Abstract: Systems and methods are disclosed for clustering multiple devices that are associated with particular users by utilizing both probabilistic and deterministic data derived from analytics information on the users. An analytics computing system generates at least one deterministic device cluster that groups a first set of devices associated with a first user. The first set of devices share deterministic user identifiers specific to the first user. The analytics computing system also identifies a probabilistic link between a device in the first set of devices and additional devices. The probabilistic link indicates common usage patterns between two devices. Based on the probabilistic link, the analytics computing system generates a data structure that includes the deterministic device cluster and the additional devices.

Abstract: A method for clustering geolocations using geo-point density includes receiving a user log of geolocation data extracted from user interactions with at least one electronic device. A density is determined relative to other geo-points for each geo-point in a set of geo-points extracted from the user log. Lower density geo-points in the set are merged into higher density geo-points in the set to result in a merged set of geo-points, and clusters of geo-points are identified from the merged set. Merging the geo-points tends to preserve frequently occurring geo-points while reducing those that constitute noise, which improves the reliability of identifying the clusters. Core geo-points of the user log are selected from the clusters. The core geo-points of the user log can be compared to core geo-points of other use logs to identify associations between the user logs.

Abstract: Embodiments of the present invention relate to identifying website visitors. Initially, a predictor is trained with a set of data of known website visitors to identify a rule with the highest effectiveness score. To do so, each rule in a set of rules is applied to all cookies in the set of data. Based on a selected goal of identifying unknown website visitors, the rule with the highest effectiveness score is identified. To identify a cookie of an unknown website visitor, a cookie representation corresponding to the cookie is identified. The cookie representation represents the cookie as an n-dimensional vector of features and is computed using hit statistics for various aspects of the cookie. Utilizing the cookie representation, a cookie-stitching rule is selected and applied to the cookie. In this way, a website visitor associated with the cookie can be identified.

Abstract: Embodiments of the present invention relate to identifying website visitors. Initially, a predictor is trained with a set of data of known website visitors to identify a rule with the highest effectiveness score. To do so, each rule in a set of rules is applied to all cookies in the set of data. Based on a selected goal of identifying unknown website visitors, the rule with the highest effectiveness score is identified. To identify a cookie of an unknown website visitor, a cookie representation corresponding to the cookie is identified. The cookie representation represents the cookie in a numeric vector space and can computed based on hits in log data and a selection of variables. Utilizing the cookie representation, a cookie-stitching rule is selected and applied to the cookie. In this way, a website visitor associated with the cookie can be identified.

Abstract: Systems and methods are disclosed for clustering multiple devices that are associated with particular users by utilizing both probabilistic and deterministic data derived from analytics information on the users. An analytics computing system generates at least one deterministic device cluster that groups a first set of devices associated with a first user. The first set of devices share deterministic user identifiers specific to the first user. The analytics computing system also identifies a probabilistic link between a device in the first set of devices and additional devices. The probabilistic link indicates common usage patterns between two devices. Based on the probabilistic link, the analytics computing system generates a data structure that includes the deterministic device cluster and the additional devices.

Abstract: Network-based probabilistic device linking techniques are described that link multiple devices associated with a common entity. In one example, log records are received from service providers including a device identifier and an IP address associated with a computing device that uses the service providers to access resources. The received log records are filtered and analyzed to identify connection frequencies between each device identifier and various IP addresses. Connection frequencies are scored and used to identify a subset of connections for computing linked devices belonging to a common entity, such as a single user, a household of users, users in a specific location, and so on. Linked devices are computed from the subset of selected connections and combined into linked device clusters. These linked device clusters can then be output so that market analysis can be performed on the linked device cluster rather than data pertaining to a single device.

Abstract: Sales Forecasting using Browsing Ratios and Browsing Durations is described. In one or more implementations, browsing ratios representative of how much users visited webpages associated with a product or service, and browsing durations representing how much time users spent visiting the webpages associated with the product or service are determined. Based on the determined browsing ratios and browsing durations, a sales forecast of the product or service can be accurately determined.

Abstract: A system and method for providing query results with visual cues is described. The visual cues, such as variation in color, typography (e.g., size, spacing, etc.), layout, etc., indicate relevance of a query result relative to other displayed results. The visual appearance of the query results may be determined based on a distribution of scores associated with the query results. Generally, the more a particular score deviates from a chosen statistical measure (e.g., mean, median, mode, etc.), the more distinct the visual appearance of the associated query result is from the other results. In this way, unlike with previous techniques, query results are presented to a user in an intuitive way that allows the user to distinguish between highly relevant and less relevant results.

Abstract: Methods and systems are described that automatically organize directory hierarchies and to label individual directories systematically. Upon a number of files in a first directory exceeding a maximum number of files, a second directory is created. The files formerly disposed only in the first directory are organized into both of the first directory and the second directory so that the threshold number of files is not exceeded in either of the first or second directories. Organizing the files into the first and second directories uses vector representations of each of the files generated by the system so that, when organized, the first and second directories each include files with similar content. Labels are selected for each of the directories based on a comparison between a vector representation of the collective contents of each directory and vector representations of titles in a database.

Abstract: A method for clustering geolocations using geo-point density includes receiving a user log of geolocation data extracted from user interactions with at least one electronic device. A density is determined relative to other geo-points for each geo-point in a set of geo-points extracted from the user log. Lower density geo-points in the set are merged into higher density geo-points in the set to result in a merged set of geo-points, and clusters of geo-points are identified from the merged set. Merging the geo-points tends to preserve frequently occurring geo-points while reducing those that constitute noise, which improves the reliability of identifying the clusters. Core geo-points of the user log are selected from the clusters. The core geo-points of the user log can be compared to core geo-points of other use logs to identify associations between the user logs.

Abstract: Embodiments of the present invention relate to identifying website visitors. Initially, a predictor is trained with a set of data of known website visitors to identify a rule with the highest effectiveness score. To do so, each rule in a set of rules is applied to all cookies in the set of data. Based on a selected goal of identifying unknown website visitors, the rule with the highest effectiveness score is identified. To identify a cookie of an unknown website visitor, a cookie representation corresponding to the cookie is identified. The cookie representation represents the cookie in a numeric vector space and can computed based on hits in log data and a selection of variables. Utilizing the cookie representation, a cookie-stitching rule is selected and applied to the cookie. In this way, a website visitor associated with the cookie can be identified.

Abstract: An analytics server receives data characterizing consumer interactions that are observed by a cross-section of data providers, which may include, for example, website administrators, campaign managers, application developers, and the like. Such observational data includes device and login identifiers for a particular interaction, and optionally, timestamp information indicating when the interaction occurred. A statistical device graph model is generated based on this observational data. The statistical device graph model allows inferences to be drawn with respect to whether a given device is a private device, a shared device, or a public device. This, in turn, allows private devices which are “owned” by a single consumer to be identified. Depending on the type of observational data collected by the data providers, a wide range of additional insights can be drawn from the statistical device graph model, including for example, device usage patterns and confidence levels.

Abstract: The present disclosure is directed toward systems and methods for determining influence of users within a social media community for marketing purposes. For example, systems and methods described herein involve identifying terms of a marketing campaign and accessing a user context for a user. The systems and methods further involve determining a popularity metric and a relevance metric for the user based on the terms of the marketing campaign and the user context. Further, the systems and methods involve determining an influence level of the user for the marketing campaign based on the popularity metric and the relevance metric.