Understand your Event Segmentation analysis

  • Updated

Template structure​

An Event Segmentation template consists of the following modules:

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Measure module​

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

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Running an Event Segmentation analysis helps you measure the following:

  • Count of Events – The count of events during a specified time frame.
  • Uniques – The count of unique users triggering events.
  • Frequency – The distribution of actors by the number of times they performed an event.
  • Average Events – The average number of events performed by an actor.
  • Intervals Engaged – The count of time buckets for each actor, in which that actor met the engaged event criteria.
  • Active % – The count of users who performed any active event.
  • Aggregate by Property – Custom aggregate for actors who did at least one of the events.
  • Metric – A metric from the catalog or a custom block.

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

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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.

Create a custom measure over multiple event streams

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.

  1. Go to + > Event Segmentation.
  2. Choose Make Purchase for event A.
  3. Choose all events related to content interaction, Pause Content, Play Content, Rate Content, and Share Content for event B, and click Apply.
  4. Go to the block shelf, click + > Formula to create a formula block.
  5. Enter the following formula: count(@A) / uniques(@B.user_id), where count(@A) is the number of purchases and uniques(@B.user_id) is the number of unique users who performed any content interaction. Click Apply.
  6. Choose Metric in the measure module and select the formula block as the value.
  7. Run the exploration and click Save.
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Events module​

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.

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When a single dataset is on the platform, the event selector shows only event types, hiding the event streams selector, and providing an event type drop-down list for analysis. It also includes a filter for selecting events based on conditions. You can choose JSON columns, which expand to show available keys. You can select a key and click Apply to set the path of the selected field as the column's display name.

Segmentation module​

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.

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Filters module

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.

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Visualization module

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.

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The following features are available in this module:

Switch between numbers and percentages

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.

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Top 'N' segments

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.

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Time range and time grain section

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.

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Sampling section

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.

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Series-level customization

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).

Edit a series' color and label

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

  1. Go to an event segmentation analysis or a dashboard tile.

  2. Hover over the series you want to customize, then click pencil to edit the series.

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  3. Edit the text in the Label field to change the series display name.
  4. Use the color picker to select a new color for the series. You can use the same color for different series if you want, it does not need to be unique.
  5. (Optional) Click Reset to default to return Label and Color to the system-generated values.
  6. Click Apply to save your changes. The chart and legend display the updated values across the draft exploration, the saved exploration, and any dashboard tile using that configuration.
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Edit visualization section

  • Inspect Query​ – Click this option to open a query inspector. It has three tabs, SQL, Warehouse SQL, and NetScript. You can change the SQL and NetScript queries by clicking the Open as SQL Explore and Open as NetScript Explore buttons in the respective sections.
  • Add to Dashboard​ – Add the exploration you created to a dashboard. You can choose from two options: Pick a dashboard or New Dashboard.
  • Edit visualization – Edit the chart settings. It has two sub-sections: Visualization and Sorting.
    • Under Visualization, you can configure
      • Automatic refresh for your chart.
      • Chart type.
      • X-axis and Y-axis configuration.
      • Tooltip.
      • Size configuration.
    • Under Sorting, you can
      • Enable the sorting option on the graph.
      • Configure sorting rules and the limit.
  • Download As - Download your analysis in either CSV or EXCEL format. The system preserves the column formatting for the downloaded files as well.
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When you use a pie chart along with segmentation over multiple cohorts, the system may double count the results. This is because a given event or actor may be included within multiple cohorts, whereas a pie chart is designed to show breakdown of the whole into multiple, non-overlapping segments.