Blueshift builds a single, 360-degree profile for each of your users by tracking behavioral, event, and attribute data for both anonymous and signed-in identifiable users across devices and sessions and merging/stitching profiles using explicit, deterministic methods using a hierarchy of user identifiers.
Blueshift provides a highly sophisticated, flexible approach to identity resolution. It connects, unifies, and resolves identity, attribute, and event data from multiple touch points, devices, and channels into a single customer view. It supports both anonymous and known user data and resolves identities between them.
Users are initially tracked anonymously with a first-party cookie or device ID, and once user identity is known after an authentication event (the user signs in), user profiles are merged. The following user identifier hierarchy is used by Blueshift:
- uuid: Blueshift internal UUID for each user
- customer_id: Unique customer ID from your system
- email: Email address
- device_id: One or more device ids associated with the user
- cookies: One or more cookies associated with the user
User profiles get merged when either a customer ID or email (or both) are available, with the customer ID getting a higher priority. User attribute data and event data are resolved and merged into the user profile based on the user identifying themselves using one of the known user identifiers listed above.
Blueshift is uniquely able to support customers' use cases where account, group, and a householding data model is applicable. In this scenario, different customer profiles are linked to a common parent entity called a group.
Blueshift creates unified, comprehensive profiles of each user by aggregating data from across all user touchpoints. You can easily search and access a user profile within the Blueshift UI based on any user attribute. The user profile is intuitively organized into sections.
The user overview contains the basic user profile information along with channel preferences, activity, location, custom attributes, and predictive scores.
This includes information like user and mobile identifiers, CRM demographic data, mobile device data, channel subscription preferences, lifetime activity (such as revenue, orders, and visits), demographic data, and recent onsite behaviors (views, abandoned items, saved content and products, etc).
Blueshift also enriches user profiles using our AI patent technology with additional insights, including predictive scores about user's likelihood to purchase, churn, retention, engage, or perform over key behaviors, as well as their channel, brand, and category affinities.
|Campaigns||Shows a history of all the messaging activity and engagements across all marketing campaigns sent to the user.|
|Activity||Shows user behavior and activity. For example, view, purchase, add_cart, search, and so on.|
|Transactions||Shows a history of the user's transactions. For example, orders, subscriptions, and so on.|
|Recommendations||Provides a preview of 1:1 personalized recommended content/products that are derived from our predictive recommendation engine.|
As new data comes into Blueshift, the user profile is updated with the new and latest data in real-time based on one of the known user identifiers (i.e., customer_id, email, device_id). For user profile attributes, in-order data processing is done in real-time using a streaming lambda architecture to update the user data in the order in which it was received. This ensures that the user profile is updated with the most recent name, address, and so on.
Additionally, Blueshift uses explicit, deterministic methods to accurately merge and update user profiles to ensure the most accurate and complete version of the user profile.