Understand your Event Segmentation analysis

An Event Segmentation template consists of the following modules:

Contains a selector that lets you choose a measure to calculate for the selected events and segments.

Running an Event Segmentation analysis helps you measure the following:

Inside the Measures module, you can enable period-over-period comparison for each measure by clicking the over time drop-down list.

A metric is a custom measure derived from the same event dataset used in event segmentation, like ProductInteractions. This means the metric aggregates data from the selected event dataset.

For example, consider a metric called the Freemium to Enterprise Ratio, which you can use to calculate the total events by Freemium users, then divide that total by those by Enterprise users. In an event segmentation analysis, this metric shows a typical ratio of four to six freemium events per enterprise event. However, the ratio drops to one to two for Add Data Source events, indicating it is likely an enterprise feature.

Metrics like Count of unique actors, Frequency, Intervals Engaged, Active Actor %, and Average event count per actor require actor-focused data analysis. When you select these options, an additional catalog picker displays to help you choose actor-specific objects. To define Active Actor %, ensure an active event annotation is on your event stream.

You can create and use a custom measure in your analysis if the prebuilt measures in Optimizely Analytics do not meet your requirements. Add a custom formula block and then choose that metric in the Measure module.

For example, you want to create a measure to count the number of users who have purchased on the website, but you only want to see users who have interacted with content.

Contains an event selector that lets you choose one or more events to analyze.

The event picker displays event streams and their event types for analysis. In Analytics, event streams are datasets configured to model event flows. However, not all datasets are event streams. For instance, the Users dataset lists users rather than events.

Hover an event to see its usage stats, including the average events received per day. Use them to judge whether an event still receives data. See Event taxonomy.

Choose a cohort of actors, such as users, or one or more attributes to include in the analysis. This module has two sub-sections: Performed by and Grouped by, which let you add cohorts and attributes. You can create a cohort by choosing an existing cohort from the drop-down list or clicking + New Cohort to create a behavioral cohort block in one click.

You can use filters to narrow down data in a visualization. They make it easier for you to answer exploratory questions. For example, if you want to see results for a specific region, you can define a region filter and see the narrowed-down data.

Analytics also lets you choose JSON columns in this module. When you click a JSON column, it expands to display the keys available for that column. You can choose a key and click Apply. When you complete this, the selected end key becomes the display name for that column.

Edit the visualization, examine the underlying SQL query, and add charts to a dashboard.

There are six visualization options: Line Chart, Bar Chart, Pie Chart, Horizontal Bar Chart, Single Value, and Table. When you choose a Pie chart, you can see the distribution of events by segmentation attribute across the selected time range.

The following features are available in this module:

Switch the values on the Y-axis from absolute values to percentages and vice versa in one click. This feature is useful when you deal with multiple segments, as using absolute values lets you see a clearer breakdown of the number of individuals are in each segment.

Choose the number of top segments you want to display in the analysis. You can click the Top N drop-down list and enter a number. You can use + and – to enter your input. The total count of selected events defines the top attribute values. You can also set the number of Top N segments when you create beelines.

Configure the time range and time grain for the analysis. Time range is the complete period of time during which the system takes events into account for the analysis. For example, the last two years or the time range between two specific dates. Time grain refers to the granularity of analysis, such as Daily (one day), Weekly (seven days), and so on.

You can set the time range using a drop-down list or choose from the quick options and iterate through different choices of time range and time grain without leaving the chart. You can also set a lag by clicking Offset and setting the Ending.

Configure sampling modes for your exploration by clicking lightning in the visualization window. The drop-down list displays two sampling modes, Enabled Faster Response or Enabled Higher Precision.

You can override the default, auto-generated color and display name for individual data series directly from the legend within a chart. This customization functionality ensures that a specific series displays in the same color across multiple charts, making dashboards easier to interpret at a glance. For example, when you break down a single measure by a dimension (for example, device_type), you can customize the color and label for each breakdown value (for example, Mobile, Web, Tablet).

You can customize a series directly from the chart legend, providing an intuitive way to adjust the visualization.