Segment Examples

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

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.

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

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

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

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

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

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

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

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

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

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

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

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 = opens

"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.

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

In this example, we see how to create a segment to query for customers who browsed books related to East Asian Philosophy, but did not buy any. The product catalog does not have an “East Asian Philosophy” category, so it is not possible to use a basic segment for this query. The catalog does have the Philosophy > Zen and the Philosophy > Taoism categories and the books related to East Asian Philosophy are part of one of these categories.

So let's now build the Abandoned Browse East Asian Philosophy segment to find customers who browsed books from either the Philosophy > Zen or the Philosophy > Taoism category, but did not buy any.

Step 1: We are looking for customers who abandoned browsing in the category Philosophy > Zen or the category Philosophy > Taoism.

To achieve this, we must set the outermost grouping to OR.

Step 2: Search for customers who abandoned browsing in the Philosophy > Zen category.

To achieve this, we must search for customers who satisfy both the following conditions:

• Customers viewed an item in the Philosophy > Zen catalog within the past 7 days.
• Customers did not buy an item in the Philosophy > Zen catalog within the past 7 days.

1. Click Add Grouping within the outer OR grouping and add an AND grouping.

   

2. Click Add Condition > Catalog Activity.

   

3.  Set the condition to select customers who viewed an item in Philosophy > Zen within the past 7 days.

   

4. Click Add Grouping within the added AND grouping and add another AND grouping.

   

5. Change the newly created grouping from AND to NOT.

   

6. In the NOT grouping, click Add Condition > Catalog Activity and set the condition to select customers who bought an item in Philosophy > Zen within the past 7 days.

   

Step 3: Search for customers who abandoned browsing in the Philosophy > Taoism category.

To achieve this, we must search for customers who satisfy both the following conditions:

• Customers viewed an item in the Philosophy > Taoism catalog within the past 7 days.
• Customers did not buy an item in the Philosophy > Taoism catalog within the past 7 days.

1. Click Add Grouping within the outer OR grouping and add an AND grouping.

   

2. Click Add Condition > Catalog Activity and set the condition to select customers who viewed an item in Philosophy > Taoism within the past 7 days.

   

3. Click Add Grouping within the added AND grouping and add another AND grouping. Change the newly created grouping from AND to NOT.

   

4. In the NOT grouping, click Add Condition > Catalog Activity and set the condition to select customers who bought an item in Philosophy > Taoism within the past 7 days.

   

The Abandoned Browse East Asian Philosophy segment is now created. The right side of the segment editor has a query summary.