Abstract: Segment valuation techniques usable in a digital medium environment are described. To do so, a segment valuation system first identifies the attributes that are significant in achievement of a desired metric (e.g., conversion) and then values segments based on those significant attributes. Attributes are selected from the trained model based on significance of those attributes towards achieving the desired metric. A valuation of a segment may then be calculated based on the valuations of these attributes. For example, inclusion of the selected attributes within a segment, and the valuations of those selected attributes, is then used by the segment valuation system to generate data describing a value of the segment towards achieving the metric.

Abstract: User data overlap determination in a digital medium environment is described. Initially, a user selects segments of user data for which a determination of overlap is to be made. For example, the user selects a segment representing users that are working professionals and a segment representing users that are mothers, such that working-mother users may correspond to the overlap. Regardless of the particular segments selected, an indication of those segments is received. One of multiple different overlap determining techniques—which can include a combined MinHash and HyperLogLog (HLL) technique and an Inclusion-Exclusion technique—may be selected for computing the overlap based on a number of segments indicated and numbers of users represented by the segments. The selected overlap determining technique is then used to compute the user data overlap between the indicated segments. Digital content including values indicative of the determined overlap is generated for presentation to a user.

Abstract: Segment valuation techniques usable in a digital medium environment are described. To do so, a segment valuation system first identifies the attributes that are significant in achievement of a desired metric (e.g., conversion) and then values segments based on those significant attributes. Attributes are selected from the trained model based on significance of those attributes towards achieving the desired metric. A valuation of a segment may then be calculated based on the valuations of these attributes. For example, inclusion of the selected attributes within a segment, and the valuations of those selected attributes, is then used by the segment valuation system to generate data describing a value of the segment towards achieving the metric.

Abstract: Segment valuation techniques usable in a digital medium environment are described. To do so, a segment valuation system first identifies the attributes that are significant in achievement of a desired metric (e.g., conversion) and then values segments based on those significant attributes. Attributes are selected from the trained model based on significance of those attributes towards achieving the desired metric. A valuation of a segment may then be calculated based on the valuations of these attributes. For example, inclusion of the selected attributes within a segment, and the valuations of those selected attributes, is then used by the segment valuation system to generate data describing a value of the segment towards achieving the metric.

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: User data overlap determination in a digital medium environment is described. Initially, a user selects segments of user data for which a determination of overlap is to be made. For example, the user selects a segment representing users that are working professionals and a segment representing users that are mothers, such that working-mother users may correspond to the overlap. Regardless of the particular segments selected, an indication of those segments is received. One of multiple different overlap determining techniques—which can include a combined MinHash and HyperLogLog (HLL) technique and an Inclusion-Exclusion technique—may be selected for computing the overlap based on a number of segments indicated and numbers of users represented by the segments. The selected overlap determining technique is then used to compute the user data overlap between the indicated segments. Digital content including values indicative of the determined overlap is generated for presentation to a user.

Abstract: Segment valuation techniques usable in a digital medium environment are described. To do so, a segment valuation system first identifies the attributes that are significant in achievement of a desired metric (e.g., conversion) and then values segments based on those significant attributes. Attributes are selected from the trained model based on significance of those attributes towards achieving the desired metric. A valuation of a segment may then be calculated based on the valuations of these attributes. For example, inclusion of the selected attributes within a segment, and the valuations of those selected attributes, is then used by the segment valuation system to generate data describing a value of the segment towards achieving the metric.