1.1 - 8.10 - Attribution_MLE (ML Engine) - Teradata Vantage

Teradata Vantage™ - Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
1.1
8.10
Release Date
October 2019
Content Type
Programming Reference
Publication ID
B700-4003-079K
Language
English (United States)
The Attribution_MLE function assigns attribution (credit) for an event of interest to preceding events. The following are common Attribution_MLE applications:
  • Web page analysis: allow a company to attribute credit for an event, such as a purchase, to preceding page visits or clicks
  • Health care: analyze events leading to an event, such as a diagnosis
  • Industry: analyze events leading to an event, such as equipment failure

The event of interest is called the conversion event. You specify the interval preceding the conversion event, in either rows or seconds, in which events must occur to be eligible for attribution. This interval is called the window.

You can either specify the event types that are eligible for attribution or you can specify that attribution be allocated across a specified number of rows or seconds prior to the conversion event.

The function can assign attribution in any of the following ways:
  • To the earliest eligible event
  • To the latest eligible event
  • Distributed uniformly across eligible events
  • Distributed across eligible events according to weights you specify
  • To eligible events in an exponentially decaying way

The function recognizes the following event types:

Event Type Description
Conversion Event for which attribution (credit) is to be assigned to preceding events; for example, a purchase.
Regular Event that is eligible for attribution.
Excluded Event to ignore (not eligible for attribution).
Optional Event that is eligible for attribution only if no other event is eligible.

The Attribution_MLE function has two syntaxes:

Syntax Description
Multiple-Input Attribution_MLE (ML Engine) Attribution models, optional events, conversion events, and exclusion events are described in tables.
Single-Input Attribution_MLE (ML Engine) Attribution models, optional events, conversion events, and exclusion events are described in syntax elements.