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Event Segmentation Build an event segmentation analysis Choose the right measurement Create in-line custom events Interpret your analysis, part 1 Interpret your analysis, part 2: Advanced features Custom formulas: Syntax and definitions

Funnel Analysis Get the most out of Amplitude's Funnel Analysis chart Build a funnel analysis Interpret your funnel analysis How Amplitude computes funnels How Amplitude computes conversions through funnels How Amplitude handles simultaneous events in a funnel How filters work in a Funnel Analysis chart How Amplitude handles segmenting on a user property in a Funnel Analysis chart A/B testing in a Funnel Analysis chart Combine funnel events inline Compare multiple funnel events in a single step Compare group-by values in your Funnel Analysis chart to each other Hold properties constant in a Funnel Analysis chart Identify conversion drivers in your funnel analyses Mark a step in your funnel as optional

Retention Analysis Build a retention analysis Interpret your retention analysis Build a usage interval analysis Interpret your Retention Analysis chart: Usage interval How the Retention Analysis chart calculates retention How time works in a retention analysis

Data Tables Multi-dimensional analysis with Data Tables Results limits and sorting logic in Data Tables charts Create a metric Use session metrics to evaluate content paths Attribute credit to multiple acquisition touch points

Journeys Understand the paths users take, and why they convert Understand and use the Journeys visualizations

Experiment Results Dig deeper into experimentation data with Experiment Results Using formula metrics in Experiment Results

Revenue LTV Interpret your revenue analysis The Revenue LTV chart: Track how well you're monetizing new users

Compass The Compass chart: discover your users' 'a-ha' moments Find your company's inflection metrics with Compass Interpret your Compass chart, part 1 Interpret your Compass chart, part 2: Correlation and cohorts

Stickiness Stickiness: Identify the features that drive users back to your product Interpret your stickiness analysis

Personas The Personas chart: Find your product's user personas Interpret your Personas chart

Impact Analysis Impact Analysis: How engagement changes user behavior Interpret your Impact Analysis chart

User Sessions User Sessions: Track engagement frequency and duration Interpret your User Sessions chart Marketing metrics recipes

Engagement Matrix Engagement Matrix: see how users feel about your product Interpret your Engagement Matrix

Lifecycle Lifecycle: track the growth of your product's user base Interpret your Lifecycle chart

Other Charts Amplitude SQL: Getting started with Query Amplitude SQL: Table schema and field shortcuts User Composition: View your users by common properties

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Funnel Analysis

How filters work in a Funnel Analysis chart

This article helps 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).

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May 29th, 2024

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