1.1 - 8.10 - Attribution Model Specification - 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)

You can specify attribution models with either tables (using multiple-input syntax) or syntax elements (using single-input syntax). The following tables show the information you must specify, which is the same for both syntaxes.

Attribution Model Types and Distribution Model Formats

Attribution Model Type Distribution Model Format Additional Information
SIMPLE MODEL:PARAMETERS Distribution model applies to all eligible events.
EVENT_REGULAR EVENT:WEIGHT:MODEL:PARAMETERS Distribution model applies to all regular events.

EVENT cannot be a conversion, excluded, or optional event.

Sum of WEIGHT values must be 1.0.

For example, suppose model is specified as follows:

email:0.20:LAST_CLICK:NA
impression:0.80:UNIFORM:NA

Within WindowSize of a conversion event, 20% of conversion event is attributed to most recent email event and 80% is attributed uniformly to all impression events.

EVENT_OPTIONAL EVENT:WEIGHT:MODEL:PARAMETERS Distribution model applies to optional events only if no regular event received attribution.

EVENT must be an optional event.

Sum of WEIGHT values must be 1.0.

SEGMENT_ROWS K i:WEIGHT:MODEL:PARAMETERS Distribution model applies to all rows within K rows of conversion event. K is specified by 'rows:K' in WindowSize syntax element.

Sum of K i values must equal K.

For example, suppose WindowSize is ('rows:10') and model is specified as follows:

3:0.5:UNIFORM:NA
4:0.3:LAST_CLICK:NA
3:0.2:FIRST_CLICK:NA

Attribution for a conversion event is divided among attributable events in 10 rows immediately preceding conversion event. If conversion event is in row 11, first model specification applies to rows 10, 9, and 8; second applies to rows 7, 6, 5, and 4; and third applies to rows 3, 2, and 1.

Half attribution (5/10) is uniformly divided among rows 10, 9, and 8; 3/10 to last click in rows 7, 6, 5, and 4 (that is, in row 7), and 2/10 to first click in rows 3, 2, and 1 (that is, in row 1).

SEGMENT_SECONDS K i:WEIGHT:MODEL:PARAMETERS Distribution model applies to all rows within K seconds of conversion event. K is specified by 'seconds:K' in WindowSize syntax element.

Sum of K i values must equal K.

For example, suppose WindowSize is ('seconds:10') and model is specified as follows:

6:0.5:UNIFORM:NA
8:0.3:LAST_CLICK:NA
6:0.2:FIRST_CLICK:NA

Attribution for a conversion event is divided among attributable events in 20 seconds immediately preceding conversion event. If conversion event is at second 21, first model specification applies to seconds 20-15 (counting backward); second applies to seconds 14-7; and third applies to seconds 6-1.

Half attribution (5/10) is uniformly divided among seconds 20-15; 3/10 to last click in seconds 14-7, and 2/10 to first click in seconds 6-1.

Distribution Model Parameters

Distribution Model Description Parameters
'LAST_CLICK' Conversion event is attributed entirely to most recent attributable event. 'NA'
'FIRST_CLICK' Conversion event is attributed entirely to first attributable event. 'NA'
'UNIFORM' Conversion event is attributed uniformly to preceding attributable events. 'NA'
'EXPONENTIAL' Conversion event is attributed exponentially to preceding attributable events (the more recent the event, the higher the attribution). 'alpha,type' where alpha is a decay factor in range (0, 1) and type is ROW, MILLISECOND, SECOND, MINUTE, HOUR, DAY, MONTH, or YEAR.

When alpha is in range (0, 1), sum of series w i=(1-alpha)*alpha i is 1. Function uses w i as exponential weights.

'WEIGHTED' Conversion event is attributed to preceding attributable events with weights specified by PARAMETERS.SEGMENT_SECONDS (when you specify 'rows:K&seconds:K' in Window syntax element) You can specify any number of weights. If there are more attributable events than weights, extra (least recent) events are assigned zero weight. If there are more weights than attributable events, then function renormalizes weights. See Attribution_MLE Example (Single Input): Dynamic Weighted Distribution Models.