This article will help you:
- Understand how Amplitude interprets different filters in a Funnel Analysis chart
There are certain nuances to applying filters in a funnel analysis:
Applying filters in the Segmentation Module
In a funnel chart, any filters applied via the Segmentation Module apply only to the first event. You can, however, add filters to individual steps directly in the Events module.
NOTE: Only users who have triggered an event that meets the conditions of the filters applied to the first event will be counted by Amplitude as entering the funnel.
Applying group-by filters
You can apply a group-by filter in the Segmentation Module, for up to two properties. The group-by filter will apply only to the first event, similar to the other filters in the Segmentation Module.
If you are looking at the Unique Users metric and users can complete the steps of your funnel multiple times, the group-by filter will take the first occurrence of the event and bucket the user for the value on that event.
NOTE: If “holding property constant” is applied at the same time, Amplitude counts each property value / user pair as a separate user, so the user is included once for each property value they have.
You can also use the group-by filter for an event (limit of one event group-by per funnel). The results will show how users with a certain event or user property converted through the other steps in the funnel. This helps you understand what property value potentially has the greatest or smallest impact on conversion.
For example, look at this Funnel Analysis chart:
The Group-by here looks at users' property values for Genre_Type
at the time their Favorite Song or Video
events are triggered, and shows how they converted through the remaining events of the funnel.
For example, a user that has a Pop
property value for Genre_Type
at the time their Favorite Song or Video
event was triggered will show up under the Pop
property bar for the Play Song or Video
event as well.
NOTE: If users in your funnel can complete the steps multiple times, this method will take the first occurrence of each event and bucket the user for the value on that event.
This three-step funnel is grouped by Step 2's event property, item_id
:
The graph shows the conversion distribution of users who triggered Step 2 (Add Item to Cart
) event, broken out by each item_id
value.
If you choose to group by a step other than the first, you will also see a segment of users who did not reach that segmented step (the blue-shaded segment for 'did not reach step' in this example).