Techniques for detecting anomalies in live marketing campaign data are disclosed, including: obtaining baseline data associated with one or more digital marketing campaigns; configuring an anomaly detection model to detect anomalies in digital marketing data, based at least on the baseline data; receiving a live stream of a set of digital marketing data associated with a particular digital marketing campaign that is currently being executed; while the particular digital marketing campaign is being executed: applying the anomaly detection model to the set of digital marketing data, to determine if the set of digital marketing data includes an anomaly relative to the baseline data; prior to completion of the particular digital marketing campaign and responsive to determining that the set of digital marketing data includes the anomaly relative to the baseline data, executing an action to address the anomaly.

A system and method to analyze the effectiveness of advertisements of presented content. The advertisements include advertisements for which the behavior desired by the advertiser is to drive a viewer to perform a certain action, such as to watch particular television program content. The system determines if an advertisement is effective by correlating exposure to presented advertisements with the subsequent presentation of content that was promoted in the advertisements. To perform the analysis, the system collects tune data from set top boxes, digital video recorders (DVRs), or other component capable of storing tune data related to the content presented on an associated display component.

Content creation and deployment collaboration techniques are described. In one or more implementations, metadata that describes the creation of the content may be associated with the content. The content may then be provided from a content creation service to a content deployment service for deployment as part of a marketing activity. Deployment data obtained from this tracking may be utilized to support a variety of functionality, such as by content creators to determine which of their content has been successfully employed as part of marketing activities, marketers may also use knowledge of the deployment of the content to choose content to be included in a marketing activity as well as select content creators that are best suited to provide this content, used to configure badges, by retailers and manufacturers, and so forth.

Content creation and deployment collaboration techniques are described. In one or more implementations, metadata that describes the creation of the content may be associated with the content. The content may then be provided from a content creation service to a content deployment service for deployment as part of a marketing activity. Deployment data obtained from this tracking may be utilized to support a variety of functionality, such as by content creators to determine which of their content has been successfully employed as part of marketing activities, marketers may also use knowledge of the deployment of the content to choose content to be included in a marketing activity as well as select content creators that are best suited to provide this content, used to configure badges, by retailers and manufacturers, and so forth.

An online system predicts, using a first targeting model, a first group of users as candidates to be in a targeting cluster, and predicts, using a second targeting model, a second group of users as candidates to be in the targeting cluster. The online system determines a first set of users that are not part of the first group of users, and a second set of users that are not part of the second group of users, and provides surveys to the first and second set of users. The online system determines a first subgroup of the first group of users and a second subgroup of the second group of users, and provides an ad preferences tool to the first subgroup and the second subgroup. The online system scores the first and second targeting models based in part on responses to the surveys and/or the ad preferences tools.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for an object grouping system that obtains data for multiple sketches that are each stored using a set of registers and are a sampling of objects in a dataset. Each object in the dataset is a member of a digital audience. For each sketch, the system uses an identifier for a first object to generate a hashed parameter. The system determines whether the hashed parameter contributes to describing demographic attributes of the sampling of objects. The system stores demographic attributes of the first object at a register in the set when it determines that the hashed parameter contributes to describing the demographic attributes. The system generates an output that indicates a number of objects in the digital audience that were reached by content directed at the digital audience and demographic attributes for the number of objects.

A system and method for generating advertisement automatically are provided. The system may comprise at least one computer-readable storage medium including a set of instructions; at least one processor in communication with the at least one computer-readable storage medium, wherein when executing the set of instructions, the at least one processor is configured to cause the system to generate a first plurality of ads, the first plurality of ads including a first plurality of advertisement elements and a first plurality of information components; transmit, via a network, the first plurality of ads to a first group of user terminals; determine at least one of a click-through rate, a number of impressions, or a conversion rate for the first plurality of ads; and analyze the at least one of the click-through rate, the number of impressions, or the conversion rate of the first plurality of ads.

Systems and methods for targeted advertising to specific consumers are disclosed. A system may include a memory storing instructions and at least one processor configured to execute the instruction to: receive, over a network, client-provided data from a client device; identify at least one consumer by comparing the client-provided data against consumer data recorded in an electronic consumer database; obtain at least one unique consumer identifier for the identified at least one consumer, the at least one unique consumer identifier not including personal identifiable information; generate a target audience pool based on the at least one unique consumer identifier; and deliver, over a network, the target audience pool to the client device to facilitate targeted advertising to specific consumers.

The present disclosure is related to systems and methods for determining an optimal strategy. The method includes classify one or more users into a first user group and a second user group using an optimization model, wherein the first user group and the second user group correspond to two strategies, respectively. The method also includes obtain behavior data from terminals of the one or more users in the first user group and the second user group. The method further includes determine a first value of a parameter regarding the first user group and a second value of the parameter regarding the second user group using the optimization model. The method further includes determine a strategy based on the first value and the second value.

A hierarchical feature tree is generated. Each child node's feature is more specific than its respective parent node's feature. A behavioral model comprising features of the feature tree is created and used in the operation of an advertising campaign. A degraded model feature is detected at the discovery system by comparing a performance metric of a model feature from two different time windows. The discovery system matches a node of the feature tree with the degraded feature and selects a prospective model feature from a family node. An estimated performance metric for the prospective model feature is determined and the results are used to decide if the model should be updated to include the prospective model feature. The campaign can be operated with the automatically updated model.