Google Inc.

Bozhena Bidyuk

Chao Cai

Richard Edward Little

Stefan F Schnabl

William Patrick Kee

John Chih Huang

David Earl Barney

Todd Morris Jackson

John Chih Chang Huang

James Edward Stewart

Robert H Murray

Hongxu Cai

Xiaosa Zhao

Jyotsna Gangwar

Systems and methods for creating a data-driven attribution model are described. A processor identifies visits to a website. The processor identifies a path for each visitor identifier associated with the visits. The processor determines, for each path type associated with the identified paths, a path-type conversion probability based on a number of visits corresponding to the path type that resulted in a conversion. The processor calculates, for each of a plurality of the path types, a counterfactual gain for each event based on a conversion probability of the given path type and a conversion probability of a path type that does not include the event for which the counterfactual gain is calculated. The processor determines, for each event, an attribution credit based on the calculated counterfactual gain of the event. The processor then stores the attribution credits of each of the events.

Methods and systems for creating a data-driven attribution model for assigning attribution credit to a plurality of events

Systems and methods for creating rules for assigning attribution credit across events, includes, identifying, by a processor, conversions at a website. The processor identifies path types associated with the conversions. Each path type identifies events and a index position indicating an event's relative position. The processor identifies a subset of the identified path types to be rewritten according to a path rewriting policy. The processor then rewrites the identified subset of the identified path types as rewritten path types. The processor determines, for each of the rewritten path types and remaining identified path types associated with the identified conversions, attribution credits for each event included in the path type. The processor creates, for each of the rewritten path types and remaining identified path types associated with the identified conversions, a rule for assigning the determined attribution credit to each event of the path type for which the rule is created.

Methods and systems for creating rules for assigning attribution credit across a plurality of events

Methods and systems for providing for display attribution data associated with one or more events are disclosed. Processor identifies channels from paths including events corresponding to position data identifying a position along the path at which the event was performed. Processor determines attribution credits assigned to each event included in the paths corresponding to the channel. Processor determines a number of attribution credits assigned to the channel. Processor identifies, from the paths, a plurality of event-position pairs. Each event-position pair corresponds to events that correspond to a respective channel and are performed at a respective position of the plurality of paths corresponding to the event-position pair. Processor determines, for each identified event-position pair, a weighting based on an aggregate of the attribution credits assigned to the events to which the event-position pair corresponds. Processor provides, for display, a visual object including an indicator to display the determined weightings.

Methods and systems for displaying attribution credit data based on parameters

Systems and methods for measuring conversion probabilities of a path types for an attribution model includes, identifying by a processor, paths taken by visitors to visit a website. The paths correspond to a sequence of events that cause a visitor to visit the website. The processor can identify as paths, for each path, subpaths corresponding to each visit to the website. The processor computes a total path count for each path type. The processor identifies, for each path type, a conversion path count indicating a number of paths taken by visitors that resulted in a conversion at the website. The processor calculates, for each path type, a probability of conversion and then provides the calculated probability of conversion for a given path type for an attribution model used in assigning attribution credit to events of a path.

Methods and systems for measuring conversion probabilities of paths for an attribution model

Systems and methods for selecting content for display at a device includes, identifying by a processor, a visitor identifier associated with a device on which to display content. The processor can identify a path associated with the visitor identifier. The path corresponding to a sequence of one or more events through which the visitor identifier has visited the website. The processor can identify a conversion probability of the identified path. The conversion probability of the identified path indicates a likelihood that the visitor identifier will convert at the website. The conversion probability of the identified path is a ratio of a number of conversions at the website to a number of visits to the website over a given time period. The processor can select content for display. The content selected based on the identified conversion probability of the identified path.

Methods and systems for selecting content for display based on conversion probabilities of paths

Methods, systems and apparatus, including computer programs encoded on a computer storage medium, for generating a target bid for an advertiser to use in an auction directed at enabling the advertiser to reach the advertiser's advertising goal (e.g., a cost-per-call goal) for a particular advertising target (e.g., a television program or demographic) based on the target's historical advertising performance.

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Mountain View, CA, United States of America

Bozhena Bidyuk