Segment filter categories

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

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

Default data limits and retention policy

  • For the Recent activity filter you can segment based on event data for the last 31 days.
  • For the Catalog activity filter, you can segment based on product events for the past 6 months.
  • Messaging data includes:
    • 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 from the last one month.
  • Transactions include 10 years of transaction data such as orders placed, returns initiated, items shipped, and so on.

Contact support@blueshift.com if you have questions about these limits. 

Segment Filters

The following filters are available to use while building segments: 

Recent Activity

Build conditions based on user 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 users.
  • Filter by event attributes: Use attributes of a behavior to narrow down to page views that came from certain types of devices or channels or searches that contained a certain keyword.
  • Frequency: Find users 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

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

Catalog Activity

You can segment by user interactions with various parts of your product or content catalog for up to last 6 months. In addition to the filters available on the Recent Activity tab, you can also 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 upto 1 year.

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 user's first name by using census information.
  • IP Addresses are expanded into location data.
  • Recent location (city/state/country)
  • Proximity to a location (city/zipcode/latitude-longitude)   

Predictive Scores

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

See the Predictive Studio documentation to understand the correlation of predictive scores with the metrics you are interested in.

User Affinity

Users often have affinities towards different sections of the catalog 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 user using cutting edge AI algorithms. The algorithms look at the sum total of user activity on each messaging channel, opens, clicks and downstream engagement metrics like number of sessions, time spent on sites and apps and recency of engagement, to learn for each individual user, what channel they are most likely to engage on.

The following affinity types are available:

  • Hour
  • Item category
  • Messaging channel

Traffic Source

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

Traffic source (first utm params)

Note: If a new user gets created in Blueshift, the traffic source params for the user are set to the first utm params we see for the user.

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 user 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 user attributes, including first & last traffic source, location, unsubscribed_date, and so on.

Note: The search is case sensitive.

First & last traffic source (first utm and last utm params)

In addition to information about how users were acquired (first utm params), Blueshift also stores the last/recent traffic source information (last utm params) 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 users which you can use to create segments. After you create a Customer List, you can reference it in the Segment. 

Some examples where a customer list might be useful include:

  • As a control group containing a randomly selected set of users to whom you do not send a particular message.
  • As a targeted list you have computed outside of Blueshift, but want to send a message to.

Messaging

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

Blueshift provides a comprehensive solution for segmenting users 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 from the last one month.
  • Specify a campaign or select all campaigns.
  • For a specific campaign, you can filter based on the experiment or trigger level.
  • Select a fixed or a relative timeline.

Transactions

You can use the transactions feature to chain related events/activities and segment users on different states across their journey. The transaction based segmentation empowers you to find users based on their transaction status. For instance, you could find all orders that were placed last week, but not shipped yet.

For more information, see 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 basic criteria in a segment and then reuse that criteria in other segments. You can add basic segments and advanced segments as reference segments.

For more information, see 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 that targets the users based on their interest in the topic.

For more information, see Segmenting on Interest alerts.



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