Examples of Basic segments

Here are some examples of basic segments to help you understand how you can combine attributes from various filters to create the required segment.

For more information, see the following topics:

• Segment conditions and operators
• Segment filter categories
• Basic segment builder
• Optimizing your segments

Based on activity

1. Users who added items to cart multiple times but did not purchase

In this example, we are creating a segment for users who added items to their carts at least 10 times in the past 14 days but did not purchase in the past 14 days.

Use the following attributes to create the segment:

• Recent activity filter: add_to_cart attribute
• Recent activity filter: purchase attribute

2. Users who purchased items but did not download the app

In this example, we are creating a segment for users who purchased items within the past 6 months but have not downloaded the app as yet (their device_ids are empty).

Use the following attributes to create the segment:

• Catalog activity filter: purchase attribute
• User attributes filter: device_ids attribute

Catalog activity contains a subset of events available under Recent Activity. As such, you can also use the Recent activity filter here instead of the Catalog activity filter.

3. Users who added specific items to their wish list

In this example, we are creating a segment for users who added books by both William Shakespeare and Charles Dickens to their wish list in the past 50 days.

Use the following attributes to create the segment:

• Catalog activity filter: add_to_wishlist attribute
• Catalog activity filter: author attribute

4. Users who purchased items from specific categories during a specific time period

In this example, we are creating a segment for users who purchased books from any of the 3 categories, history, fiction, or poetry, between 1st and 10th February.

Use the following attributes to create the segment:

• Catalog activity filter: purchase attribute
• Catalog activity filter: author attribute

Based on predictive scores

1. Users with recent engagement and high likelihood of purchase

In this example, we are creating a segment for users who last clicked an email within the past 1 week and have a 80%-100% likelihood of making a purchase.

Use the following attributes to create the segment:

• Predictive scores filter: purchase intent attribute
• User attributes filter: last_click attribute

2. Highly engaged users who joined recently but are less likely to purchase

In this example, we are creating a segment for users who joined in the past 1 month, have an 80%-100% engagement rate and are only 30%-50% likely to make a purchase. 

Use the following attributes to create the segment:

• Predictive scores filter: purchase intent attribute
• Predictive scores filter: engagement attribute
• User attributes filter: joined_at attribute

Target a particular set of users

1. Users from an uploaded list who have not engaged as yet

In this example, we are creating a segment for users who are in an uploaded list named “example list of new users”, but haven't opened an email as yet. 

Use the following attributes to create the segment:

• Customer lists filter: Customer list group = “example list of new users”
• Messaging filter: engagement channel = email
• Messaging filter: engagement metric = opens

2. Users who were first acquired from a particular campaign and who last accessed messages sent by email

In this example, we are creating a segment for users who were first acquired from the “Summer Drive 2021” campaign and who last accessed messages sent by email. 

Use the following attributes to create the segment:

• User attributes filter: first_utm_campaign attribute OR Traffic source filter: Campaign attribute
• User attributes filter: last_utm_medium attribute

3. Users who engaged with a particular campaign but did not purchase

In this example, we are creating a segment for users who clicked an email sent for a campaign called “Blueshift_Test” but did not purchase anything in the past 1 day.

Use the following attributes to create the segment:

• Messaging filter: engagement channel = email
• Messaging filter: engagement metric = opens
• Messaging filter: campaign = “Blueshift_Test”
• Catalog activity filter: purchase attribute

4. Users who clicked on a particular link inside a template

You might want to find users who clicked on a certain item, button or a link inside a template. The template might have multiple buttons and links and you want to find all the users who clicked a specific link. In order to do this, you must know the specific version of the template and you can look up the link index for the link from the Engagement tab of the template.

In this example, we are creating a segment for users who clicked a link to purchase DIY books. This link was included in the "Summer 23 book promo" template and was sent to customers as part of the "Summer 23 sales" campaign.

Use the following attributes to create the segment:

• Messaging filter:
  • engagement channel = email
  • action = click
  • campaign = Summer 23 sales
  • Trigger = Email promo #1
  • experiment = Summer 23 book promo
  • Version = 18
  • Link index = 3

Based on affinity

1. Users with a particular category affinity who have engaged actively recently

In this example, we are creating a segment for users who have an affinity for Literary books and have opened emails sent in the past 3 weeks. 

Use the following attributes to create the segment:

• Messaging filter: engagement channel = email
• Messaging filter: engagement metric = opens
• User affinity filter: Item category = Literary

2. Users with a particular time affinity who engaged with push notifications recently

In this example, we are creating a segment for users who have an affinity to engage at 10 am and have clicked a push notification in the past 20 days.

Use the following attributes to create the segment:

• Messaging filter: engagement channel = push
• Messaging filter: engagement metric = click
• User affinity filter: Hours = 10

3. Users who are highly likely to engage with emails but not push notifications

In this example, we are creating a segment for users who have an affinity to open emails but not push notifications.

Use the following attributes to create the segment:

• User affinity filter: messaging channel = email

Miscellaneous

1. Users who are using email clients that automatically open the email

In this example, we are creating a segment for users who are using email clients that automatically download/ open the email for the user. For example, Apple Mail on iOS15+ devices.

Use the following attributes to create the segment:

• Messaging filter: engagement channel = email
• Messaging filter: engagement metric = open

"Open" events have the following attributes:

• prefetch: if the email was automatically opened or downloaded by the email client. For example, AppleMail on iOS15+ devices.
• proxy: if the email was opened by a proxy server.
• user: if the email was opened by a human user. For example, a user on a non-Apple device.

2. User accounts with bot click activity

In this example, we are going to identify user accounts for which we have seen bot click activity in the past 7 days. 

Use the following attributes to create the segment:

• Messaging filter: engagement channel = email
• Messaging filter: engagement metric = click

"Click" events have the following attributes:

• user: the click activity was most likely from a genuine human user.
• bot: the click activity was most likely from a bot.

 

3. High value users who have signed up recently

In this example, we are creating a segment for users who signed up within the past 31 days and already have a lifetime revenue spend of at least 100 dollars.

Use the following attributes to create the segment:

• Recent activity filter: purchase attribute
• Lifetime activity filter: device_ids attribute

4. Users located in a specific area

In this example, we are creating a segment for users located within 10 miles of San Francisco.

Use the following attributes to create the segment:

• Demographic filter: proximity attribute

5. Users with push enabled devices

In this example, we are creating a segment of users who have installed your app and are push enabled.

Use the following attributes to create the segment:

• User Attributes: device_tokens is not empty
• User Attributes: devices.enable_push is true

Refresh the user count to get the number of customers who have installed your app on at least one device.

Note: This count includes users who have unsubscribed from receiving push notifications.