Advertising and attribution

[GA4] Attribution paths report

Understand your customers’ paths to triggering key events, and how different attribution models distribute credit on those paths

Use the Attribution paths report (formerly called "Conversion paths") to understand your customers’ paths to triggering key events, and how different attribution models distribute credit on those paths.

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In this article:

Overview

The Attribution paths report has two sections: the data visualization and the data table. The data visualization helps you quickly see which channels initiate, assist, and close key events. The data table shows you the paths users take to complete key events, as well as the following metrics: Key events, Purchase revenue, Days to key events, and Touchpoints to key event.

How to use the report

Access the report

  1. In Google Analytics, click Advertising on the left.
  2. Go to Attribution > Attribution paths.

Select date range and key events

Start by selecting a date range from the date picker drop-down menu at the top right. Then select one or more key events from the drop-down menu at the top left of the report. By default, all key events are selected, and they're aggregated together in the report.

Note: The Attribution reports include data from June 14, 2021 onwards.

Add a filter (optional)

The report shows data from all users. To see data for a specific set of users, click Add filter at the top left.

Example
If you only want to see data for Chrome users, you'd add an Include filter (the default), select the Browser dimension (under Device), choose Chrome as the dimension value, and click OK.

Select dimension to report on

Both the chart and the data table show your data broken out by the Default channel group dimension. Use the drop-downs at the top of the chart and data table to view data by the Source, Medium, or Campaign dimensions.

Select the attribution model for the data visualization

Note: The first click, linear, time decay, and position-based attribution models are no longer available as of November 2023. Learn more About the deprecated models.

The data visualization at the top of the report displays data as calculated by the Paid and organic channels last click attribution model. Use the attribution model drop-down to view the data visualization using a different attribution model.

Customize report

Click Edit comparisons in the top right to change what's displayed in the report. In the Customize report panel, you can change the Filters and Path length settings.

Filters

The report shows data from all users. Click the filter name to edit or add filters.

Path length

The report displays all paths up to 20 touchpoints long. Use this section to filter the path length:

  1. Select an operator (equal to, not equal to, greater than, greater than or equal to, less than, or less than or equal to)
  2. Enter a numeral next to touchpoint(s).
  3. Click Apply.

Share, download, or export report

Click Share this report in the top right to share, download, or export the data currently shown in the report.

Understand the data

Data visualization

The top of the data visualization shows how much credit each segment of the attribution path receives using the selected attribution model. The segments are defined as follows:

  • Early touchpoints: The first 25% of touchpoints in the path rounded to the nearest whole number. This segment is empty if the path has only one touchpoint.
  • Mid touchpoints: The middle 50% of touchpoints in the path. If the path has <3 touchpoints, this segment is empty.
  • Late touchpoints: The last 25% of touchpoints in the path rounded to the nearest whole number. If the path consists of just one touchpoint, this segment gets all the credit.

The bar charts under each segment show how much credit a particular dimension (Channel, Source, Medium, Campaign) receives while in that part of the attribution path.

Hover over the data visualization elements for more specific information.

Fractional credit

Based on your selected attribution model, you'll notice changes to the following metrics when used with event-scoped traffic dimensions: Key events, Total revenue, Purchase revenue, Touchpoints to key event, and Total ad revenue.

When you switch to a non-last click attribution model, you may notice decimals or "fractional credit" for the first time in these columns. This is because credit for a given key event is distributed between contributing ad interactions according to your selected attribution model.

Example

You select the Linear model. A user follows the path keyword1 > keyword2 and then converts. In this case, each keyword will display 0.5 in the Key events column from that key event.

Data table

The top row of the data table provides an overview of how the selected key event types are performing. It shows the total Key events, Purchase revenue, Days to key event, and Touchpoints to key event.

By default, the report is sorted by paths with the highest number of conversions. Click the down arrow next to one of the other metrics to sort by that metric.

The credit displayed in each row is an average across the individual paths included in that row.

Hover over individual touchpoints on paths to see the credit and number of key events.

Unattributable dimensions

In some cases, Analytics is unable to display dimension values because they're missing or otherwise unavailable. To keep the metric totals accurate, the report might show one or more of the following values:

Value Definition
(not set) (not set) is a placeholder name that Analytics uses when it hasn't received any information for the dimension you have selected. For example, manually tagged URLs could be missing a parameter like campaign, source, or medium
Unassigned

Unassigned is the value Analytics uses when there are no other channel rules that match the event data

Direct

Direct is the value that Analytics uses when there is no path data for which to credit the key event; for example, data import

Unattributable Unable to assign credit for the dimension you've selected
(Other) (Other) is the value Analytics uses for an aggregated row due to cardinality limits. Learn more

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