This page will help you understand how the different modules of the Funnel Analysis chart work, and how you should interpret the data it contains.

The following is the list of modules that you will see inside the funnel template:

Let's see how each module is configured in the template.

The Measure module in the funnel template contains a selector that lets you choose a funnel measure to analyze based on the selected event pattern and actor segments.

Running a Funnel analysis helps you measure the following:

The Of selector enables you to select any dataset for actors. You can also use blocks in this section.

This section lets you customize the execution of the query sequence. The following options are available:

This toggle helps analyze out-of-order funnels. Enabled lets users complete the defined funnel stages in any sequence. For example, if a funnel has three stages and a user completes stage three, followed by stage one, the user is counted as converted for stage one. They are converted through stage three if they complete stages two, one, and three. To be counted as converted up to a specific stage, all prior events must be completed, regardless of the order in which they occur.

This option lets you hold a property constant, such as session_id, across analyzed events, ensuring they occur within a single session. It is especially useful for analyzing sequential events and provides a realistic view of user behavior and trends over time.

This option is designed to enhance the accuracy of funnel trends over time charts. By default, a funnel analysis counts each user only once, based on their earliest event within the specified time range. This can lead to a misleading decline in trends over time, particularly when events naturally follow one another.

To address this, the Holding Property Constant feature lets you add a time bucket to the analysis. By doing so, you can count each user once per time period (e.g., per month), rather than just once overall. This adjustment provides a more accurate representation of trends by flattening out the curve, offering insights that align more closely with event segmentation charts.

This option accounts for slight out-of-order event arrivals and is useful when event timestamps are a few seconds off due to factors like buffering delays or clock skew across devices.

Analytics also allows users to choose JSON columns in this module. When you click on a JSON column, it expands to display all the keys that are available for that particular column. You can choose a key and click Apply. Once this is done, the end key that is selected will be chosen as the display name for that column.

The Segmentation module in the Funnel template enables you to choose a collection of actors, such as users, or one or more attributes to include in the analysis. It has two sub-sections: Performed by and Grouped by, which allow users to add cohorts and attributes, respectively. Analytics users can choose to create a collection either by choosing an existing collection from the drop-down list or by using the + New Cohort option to create a behavioural cohort block in one click.

The Visualization module in the Funnel template enables you to run and view the analysis in a chart. It also allows you to edit the visualization, examine the underlying SQL query, and add the chart to a dashboard.

There are 4 important sections in this module:

This feature allows users to switch the values on the Y-axis from absolute values to percentage and vice versa in one click. This feature is particularly helpful while dealing with multiple segments, as using absolute values allows users to see a clearer breakdown of how many individuals are in each segment. It is currently available for the default measure in the funnel template - Converted to each stage.

To choose the number of top segments you want to display in your funnel analysis, you can click the Show top N drop-down and enter the required number. You can also use the + and - symbols to enter your input. The 'top' attribute values are defined by total count of selected events. You can also set the number of Top 'N' segments when you create beelines.

At the top of the Visualization window, users can configure the time range and time grain for the analysis. Analytics users can now set the time range using a simple drop-down list or even choose from the quick options and quickly iterate through different choices of time range and time grain without leaving the chart. It is also possible to set a lag by clicking Offset and setting the Ending.

In the visualization module, you can configure sampling modes for your exploration by clicking on the lightning icon on top of the exploration. The drop-down list displays two sampling modes – Enabled Faster Response or Enabled Higher Precision.

Filters can be used to narrow down data in a visualization. Filters make it easier for the user to answer exploratory questions, for example if the user wants to see results for a specific region, they can define a region filter and see the narrowed down data.

Analytics also allows users to choose JSON columns in this module. When you click on a JSON column, it expands to display all the keys that are available for that particular column. You can choose a key and click Apply. Once this is done, the end key that is selected will be chosen as the display name for that column.