Abstract: A system, method, apparatus, and processor readable non-transitive storage media are described for matching items in large datasets based on non-invasive fingerprints of users so that collected metric data for advertisements (media) and behavioral data may be reconciled and analyzed. Since user fingerprints may not generate a unique one to one correspondence or mapping under certain constraints, the various embodiments employ a sampling method that optimally matches the output of a random sampling of non-invasive fingerprints. The use of non-invasive fingerprints and specialized sampling enables the various embodiments to provide advanced analytics for advertising content and metric data in targeted behavior advertising campaigns. To compare impressions with user profile data, the various embodiments employ in part the time stamp dimension of user profiles to generate temporally unique persistent non-invasive fingerprints.

Abstract: Embodiments are directed towards an advertising pricing system that uses striped aggressive discounting and shared audience auctions. A plurality of stripes is determined for an audience, where at least one stripe is assigned a different discount. Each user in the audience is associated with one of the stripes. All audience data can then be distributed to and shared between data buyers; thus, each data buyer can use the audience independent of other data buyers. In one embodiment, data buyers can provide content to individual users regardless of the stripe that is associated with the user. Data buyers can then be billed based on the stripe discount associated with each user that the data buyer provided content. In one embodiment, the number, size, and discount associated with each stripe may be modified to dynamically produce an optimum yield based on shared audience auction bidding volume using striped aggressive discounting.

Abstract: A system, method, apparatus, and processor readable storage media are described for real-time computation of statistics between audiences including, but not limited to, correlations, comparative metrics, and/or other statistical analysis for the audiences. Embodiments enable a user to specify a query that defines a specific audience for analysis in real-time. A past time period may be determined that indicates the amount of historical data that is to be analyzed to generate the statistical comparisons and/or correlations. Employing the user-specified query, statistical comparisons and/or correlation may be provided, based on an analysis of retrieved historical audience data for the time period. Embodiments may also enable a user to generate new statistics in real-time through modification of the audience in one or more iterations. Embodiments may also enable a user to filter, sort, and search through audiences by setting desired statistical boundaries for the resulting audience set.

Abstract: A system, method, apparatus and processor readable media are described for real-time prediction of an advertising audience volume through analysis of historical audience data, and tuning of the predicted audience volume. Embodiments enable a user to specify a query for audience volume prediction. Such a query may be a Boolean combination of various audience categories. A time range may be determined that indicates the amount of historical data that is to be analyzed to make the audience volume prediction in real time. Employing the user-specified query, an audience volume prediction may be provided for a future time period, based on an analysis of retrieved historical audience data for the time range. Embodiments may also enable a user to tune the predicted audience volume through modification of the query through one or more iterations.

Abstract: A method, system, apparatus, and storage medium for determining that a client device is enabled for persistent identification, and that may operate as a filter for user information based on whether the client is enabled for persistent identification. A first communication is received regarding a request by the client for a web page. At least one application level data structure is then provided for storage by the client, that persistently identifies at least one characteristic of the client. A second communication is then received that includes a representation of the application level data structures provided, and a comparison is made between the data structures provided and the representation received to determine whether the client is enabled for persistent identification. If persistent identification is enabled, the client may then be provided with advertisements or other content related to the client interests or characteristics.