公开(公告)号：US20190138643A1

公开(公告)日：2019-05-09

申请号：US15804012

申请日：2017-11-06

Applicant: Adobe Systems Incorporated

Inventor： Shiv Kumar Saini ,  Sunav Choudhary ,  Gaurush Hiranandani

IPC: G06F17/30

Abstract: Certain embodiments involve extracting seasonal, level, and spike components from a time series of metrics data, which describe interactions with an online service over a time period. For example, an analytical system decomposes the time series into latent components that include a seasonal component series, a level component series, a spike component series, and an error component series. The decomposition involves configuring an optimization algorithm with a constraint indicating that the time series is a sum of these latent components. The decomposition also involves executing the optimization algorithm to minimize an objective function subject to the constraint and identifying, from the executed optimization algorithm, the seasonal component series, the level component series, the spike component series, and the error component series that minimize the objective function. The analytical system outputs at least some latent components for anomaly-detection or data-forecasting.

公开(公告)号：US20170111432A1

公开(公告)日：2017-04-20

申请号：US14886453

申请日：2015-10-19

Applicant: Adobe Systems Incorporated

Inventor： Shiv Kumar Saini ,  Ritwik Sinha ,  Michael Rimer ,  Anandhavelu N

IPC: H04L29/08 ,  G06F11/34 ,  G06Q30/02

CPC classification number: H04L67/025 ,  G06F11/3409 ,  G06Q30/0201 ,  H04L41/142 ,  H04L43/08 ,  H04L67/02 ,  H04L67/22

Abstract: The present disclosure is directed toward systems and methods for identifying contributing factors associated with a multi-variable metric anomaly. One or more embodiments described herein identify one or more contributing factors that led to an anomaly in a multi-variable metric by calculating linearizing weights such that the total deviation in the multi-variable metric can be written as a weighted sum of deviations for dimension elements associated with the multi-variable metric.

公开(公告)号：US20160117389A1

公开(公告)日：2016-04-28

申请号：US14526149

申请日：2014-10-28

Applicant: Adobe Systems Incorporated

Inventor： Shiv Kumar Saini ,  Ritwik Sinha ,  Burhanuddin Iftikhar Ahamath ,  John B. Bates

IPC: G06F17/30

CPC classification number: G06F17/30864 ,  G06F17/3089

Abstract: Techniques for automatic identification of sources of web metric changes are described. In one or more implementations, changes in a web metric that indicate a measurable attribute associated with a website are determined, and the web metric is analyzed to identify sources that contributed to the changes in the web metric. In implementations, data is queried to obtain actual values for dimension elements along one or more dimensions of the web metric. In addition, expected values for the dimension elements are estimated along the dimensions of the web metric based on historical data. Then, deviations between the actual values and the expected values are calculated by using comparable statistics. Subsequently, the comparable statistics can be analyzed to identify corresponding dimension elements as the sources that contributed to the changes in the web metric.

Abstract translation: 描述了用于自动识别web度量变化的源的技术。 在一个或多个实现中，确定指示与网站相关联的可测量属性的web度量中的变化，并且分析web度量以识别有助于web度量中的变化的源。 在实现中，查询数据以获取沿web度量的一个或多个维度的维度元素的实际值。 此外，维度元素的预期值是根据历史数据沿着Web度量的维度进行估计的。 然后，通过使用可比较的统计量计算实际值和预期值之间的偏差。 随后，可以分析可比较的统计数据，以将相应的维度元素确定为有助于Web度量变更的来源。

公开(公告)号：US10242101B2

公开(公告)日：2019-03-26

申请号：US14526149

申请日：2014-10-28

Applicant: Adobe Systems Incorporated

Inventor： Shiv Kumar Saini ,  Ritwik Sinha ,  Iftikhar Ahamath Burhanuddin ,  John B. Bates

IPC: G06F17/30

Abstract: Techniques for automatic identification of sources of web metric changes are described. In one or more implementations, changes in a web metric that indicate a measurable attribute associated with a website are determined, and the web metric is analyzed to identify sources that contributed to the changes in the web metric. In implementations, data is queried to obtain actual values for dimension elements along one or more dimensions of the web metric. In addition, expected values for the dimension elements are estimated along the dimensions of the web metric based on historical data. Then, deviations between the actual values and the expected values are calculated by using comparable statistics. Subsequently, the comparable statistics can be analyzed to identify corresponding dimension elements as the sources that contributed to the changes in the web metric.

公开(公告)号：US20170213237A1

公开(公告)日：2017-07-27

申请号：US15005205

申请日：2016-01-25

Applicant: Adobe Systems Incorporated

Inventor： Meghanath Macha Yadagiri ,  Ritwik Sinha ,  Shiv Kumar Saini

IPC: G06Q30/02 ,  G06Q10/06

CPC classification number: G06Q30/0244 ,  G06Q10/067

Abstract: Techniques for managing a marketing campaign of a marketer are described. In an example, the marketing campaign uses multiple marketing channels. Attribution of each marketing channel to a user conversion is estimated. Usage of a marketing channel within the marketing campaign is set according to the respective attribution. A marketing channel attribution model is selected from candidate marketing channel attribution models and is applied to estimate the attributions. The selection is based on the accuracy of each of the models associated with estimating the attributions given a set of parameters. To evaluate the accuracy, user journeys are simulated given the set of parameters. True attributions of each marketing channel are determined from the simulation. Each of the marketing channel attribution models is also applied to the simulation to generate estimated attributions. The true and estimated attributions are compared to derive the accuracies.

公开(公告)号：US20160098735A1

公开(公告)日：2016-04-07

申请号：US14508141

申请日：2014-10-07

Applicant: ADOBE SYSTEMS INCORPORATED

Inventor： Ritwik Sinha ,  Shiv Kumar Saini ,  Anandhavelu N

IPC: G06Q30/02

CPC classification number: G06Q30/0202 ,  G06Q30/0241

Abstract: Techniques are disclosed for evaluating the incremental effect of a marketing channel that forms part of a multichannel marketing campaign. In one implementation data characterizing observed marketing interactions and outcomes is collected. A conversion probability is estimated as a function of the observed interactions using logistic regression techniques, wherein converting and non-converting consumers comprise the two classes upon which the regression is based. As a result, marketing interactions that are relatively more commonplace amongst converting consumers (as compared to non-converting consumers) receive greater attribution for observed conversions. The estimated conversion probability is then used to predict an incremental quantity of conversions that can be attributed to a kth marketing channel based on the average treatment effect. Based on these predictions, it is possible to evaluate the extent to which market segment variables influence how attribution is distributed amongst various marketing channels.

Abstract translation: 披露技术来评估构成多渠道营销活动一部分的营销渠道的增量效应。 在一个实现中，收集了观察到的营销相互作用和结果的特征。 使用逻辑回归技术估计转换概率作为所观察到的相互作用的函数，其中转换和非转换消费者包括基于回归的两个类别。 因此，在转换消费者（与非转换消费者相比）相对较为常见的营销互动对于观察到的转化而言具有更大的归因。 然后，估算的转换概率用于根据平均处理效果来预测可归因于第k个营销渠道的增量转化量。 基于这些预测，有可能评估市场细分变量在各种营销渠道中如何分配归属分布的程度。

公开(公告)号：US20180039898A1

公开(公告)日：2018-02-08

申请号：US15228570

申请日：2016-08-04

Applicant: Adobe Systems Incorporated

Inventor： Shiv Kumar Saini ,  Natwar Modani ,  Balaji Vasan Srinivasan

IPC: G06N5/04 ,  G06F17/30

CPC classification number: G06N20/00 ,  G06F11/3447

Abstract: In various implementations, a method includes receiving a set of time series data that corresponds to a metric. A seasonal pattern is extracted from the set of time series data and the extracted seasonal pattern is filtered from the set of time series data. A predictive model is generated from the filtered set of data. The extracted seasonal pattern is filtered from another set of time series data where the second set of time series data corresponds to the metric. The filtered second set of time series data is compared to the predictive model. An alert is generated to a user for a value within the filtered second set of time series data which falls outside of the predictive model.

公开(公告)号：US20180374008A1

公开(公告)日：2018-12-27

申请号：US15633854

申请日：2017-06-27

Applicant: Adobe Systems Incorporated

Inventor： Atanu Ranjan Sinha ,  Shiv Kumar Saini

IPC: G06Q10/06 ,  G06Q30/02

Abstract: A computer based decision simulation tool system that includes data storage containing sales data for a plurality of products in a product line of a single brand. The sales data is organized to include quantity sold, selling price and sale date of a product over a period of time, at a predetermined level of temporal granularity. A processor is operatively coupled to the storage, and the processor is configured to execute instructions that when executed cause the processor to retrieve selected portions of the sales data. The processor operates to identify dependencies among products within the product line to generate a cross-product price elasticity that is indicative of percentage change in quantity sold of a focal product with respect to one percentage change in price of a different product in the product line. The process further operates to respond to user inputs to provide visual indications of the cross-product price elasticity.

公开(公告)号：US20170004511A1

公开(公告)日：2017-01-05

申请号：US14788576

申请日：2015-06-30

Applicant: Adobe Systems Incorporated

Inventor： Shiv Kumar Saini ,  Tushar Mehndiratta ,  Surya Pratap Singh Tanwar ,  Dhruv Anand

IPC: G06Q30/02 ,  G06F17/30 ,  H04L29/08

CPC classification number: G06Q30/0201 ,  H04L67/02 ,  H04L67/22

Abstract: In one or more implementations, data is obtained for metrics that describe visitor interaction with a web site. From these metrics, a user selection is received of a metric-of-interest, which describes a particular visitor interaction with the website. The user selection indicates that driving metrics, which describe visitor interaction that is determined to be influential in causing the particular visitor interaction, are to be identified. Once the metric-of-interest is selected, the data obtained for the website is processed to identify the driving metrics. The processing involves application of a feature selection technique to ascertain candidate driving metrics from the metrics for which the data is obtained. The candidate driving metrics are the metrics likely to be influential in causing the metric-of-interest. The processing also involves application of a statistical causality technique to determine whether the candidate driving metrics are influential in causing the metric-of-interest. The candidate driving metrics that are determined to be influential in causing the metric-of-interest are identified as the driving metrics. A graphical user interface is then generated to present the driving metrics to a user.

Abstract translation: 在一个或多个实现中，获得用于描述访客与网站交互的度量的数据。 从这些指标中，收到用户选择，其中描述了与网站的特定访客交互的兴趣度量。 用户选择指示将识别描述访问者交互被确定为影响特定访问者交互的影响的驾驶指标。 一旦选择了兴趣度量，就会处理为网站获取的数据，以确定驾驶指标。 处理涉及应用特征选择技术以从获得数据的度量来确定候选驾驶度量。 候选人驾驶指标是导致感兴趣度量可能有影响力的指标。 该处理还涉及应用统计因果技术来确定候选驾驶指标是否有影响力来引起兴趣度量。 被确定为对感兴趣度量有影响力的候选驾驶指标被确定为驾驶指标。 然后生成图形用户界面以向用户呈现驾驶量度。

公开(公告)号：US20180349756A1

公开(公告)日：2018-12-06

申请号：US15609254

申请日：2017-05-31

Applicant: Adobe Systems Incorporated

Inventor： Shiv Kumar Saini ,  Prakhar Gupta ,  Harvineet Singh ,  Gaurush Hiranandani

IPC: G06N3/04 ,  G06N7/00

Abstract: Techniques of forecasting web metrics involve generating, prior to the end of a period of time, a probability of a metric taking on an anomalous value, e.g., a value indicative of an anomaly with respect to web traffic, at the end of the period based on previous values of the metric. Such a probability is based on a distribution of predicted values of the metric at some previous period of time. For example, a web server may use actual values of the number of bounces collected at hourly intervals in the middle of a day to predict a number of bounces at the end of the current day. Further, the web server may also compute a confidence interval to determine whether a predicted end-of-day number of bounces may be considered anomalous. The width of the confidence interval indicates the probability that a predicted end-of-day number of bounces has an anomalous value.