1.0 - 8.00 - Attribution Arguments (Single Input) - Teradata Vantage

Teradata® Vantage Machine Learning Engine Analytic Function Reference

Teradata Vantage
Release Number
Release Date
May 2019
Content Type
Programming Reference
Publication ID
English (United States)
Specify the name of the input column that contains the clickstream events.
Specify the conversion events. Each conversion_event is a string or integer.
[Optional] Specify the events to exclude from the attribution calculation. Each exclude_event is a string or integer. An exclude_event cannot be a conversion_event.
[Optional] Specify the optional events. Each optional_event is a string or integer. An optional_event cannot be a conversion_event or exclude_event. The function attributes a conversion event to an optional event only if it cannot attribute it to a regular event.
Specify the name of the input column that contains the timestamps of the clickstream events.
Specify how to determine the maximum window size for the attribution calculation:
Option Description
rows: K Consider maximum number of events to attribute, excluding events of types specified in excluding_event_table, which means assigning attributions to at most K effective events before current impact event.
seconds: K Consider maximum time difference between current impact event and earliest effective event to attribute.
rows: K &seconds: K2 Consider both constraints and comply with stricter one.
Specify the type and specification of the first model. For example:
Model1 ('EVENT_REGULAR', 'email:0.19:LAST_CLICK:NA', 'impression:0.81:WEIGHTED:0.4,0.3,0.2,0.1')
[Optional] Specify the type and distributions of the second model. For example:
Model2 ('EVENT_OPTIONAL', 'OrganicSearch:0.5:UNIFORM:NA', 'Direct:0.3:UNIFORM:NA', 'Referral:0.2:UNIFORM:NA')

For more information about the Model1 and Model2 arguments, see the following tables in Attribution Input (Multiple Inputs):

Table Information
Attribution Model Types and Specification Definitions Model type and specification definitions
Attribution Distribution Model Specification Models and Parameters MODEL values and their corresponding PARAMETER values
Attribution Allowed Model1/Model2 Combinations Allowed Model1/Model2 combinations