Abstract: Persistent profile identifiers can be produced to identify clusters of devices accessing a network in different time periods. In one embodiment, an apparatus uses a first identifier from a first group of identifiers to identify a first cluster of devices and uses a second identifier from a second group of identifiers to identify a second cluster of devices. Further, the apparatus determines that the first cluster of devices identified by the first identifier and the second cluster of devices identified by the second identifier form an edge in a maximum cluster matching. The apparatus provides the first identifier as a persistent identifier for the first cluster of devices and the second cluster of devices.

Abstract: Systems, methods, and non-transitory computer-readable media (systems) are disclosed for generating meaningful and insightful user segment reports based on a high dimensional data space. In particular, in one or more embodiments, the disclosed systems utilize a relaxed bi-clustering model to automatically identify user segments in a data space including datasets of features specific to individual users. For example, the disclosed systems identify and include users in automatically generated user segments even though those users are associated with some, but perhaps not all, of the features as other members in the automatically generated user segments.

Abstract: Systems and techniques are disclosed for creating segments of users that include baseline users having specified traits and users that are similar to the baseline users. A segment is created by identifying baseline users based on a segment rule that specifies one or more traits of the users to include. The data about the baseline and other users in the dataset is used to extend the segment. A representation of the segment is determined, for example, by determining average values of numeric traits and frequencies of non-numeric trait values of the baseline users in the segment. The representation of the segment is used to determine the similarity (i.e., similarity scores) of users to the segment and ultimately to determine which of the other users, who are not already included in the segment, should be included in the segment based the similarity of their traits to those of the segment representation.

Abstract: Techniques are disclosed that provide more accurate clustering of devices by forming clusters of devices and merging or changing clusters based on predetermined criteria. The technique starts with a large number of clusters (e.g., one for each account) and refines the clusters, for example, by merging clusters or determining which cluster a given device should be in when the device is associated with multiple clusters. One technique iteratively adjusts clusters of devices by merging clusters determined to be associated with a single user until a cluster contains all of the devices and accounts expected to be associated with a single user.

Abstract: Techniques are disclosed that provide more accurate clustering of devices by forming clusters of devices and merging or changing clusters based on predetermined criteria. The technique starts with a large number of clusters (e.g., one for each account) and refines the clusters, for example, by merging clusters or determining which cluster a given device should be in when the device is associated with multiple clusters. One technique iteratively adjusts clusters of devices by merging clusters determined to be associated with a single user until a cluster contains all of the devices and accounts expected to be associated with a single user.

Abstract: Systems and methods are provided for identifying stable attributes in clusters of devices and naming the clusters of devices using the stable attributes. The stable attribute is selected from data associated with user/household behavior and data associated with each device. Because the stable attribute is associated with the device in the cluster determined to be the most stable, it is a persistent identifier that can be used to identify the user/household over time. Because the stable attribute is determined each time the clusters are determined, resources that would be required to track the user/household over time are saved and the system of tracking users/households is more efficient. Attributes evaluated to determine the stable attribute include IP address, the location of the device, hardware characteristics of the device, the date a CRM profile was created, timestamp of the first or last user authentication, and the number of authentications on that device.

Abstract: Techniques are disclosed that provide more accurate clustering of devices by forming clusters of devices and merging or changing clusters based on predetermined criteria. The technique starts with a large number of clusters (e.g., one for each account) and refines the clusters, for example, by merging clusters or determining which cluster a given device should be in when the device is associated with multiple clusters. One technique iteratively adjusts clusters of devices by merging clusters determined to be associated with a single user until a cluster contains all of the devices and accounts expected to be associated with a single user.

Abstract: Systems and methods are provided for identifying stable attributes in clusters of devices and naming the clusters of devices using the stable attributes. The stable attribute is selected from data associated with user/household behavior and data associated with each device. Because the stable attribute is associated with the device in the cluster determined to be the most stable, it is a persistent identifier that can be used to identify the user/household over time. Because the stable attribute is determined each time the clusters are determined, resources that would be required to track the user/household over time are saved and the system of tracking users/households is more efficient. Attributes evaluated to determine the stable attribute include IP address, the location of the device, hardware characteristics of the device, the date a CRM profile was created, timestamp of the first or last user authentication, and the number of authentications on that device.

Abstract: Persistent profile identifiers can be produced to identify clusters of devices accessing a network in different time periods. In one embodiment, an apparatus uses a first identifier from a first group of identifiers to identify a first cluster of devices and uses a second identifier from a second group of identifiers to identify a second cluster of devices. Further, the apparatus determines that the first cluster of devices identified by the first identifier and the second cluster of devices identified by the second identifier form an edge in a maximum cluster matching. The apparatus provides the first identifier as a persistent identifier for the first cluster of devices and the second cluster of devices.

Abstract: Certain embodiments involve matching devices that access online services with users or households using a combination of real-time data and batch-processed data about the devices. For example, a data management system generates a device cluster that identifies devices associated with a user or household. The device cluster is generated by batch-processing data received from devices accessing the online services and data received from third parties that describes devices that have accessed other online services. The data management system subsequently identifies a device that is accessing a first online service. The data management system matches the identified device to the device cluster based on a combination of the batch-processed data and data about the identified device received in real-time from a second online service. The identified device is matched to the device cluster while the identified device is accessing the online service.