Attributes and segmentation

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  • Optimizely Web Experimentation
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How Optimizely Experimentation sets attributes of decision events differs depending on the scenario and what Experimentation product you use. This article considers examples of assigning events in various circumstances.

For information about how event counting works, see How Optimizely Experimentation counts conversions.

Attribution and segmentation example

This example demonstrates how attribution and segmentation work in Optimizely Experimentation.

While you are experimenting on your site's product detail pages, a user visits one of those pages, enters the experiment, is bucketed into a certain variation, and converts on an event you track: Click Add to Cart. The user converts a few times while navigating your site. Unless otherwise noted, assume that the user is shown the same variation in Sessions 1 and 3.

Sessionalization Diagram

  • E = Conversion events (also known as events) – Events that are fired when a visitor converts to a desirable action, such as a click, page view, or purchase.
  • D = Decision events (also known as impressions) – Special events fired when Optimizely Experimentation determines that a visitor is bucketed into a certain experiment or variation pair.
  • Session – A period of activity for a user.

    Optimizely Experimentation does not use sessions to assign data to decisions. Instead, sessions are used for illustrative purposes only. For example, Session 1 could be considered the user's activity on one day. Session 2 is the next day, and so on. 

Count conversions when targeting an audience

Optimizely Experimentation lets you automatically target experiments and personalized experiences to groups of users who share attributes you define (called an audience or are defined by the Web Experimentation snippet). This section explains how Optimizely Experimentation attributes a single user's events over time as they go through the site.

When a user first visits the site, Optimizely Experimentation buckets them as a "logged-in visitor" and shows them a variation during Session 1. When the user returns on day three, they trigger another decision for that experiment in Session 3, and they no longer see the variation because they are now logged out and do not qualify for the experiment.

Example for Optimizely Experimentation products

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Sessionalization Diagrams - User-scoped results-4

User-scoped Experimentation products assign events from this user from the moment they are bucketed (Session 1). After no longer qualifying for the experiment, events are still assigned to the user, but they will not see the variation or experiment.

Purchase event example for Experimentation

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For example, a visitor to your site is bucketed into a variation during Session 1. Suppose this visitor is removed from the experiment in Session 3 by logging out. Afterward, they make a purchase. The visitor will no longer see the experiment, but events will continue to be assigned to the variation they were exposed to, as this change influenced them during their journey.

  • Users – 1
  • Total conversions – 17
  • Unique converting users – 1 

Purchase event example for Optimizely Web Personalization

  • Optimizely Web Personalization

Sessionalization Diagrams - Session-scoped results-3

Session-scoped products (Web Personalization) assign events to the latest decision event.

For example, a visitor to your site qualifies for a personalization campaign and is bucketed into an experience during Session 1. Suppose this visitor makes a purchase in Session 3 after logging out and qualifying for a different experience. The events from the first experience are assigned to the first experience, and the events from the second experience are assigned to the second experience.

In the preceding diagram, the red events are assigned to experience_1. The blue events are assigned to experience_2.

  • experience_1 – 7 events with "is_logged_in" as True

  • experience_1 – 2 events with "is_logged_in" as False

  • experience_2 – 7 events with "is_logged_in" as False

Count conversions when filtering by segment

Filter by segment for Experimentation products

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When you look at your results in Experimentation, you can use segmentation in Web Experimentation or Feature Experimentation to see how a subset of users respond to the variation on the results page.

Segments and filters should only be used for data exploration, not making decisions.

segment-results-web

Result segments for the events are event-scoped, meaning segmentation and attribution are based on the event's value. Event scoping lets you filter and analyze data based on the context and details provided by individual events. The visitor count is based on the decision attributes, and the event metrics are based on the events' attribute values.

When segmenting on the Experimentation results page:

  • The visitor count shows visitors who had the attribute in their decision or had a conversion event with that attribute value.
  • The conversions and events show only events with the attribute added to the event.

Event-based segmentation for registration example

A visitor browses an ecommerce site and adds items to their cart as a guest (not logged in). They create an account at the checkout (logged in). 

Optimizely segments the events accordingly:

  • not logged in – Events related to browsing and adding items to the cart.
  • logged in – Registration and checkout events.

    Sessionalization Diagrams - Event based segmentation with registration

  • Users – 1 
  • Decision event – 1
  • Total not logged in conversions – 3
  • Total logged in conversions – 4

This clear separation lets you conduct a more accurate user journey analysis, ensuring each event is associated with its accurate logged-in state.

Event-based segmentation for membership tiers example

For example, imagine you are an ecommerce platform user and want to assess users' shopping behavior across different membership levels: Basic, Premium, and Elite. 

A user with a Basic membership logs in and browses your curated selection of products. During their browsing session, they encounter a promotion, enticing them to upgrade to the Premium membership for an exclusive discount. They decide to upgrade. 

  • The actions taken by the user while on their Basic membership are accurately assigned to that tier. 
  • When they upgrade to Premium, actions are mapped under the Premium category. 

This precision in segmentation ensures that each user action is correctly associated with its respective membership level, allowing for granular analysis of user behavior across different tiers. This approach provides insights to guide marketing and UX strategies for each membership category.

Sessionalization Diagrams - event based segmentation for loyalty membership-2

  • Users – 1
  • Total Basic membership conversions – 2 (Basic memberships are not counted in the experiment because the events happened before the decision event)
  • Total Premium membership conversions – 7

Multiple attributes in events example

A user sends five events; three have the attribute not logged in, and four have logged in.

When you segment your results for:

  • not logged in – The three corresponding events are displayed.
  • logged in – The four remaining events are shown.

    Sessionalization Diagrams - Multiple attributes in events-3

Event-based segmentation lets you view detailed insight into events, ensuring you see each event in its correct context without attribute overlap or confusion.

Filter by segment for Web Personalization

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Optimizely Web Personalization campaign segmentation considers the last attribute value of the decision event, as you may qualify for multiple experiences in one session. 

Count conversions when filtering by date range

In Optimizely Experimentation, you can also filter your results by date range. For example, you might want to see the conversions that occurred between January 1 and January 20 on your Results page. This section explains how Optimizely Experimentation attributes conversion events when filtering your results by date range.

Segments and filters should only be used for data exploration, not making decisions.

The counting rule for date ranges applies when a decision is made during the filter's date range, and then events in that session are counted when that date range filter results.

Date range example for Optimizely Experimentation products

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Sessionalization Diagrams - User-scoped date range for FX-3

Optimizely looks at the first decision event of that user within the date range. Events after but within the date range are assigned. Events outside the date range and events assigned to a decision outside the date range are not counted.

  • Users – 1
  • Total conversions – 3
  • Unique converting events – 1

Date range example for Optimizely Web Personalization

Sessionalization Diagrams - Session-scoped date range-4

When a user is bucketed into the personalization campaign, and the session starts in the date range (Session 3), subsequent conversion events that occur during that user session are assigned to the last decision event. 

Unlike experiments where events are assigned to the 'first decision,' personalization campaigns consider the 'last decision' to determine to which experience the events should be assigned. This distinction is crucial because users can change states within a campaign, adding a layer of complexity compared to standard experiments.

  • Total conversions – 3

Count conversions when resetting results

In Optimizely Experimentation, you can reset your results page. This section explains how a user's events are assigned to an experiment or campaign when you reset the page.

When you reset your results, events that began before the reset point are discarded. Events after the results reset and after a decision event are counted.

Resetting results example 

Sessionalization Diagrams - Web-exp-reset-results-4

  • Users – 1
  • Total conversions – 7

Event tracking and network requests

You see event-tracking calls for visitors to your page when monitoring your network traffic. When a visitor triggers an event in Optimizely Web Experimentation, the event fires a tracking call picked up in network traffic.

You may see that Optimizely Web Experimentation tracks events for visitors not currently bucketed into an experiment and tracks events not currently part of an experiment because you can retroactively add metrics to your experiments and still get data starting the day an event was created. It also lets you target visitors with behavioral targeting and measure the reach of your campaigns.

To ensure privacy for your visitors, you can anonymize visitor IP addresses. See privacy settings in Optimizely Web Experimentation.