Attributes

Attributes can be used to create Segments using the Segment Rules. ShiftForward DMP provides two sources of attributes: event-driven and machine learning.

Event-driven

Attributes based on the user’s collected events. ShiftForward DMP comes with a standard set of Attributes but custom Attributes can be created as well.

Standard event-driven attributes:

Attribute name Type Rule description
events.eventType.last Time Time of the most recent event of the type eventType.
events.eventType.count Number Number of events of the type eventType performed.
events.timestamp.first Time Time of the oldest event.
events.timestamp.last Time Time of the most recent event.
events.count Number Total number of events.
mkt.lastcampaignId.name String Name of the most recently seen campaignId= in the event stream.
mkt.lastsource.name String Name of the most recently seen source= in the event stream.
ecom.cartLeaver Boolean True if the most recent addToCart event happened after the most recent Purchase event.
ecom.purchase.value Number Sum of the transaction.price value from all user events.
ecom.purchase.last.value Number The transaction.price value from the most recent Purchase event.

Optionally enabled standard Attributes:

geo.city.last String City name in the most recent event.
geo.country.last String Country name in the most recent event.
geo.location.last Lat/Long Lat/Long in the most recent event.

Example of custom event-driven attributes:

name Type Rule description
retarget.campaign148374.last Time Time of the most recent event that contained campaignId =148374.
retarget.geoNearShop.count Number Number of events where geo.location was less than 500m from a specific list of location.
retarget.funnelStage1.last Time Time of the most recent event that contained custom key “funnelStage” with value “1”.

Machine learning (ML)

Machine learning identifies patterns in captured data, enabling the creation of recommendations or behavior predictions for specific users. The application of machine learning greatly increases the usefulness of the captured data as it helps to better segment users.

ShiftForward DMP leverages machine learning to identify high value users from their historic behavior. Given the required information sources, ShiftForward DMP provides the following additional details for each user:

name Type Rule description
ml.clv Number The expected spending for the user for the next 12 months.

Create your own using your custom ML models, see more on the machine learning in ShiftForward DMP.