Abstract: Methods and apparatus for finding similar on-line users for advertisement or content targeting are disclosed. In one embodiment, a plurality of user data sets associated with a plurality of user identifiers for a plurality of anonymous users are obtained, and each user data set of each user identifier specifies one or more user attributes and on-line user events that have occurred for such user identifier. For each attribute, a correlation to a success metric value is determined for a particular type of event or attribute that has occurred for a plurality of user identifiers that are each associated with such attribute. The plurality of user identifiers and associated data sets are clustered into a plurality of user groups that each has similar data sets by weighting based on the attributes' relative correlation to the success metric.

Abstract: A system and method to identify targeting criteria for online advertising campaigns based on natural query event data are described. A request to determine targeting criteria is received from an advertiser entity. Natural or algorithmic query event data related to the advertiser entity is retrieved from a data storage module. Attribute data associated with targeted customers is extracted based on the retrieved natural query event data. Finally, the attribute data is further processed to determine a set of criteria to target advertisements to the customers.

Abstract: Within a computer networking environment, a first bid request having data is received. Bids are determined for advertisements based on the data associated with such first bid request. It may be determined that the first bid request's data includes one or more specific types of data for a bonus variable. At least a first portion of the bids that were determined for the first bid request are increased by a bonus amount in response to the one or more specific types of data for a bonus variable being determined to be present in the first bid request's data. For the first bid request, an optimum set of one or more of the bids are selected and transmitted, along with a reference to the associated advertisement, via to a sender of the first bid request.

Abstract: A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.

Abstract: A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, is model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.

Abstract: Within a computer networking environment, a first bid request having data is received. Bids are determined for advertisements based on the data associated with such first bid request. It may be determined that the first bid request's data includes one or more specific types of data for a bonus variable. At least a first portion of the bids that were determined for the first bid request are increased by a bonus amount in response to the one or more specific types of data for a bonus variable being determined to be present in the first bid request's data. For the first bid request, an optimum set of one or more of the bids are selected and transmitted, along with a reference to the associated advertisement, via to a sender of the first bid request.

Abstract: Methods and apparatus for finding similar on-line users for advertisement or content targeting are disclosed. In one embodiment, a plurality of user data sets associated with a plurality of user identifiers for a plurality of anonymous users are obtained, and each user data set of each user identifier specifies one or more user attributes and on-line user events that have occurred for such user identifier. For each attribute, a correlation to a success metric value is determined for a particular type of event or attribute that has occurred for a plurality of user identifiers that are each associated with such attribute. The plurality of user identifiers and associated data sets are clustered into a plurality of user groups that each has similar data sets by weighting based on the attributes' relative correlation to the success metric.

Abstract: A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.

Abstract: A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.

Abstract: A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, is model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.

Abstract: Methods and apparatus for finding similar on-line users for advertisement or content targeting are disclosed. In one embodiment, a plurality of user data sets associated with a plurality of user identifiers for a plurality of anonymous users are obtained, and each user data set of each user identifier specifies one or more user attributes and on-line user events that have occurred for such user identifier. For each attribute, a correlation to a success metric value is determined for a particular type of event or attribute that has occurred for a plurality of user identifiers that are each associated with such attribute. The plurality of user identifiers and associated data sets are clustered into a plurality of user groups that each has similar data sets by weighting based on the attributes' relative correlation to the success metric.

Abstract: Systems and methods for determining a type of a digital ad are disclosed. Generally, an ad type determination module automatically determines a type of a digital ad, such as a direct response ad or a branding ad, based on at least one of (1) a number of hyperlinks associated with the digital ad, (2) a number of click throughs associated with the digital ad, (3) a click-through rate associated with the digital ad, and (4) an industry associated with the digital ad. After the ad type determination module determines a type of the digital ad, an indication of the type of digital ad may be exported to a system of an online advertisement service provider so that the online advertisement service provider may perform actions such as optimizing the digital ad, or generating reports regarding the digital ad, based on the determined type of the digital ad.

Abstract: A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.

Abstract: A system and method to identify targeting criteria for online advertising campaigns based on natural query event data are described. A request to determine targeting criteria is received from an advertiser entity. Natural or algorithmic query event data related to the advertiser entity is retrieved from a data storage module. Attribute data associated with targeted customers is extracted based on the retrieved natural query event data. Finally, the attribute data is further processed to determine a set of criteria to target advertisements to the customers.

Abstract: Within a computer networking environment, bid requests having data are received at a communication interface of a bid determination platform. Bids for a plurality of advertisements are determined via a computer processor of the bid determination platform based on the data associated with such bid request. Via the computer processor, at least a first portion of the bids that were determined for one or more of the bid requests are decreased based on one or more quantitative and qualitative factors, such as missing variable data, of the one or more bid request's associated data. For each bid request, an optimum set of one or more of the determined bids is selected and transmitted, along with a reference to the associated advertisement, via the communication interface to a sender of the bid request.

Abstract: A system and method to identify targeting criteria for online advertising campaigns based on natural query event data are described. A request to determine targeting criteria is received from an advertiser entity. Natural or algorithmic query event data related to the advertiser entity is retrieved from a data storage module. Attribute data associated with targeted customers is extracted based on the retrieved natural query event data. Finally, the attribute data is further processed to determine a set of criteria to target advertisements to the customers.

Abstract: A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.

Abstract: A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.

Abstract: A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.

Abstract: A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.