attribution model is a rule or method for assigning credit for a recorded action to the marketing interactions in its reported path.** When a purchase follows several touchpoints, a report might give all credit to the last click, split it using a fixed rule, or apply a data-driven model. The result depends on the chosen method and which interactions the system recorded.

## One journey, different allocations

Consider a fictional CLP 120,000 purchase after three recorded interactions: someone clicked a social ad, returned through organic search, then clicked an email link back to the shop. The report counts one purchase, but an attribution model can describe that path in different ways.

| Illustrative rule | Credit allocation | What it highlights |
| --- | --- | --- |
| First touch | CLP 120,000 to the social ad | The first recorded interaction in the path. |
| Last touch | CLP 120,000 to the email | The last eligible interaction before purchase. |
| Linear | CLP 40,000 to each interaction | An equal split across the three observed touchpoints. |

The linear rule makes the arithmetic easy to see: CLP 120,000 ÷ 3 = CLP 40,000 per touchpoint. It is a conceptual example. GA4 and Google Ads no longer offer first-click or linear attribution; time decay and position-based models have also been retired. An account may still contain historical reports calculated under a model that is no longer available.

## Current attribution options in GA4

Google Analytics currently lists three models in its attribution reports: data-driven attribution, paid and organic last click, and Google paid channels last click. The data-driven model uses information from paths that completed a key event and paths that did not. It learns from those data to assign credit; it is not calculated by dividing an order's value by the number of interactions.

Last-click models follow their own rule: the last eligible touchpoint receives credit under the scope of the selected option. In GA4 attribution reports, direct visits are excluded from credit unless the entire path to the key event is direct. Channel and data availability can vary by report and configuration. Google's [Analytics attribution help](https://support.google.com/analytics/answer/10596866?hl=en) describes those options, while its [Google Ads attribution documentation](https://support.google.com/google-ads/answer/6259715?hl=en) describes the models available there.

## Credit helps interpret a report; it does not prove causation

An interaction can appear before a purchase and receive credit under a model without causing the purchase. The result describes how a rule allocated observed conversions. It also leaves out contacts the system did not record, such as an offline conversation.

Before comparing campaigns, note four decisions: which event counts as a conversion; which channels and interactions are eligible; which time window the report uses; and which model assigned credit. Keep those definitions consistent across periods. If you change the model, record when: the allocation can change even if the number of purchases does not.

UTM tags can help identify the source of links arriving on a site, but they do not determine how credit is split across multiple interactions. The [social media UTM guide](/en/guides/social-media-utm-tracking/) explains how to label and check those links.

## Choose a view that fits the decision

Start with the question behind the report. If you want to know which final recorded interaction appeared before a purchase, a last-click model gives that view under its eligibility rules. If you need to assess a path with several advertising interactions the product can observe, review data-driven attribution and its configuration. Keep the event, period, property and channel scope consistent when comparing figures.

If the decision is what would have happened without a campaign, an attribution report cannot answer it on its own. That requires a measurement design that estimates change against a comparable scenario, such as a controlled experiment when feasible. Attribution and incrementality testing answer different questions.

Use a model to interpret the credit a system reports. State which model you used and which parts of the journey were outside its view; do not present the channel with the most credit as a universal winner.

*The journey figures are fictional and illustrate three allocation rules. GA4 and Google Ads model definitions apply to their products and may change.*

The cover is an AI-generated illustration.

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