公开(公告)号：US09697534B2

公开(公告)日：2017-07-04

申请号：US13922198

申请日：2013-06-19

Applicant: Google Inc.

Inventor： Stefan F. Schnabl ,  Jon Vaver ,  Arjun Satyapal ,  John Huang ,  Wenjie Jiang

IPC: G06F17/00 ,  G06Q30/02

CPC classification number: G06Q30/0244 ,  G06Q30/0256

Abstract: The present disclosure includes systems and techniques relating to identifying value marketing activities. In some implementations, an apparatus, systems, or methods can include receiving conversion path information including data relating to user interactions with a content item associated with a marketing activity, determining a first attribution credit by applying a first attribution model to the received information, and a second attribution credit by applying a second attribution model to the received information, determining an attribution contrast ratio based on the first and second attribution credit, identifying an opportunity based on the determined attribution contrast ratio, and presenting a recommendation for the marketing activity based on the identified opportunity.

公开(公告)号：US20140379490A1

公开(公告)日：2014-12-25

申请号：US13922198

申请日：2013-06-19

Applicant: Google Inc.

Inventor： Stefan F. Schnabl ,  Jon Vaver ,  Arjun Satyapal ,  John Huang ,  Wenjie Jiang

IPC: G06Q30/02

CPC classification number: G06Q30/0244 ,  G06Q30/0256

Abstract: The present disclosure includes systems and techniques relating to identifying value marketing activities. In some implementations, an apparatus, systems, or methods can include receiving conversion path information including data relating to user interactions with a content item associated with a marketing activity, determining a first attribution credit by applying a first attribution model to the received information, and a second attribution credit by applying a second attribution model to the received information, determining an attribution contrast ratio based on the first and second attribution credit, identifying an opportunity based on the determined attribution contrast ratio, and presenting a recommendation for the marketing activity based on the identified opportunity.

Abstract translation: 本公开包括与识别价值营销活动相关的系统和技术。 在一些实现中，装置，系统或方法可以包括接收转换路径信息，包括与与营销活动相关联的内容项目的用户交互相关的数据，通过对所接收的信息应用第一归属模型来确定第一归属信用;以及 通过对所接收的信息应用第二归属模型来确定第二属性信用，基于所述第一和第二归属信用确定归因对比度，基于所确定的属性对比度来识别机会，以及基于 确定的机会。

公开(公告)号：US10719521B2

公开(公告)日：2020-07-21

申请号：US15707594

申请日：2017-09-18

Applicant: Google Inc.

Inventor： Stephanie Zhang ,  Jon Vaver

IPC: G06G7/00 ,  G06F16/2458 ,  G06F17/18 ,  G06Q30/02 ,  G06F16/242 ,  G06F17/16

Abstract: Systems and methods for model validation includes generating a first and a second time series of segmentation states for a data set representative of a simulated population, e.g., a collection of membership counts corresponding to respective segments of the simulated population. The first and second time series of segmentation states are generated by respectively processing the data set through a first and a second simulation each comprising iterative application of a plurality of event functions. The first and the second simulation differ in at least one capacity, e.g., one including a first event function configured with a first parameter, and the second not. Analysis of differences between the first and second time series may be compared to analysis of one of the time series using a subject model. The comparison is then used to validate the model or demonstrate accuracies, inaccuracies, and/or model bias with respect to a performance metric.

公开(公告)号：US10607254B1

公开(公告)日：2020-03-31

申请号：US15044923

申请日：2016-02-16

Applicant: Google Inc.

Inventor： Stephanie Sapp ,  Stefan F. Schnabl ,  Jon Vaver ,  Ruixue Fan

IPC: G06Q30/02

Abstract: Systems, methods, and computer-readable storage media for attribution modeling using withheld or near impression data are provided. One method involves determining, for a first content item impression, withheld or near impressions for a competing content item within a content auction. The method further involves identifying a first set of paths including a sequence of events that includes an interaction with the first content item impression. The method further involves identifying a second set of paths, each including the sequence of events with the competing content item impression replacing the first content item impression. The method compares conversion metrics for the first and second paths to determine attribution credit for the first content item impression.

公开(公告)号：US09875484B1

公开(公告)日：2018-01-23

申请号：US14186720

申请日：2014-02-21

Applicant: Google Inc.

Inventor： Stefan F. Schnabl ,  Jon Vaver

IPC: G06Q30/00 ,  G06Q30/02

CPC classification number: G06Q30/0242

Abstract: Methods, systems, and apparatus including computer programs encoded on computer-readable storage media are provided for evaluating attribution models and comparing estimates produced by the attribution models with causal measurements from controlled experiments. An attribution model is identified for use in determining an estimate of an effectiveness of a campaign. An experiment is identified including experiment data that reflects implementation of a change in an experiment environment including identifying a measure of effectiveness of the change within a predetermined confidence level. The estimate is evaluated as compared to the identified measure of effectiveness. A determination is made that the attribution model is an effective measure of the change for the campaign, based at least in part on the evaluating.

公开(公告)号：US20170345048A1

公开(公告)日：2017-11-30

申请号：US15627032

申请日：2017-06-19

Applicant: Google Inc.

Inventor： Stefan F. Schnabl ,  Jon Vaver ,  Arjun Satyapal ,  John Huang ,  Wenjie Jiang

IPC: G06Q30/02

CPC classification number: G06Q30/0244 ,  G06Q30/0256

Abstract: The present disclosure includes systems and techniques relating to identifying value marketing activities. In some implementations, an apparatus, systems, or methods can include receiving conversion path information including data relating to user interactions with a content item associated with a marketing activity, determining a first attribution credit by applying a first attribution model to the received information, and a second attribution credit by applying a second attribution model to the received information, determining an attribution contrast ratio based on the first and second attribution credit, identifying an opportunity based on the determined attribution contrast ratio, and presenting a recommendation for the marketing activity based on the identified opportunity.

公开(公告)号：US20190087469A1

公开(公告)日：2019-03-21

申请号：US15707594

申请日：2017-09-18

Applicant: Google Inc.

Inventor： Stephanie Zhang ,  Jon Vaver

IPC: G06F17/30 ,  G06F17/18 ,  G06Q30/02

Abstract: Systems and methods for model validation includes generating a first and a second time series of segmentation states for a data set representative of a simulated population, e.g., a collection of membership counts corresponding to respective segments of the simulated population. The first and second time series of segmentation states are generated by respectively processing the data set through a first and a second simulation each comprising iterative application of a plurality of event functions. The first and the second simulation differ in at least one capacity, e.g., one including a first event function configured with a first parameter, and the second not. Analysis of differences between the first and second time series may be compared to analysis of one of the time series using a subject model. The comparison is then used to validate the model or demonstrate accuracies, inaccuracies, and/or model bias with respect to a performance metric.

公开(公告)号：US09922340B1

公开(公告)日：2018-03-20

申请号：US14250016

申请日：2014-04-10

Applicant: Google Inc.

Inventor： Jon Vaver ,  Stefan F. Schnabl

IPC: G06Q30/00 ,  G06Q30/02

CPC classification number: G06Q30/0242

Abstract: Methods, systems, and apparatus including computer programs encoded on computer-readable storage media are provided for evaluating an attribution model, based on simulated activity streams. Parameters are specified that describe how users behave in the absence and presence of advertising, and parameters that regulate advertising in a simulation. A first set of simulated activity streams is generated with advertising turned on, observational metrics associated with the first set of streams are determined, and an attribution model is applied to determine a first fraction of incremental conversions associated with one or more advertising channels. Further sets of simulated activity streams are generated, each with a single advertising channel turned off, observational metrics associated with the further sets of streams are determined, and a second fraction of incremental conversions is determined for each advertising channel. The first and second fractions of incremental conversions are compared to evaluate the attribution model.