User Attributes and Types

Blueshift can store various types of user data - explicit, derived and predictive.  All of these user fields are case-sensitive and space-sensitive.

Standard (Blueshift Attributes) - Blueshift has a standard set of attributes you can map your data to

Attribute Definition
email Email address of the user, identifies a user uniquely.
customer_id Customer id for this user in your database, identifies a user uniquely.
firstname User first name (optional)
lastname User last name (optional)
gender User's gender (optional): male, female or unknown
joined_at Date user joined (optional) in ISO-8601 format
facebook_id User’s Facebook id (optional)
unsubscribed User’s subscription status (optional) boolean. Set to true - if the user has unsubscribed from mailing list
unsubscribed_at Date when user unsubscribed in ISO-8601 format
email_hard_bounced Automatically updated when a hard bounce occurs (true or false)
email_spam_reported Automatically updated when a user marks your email as spam via the ISP (true or false)
phone_number User's phone number, including ISD code.  Used for SMS messaging.  (optional)
device_ids Mobile device identifiers (optional)
device_tokens Mobile device tokens used for Push messaging (optional)


Custom Attributes: 
Non-standard or other custom attributes 

Custom Attribute (create your own)

 You may pass any other attribute, and it would get saved on the user.       Ex: membership_level, loyalty_points, etc.

 

Derived Attributes: Blueshift can compute certain attributes based on all the data (event, campaign) from a user 

Attribute Definition
lifetime_revenue Computed and aggregated from purchase event. Requires revenue as an event attribute
lifetime_orders Computed and aggregated from purchase event. 
lifetime_visits Computed from session data. Each session lasts 30 minutes
llifetime_(custom goal) Computed from custom event goals
unsubscribed_at The time when a user unsubscribed from an email campaign
session_last_activity_at Last known session date
last_purchase_at Date when a user last purchased.  Derived from purchase event
last_visit_at Last known pageload event
last_sent_at Most recent sent campaign time
last_open_at Most recent campaign open time
last_device_token Most recent device token sent from your mobile app
last_location_geo_longitude Last known geo longitude of the user 
last_location_geo_latitude Last known geo latitude of the user

 

Predictive Scores: Blueshift also computes Predictive Scores and affinities that can then be used in segments and campaign filters

Attribute Definition
Predictive Scores Derived from predictive models (engagement, purchase intent/conversion, and retention)
Category Affinity Based on user interactions with your content/catalog in the last 28 days, computed daily.  May contain category affinities
Top Hour Optimal engagement time based on session data (events and campaign engagement) in the last 28 days, computed daily

 

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