Segment filter categories

Blueshift has an array of filters that you can use to create segments that target the right customers based on specific features and attributes. 

For more information related to filters and usage, see the following topics:

• Explicit, derived, and predictive user attributes
• Segment examples
• Optimize your segments
• Default data limits and retention policy

Segment filters

The following filters are available to use while building segments:

Recent activity Lifetime activity Catalog activity Demographic Predictive scores

User affinity Traffic source User attributes Customer lists Messaging

Journey actions Transactions Member of segment Interests

Recent activity

Build conditions based on customer behavior on your website and apps in the past 31 days. The data in this section is populated from your event stream. 

You can use the following filters:

• Boolean conditions on behaviors.
• Timeline filtering: Apply dynamic (e.g., “last 24 hours”) or static (“during October 2015”) timelines to zoom in on the right customers.
• Filter by event attributes: Use attributes denoting a specific behavior to narrow down to page views from certain types of devices or channels or searches containing a particular keyword.
• Frequency: Find customers who performed the action frequently.

Consider the following points when you use recent activity attributes in the filter criteria:

• The search is case-insensitive.
• All event attribute values are converted to lowercase before storing the data.

Lifetime activity

During an entire customer journey, Blueshift automatically computes aggregate statistics on visits, purchases, revenue, referrals, message sends, etc. You can create conditions related to these aggregate statistics. For more information, see Derived attributes.

Catalog activity

You can segment by customer interactions with various parts of your product or content catalog for the past 180 days. In addition to the filters available on the Recent Activity tab, you can filter based on catalog/content attributes. 

Catalog Activity is created by combining the data from an event and the data from a product catalog item. For more information about setting up your catalog and tracking catalog activity, see Track catalog activity.

Consider the following points when you use catalog activity attributes in the filter criteria:

• The search is case-sensitive.
• Catalog activity contains a subset of events available under Recent Activity.
• Blueshift enhances these events with catalog information and retains the data for up to 180 days.

Note: SKU ID is the same as the item ID or product ID in the catalog.

Demographic

Blueshift expands your raw data to enable easy demographic segmentation. You can either directly upload demographic information into Blueshift or leverage Blueshift’s data expansion. 

Demographic data includes the following expanded data:

• Gender is inferred based on the customer's first name by using census information.
• IP Addresses are expanded into location data.
• Recent location (city/state/country)
• Proximity to a location (city/zip code/latitude-longitude)   

Predictive scores

You can segment not only based on a customer's past behavior but also by various predictive scores. Blueshift’s predictive scores can help you find customers with a high or low likelihood of completing multiple actions in the conversion funnel and the customer lifecycle.

Please take a look at the Predictive Studio documentation to understand the correlation of predictive scores with the metrics you are interested in.

User affinity

Customers often have affinities towards different catalog sections available on your website. Blueshift computes user affinities based on behavioral & transactional data and keeps the affinity scores updated in near real-time. You can easily use this user affinity data when you create segments.

Predictive Channel Optimization (Messaging channel affinity)  is an advanced capability for marketers on the Blueshift platform to automate finding the right messaging channel for each customer using cutting-edge AI algorithms. The algorithms look at the total customer activity on each messaging channel, opens, clicks, and downstream engagement metrics like the number of sessions, time spent on sites and apps, and recency of engagement to learn for each customer what channel they are most likely to engage on.

The following affinity types are available:

• Hour
• Item category
• Messaging channel

Note: Please contact support@blueshift.com to enable computing of user affinities.

Traffic source

Blueshift stores the traffic source information on all your web traffic. With this, you can easily segment customers by the source of traffic.

Traffic source - the first Urchin Tracking Module (UTM) parameter

Note: If a new customer gets created in Blueshift, the traffic source parameters for the customer are set to the first UTM parameters we see for the customer.

User attribute	Display name
first_utm_campaign	Campaign
first_utm_medium	Medium
first_utm_source	Source
first_utm_content	Content
first_utm_term	Term

User attributes

You can create segments by filtering on any of the customer attributes that you pass into Blueshift through the Identify event, User API, or by uploading Customer Attributes through the dashboard. In addition, Blueshift also computes some important customer attributes, including first & last traffic source, location, unsubscribed_date, and so on.

Note: The search is case-sensitive.

First & last traffic source - the first and last Urchin Tracking Module (UTM) parameter

In addition to information about how customers were acquired (first UTM parameter), Blueshift stores the last/recent traffic source information (last UTM parameter) on all your web traffic. You can access these traffic source parameters as user attributes.

Customer lists

A Customer list is a static list of customers you can use to create segments. After you create a Customer list, you can reference it in the Segment. 

Scenarios where using a customer list can be beneficial include:

• As a control group: A randomly selected set of customers excluded from receiving a specific message.
• As a targeted list: A list created outside Blueshift containing customers to whom you want to send a specific message.

Journey actions

Use the journey actions filter to segment customers based on their campaign or journey actions.

• Grouping - select one among the following:
  • ALL: Match all selected conditions.
  • ANY: Match at least one of the selected conditions.
  • NONE: Match none of the selected conditions.

• Action type - Options are Exit Journey, Data Update, and Audience Sync. Select the specific journey action to filter for. Example - Choose 'Exit Journey' to filter customers who exited a journey.

• Define timeframe

  • Within past: Filter actions in a rolling timeframe. Example - "Within the past 1 Week."
  • Between moving window: Specify a dynamic range of time relative to the current date. Example - "From 2 Days Ago to 1 Day Ago."
  • Between fixed window: Define a fixed date range for actions. Example - "From Jan 1, 2025, to Jan 15, 2025."

• Add frequency (optional - example: "At least 2 times" and "At most 5 times.")

  • At least: Minimum number of times the action occurred.
  • At most: Maximum number of times the action occurred.

 

Messaging

Segmenting customers by their responsiveness to messages (push notifications, emails, SMS) helps you adjust the messaging volume. You can send marketing communications to customers based on their engagement level.

Blueshift provides a comprehensive solution for segmenting customers by their response to messaging for up to the last 6 months.

• Select the engagement channel.
• Select the engagement metric.
  • Sends, impressions, unique impressions, clicks, unique clicks, visits, orders, activations, revenue, bounces, spam reports, unsubscribes, devices sent, devices bounced from the last 6 months.
  • Delivered in the last month.
• Select a campaign by name or select all campaigns.
  • For a specific campaign, you can filter based on the messaging template by selecting the trigger and the experiment.
  • Additionally, you can narrow down to a specific link within a template by selecting 'channel=email' and 'action=click.' To do this, specify the template version and link index, which can be found in the Engagement tab within the email studio.
  • Select a fixed or a relative timeline.
  • Apply a frequency filter.
• Select a campaign by tag. You can select one or more tags to segment customers based on their interactions with campaigns that had certain tags.
  • Select a fixed or a relative timeline.
  • Apply a frequency filter.

Transactions

You can use the transactions feature to chain related events/activities and segment users in different states across their journey. The transaction-based segmentation empowers you to find users based on their transaction status. For instance, you can find all orders placed last week but have yet to be shipped.

If you would like more information, you can visit Segmenting on transactions.

Member of segment

You can add a reference segment for the segment that you are creating. Reference segments are segments that you use in other segments. You can define a primary criteria in a segment and then reuse that criteria in other segments. You can add basic and advanced segments as reference segments.

If you would like more information, you can visit Reference segments.

Interests

You can create a segment of users based on their shared interest in a particular topic. You can then use this segment in a campaign targeting users based on their interest in the topic.

If you would like more information, you can visit Segmenting on interest alerts.