Machine Learning plays a key role in today’s technology, allowing the extraction of relevant information from datasets, significantly increasing their value. ShiftForward DMP applies the capabilities of applying Machine Learning (ML) over captured data in order to generate value. This section describes how ML can be leveraged in the system in the following pages:
ShiftForward DMP includes a set of predictors that demonstrate its ML capabilities. These use captured user information to extrapolate additional information about the user. The following attributes are computed and made available for segment creation:
CLV – The Customer Lifetime Value of the user. Corresponds to the expected spending for an user during its lifetime. It enables the creation of segments with the high-spending customers.
Probable Gender – When the user lacks gender attribute, ShiftForward DMP can identify its probable gender, increasing the pool of users with gender information and enriching gender-based segments.
Probable Age-band – When the user lacks the age-band attribute, ShiftForward DMP can identify its probable age-band, increasing the pool of users with age-band information and enriching age-band based segments.
The ShiftForward DMP ML infrastructure is available for developing custom jobs on top of ShiftForward DMP. Using the ShiftForward DMP SDK, it is possible for developers to create and deploy jobs in the ShiftForward DMP infrastructure, allowing the creation of custom reports, segments and more. For more details, check the ShiftForward DMP SDK documentation.