Embodiments provide computer apparatuses, computer systems and computer-executable methods for generating geographic zone information associated with a consumer. A method includes receiving a first geographic location associated with the consumer, and programmatically identifying a first geographic zone associated with the first geographic location. The method also includes programmatically generating a first importance score associated with the first geographic zone and associated with the consumer. The method also includes storing, on a non-transitory computer-readable medium, the first geographic zone and the first importance score associated with the consumer.

A method includes accessing a schema that specifies relationships among datasets, computations on the datasets, or transformations of the datasets, selecting a dataset from among the datasets, and identifying, from the schema, other datasets that are related to the selected dataset. Attributes of the datasets are identified, and logical data representing the identified attributes and relationships among the attributes is generated. The logical data is provided to a development environment, which provides access to portions of the logical data representing the identified attributes. A specification that specifies at least one of the identified attributes in performing an operation is received from the development environment. Based on the specification and the relationships among the identified attributes represented by the logical data, a computer program is generated to perform the operation by accessing, from storage, at least one dataset having the at least one of the attributes specified in the specification.

A method includes accessing a schema that specifies relationships among datasets, computations on the datasets, or transformations of the datasets, selecting a dataset from among the datasets, and identifying, from the schema, other datasets that are related to the selected dataset. Attributes of the datasets are identified, and logical data representing the identified attributes and relationships among the attributes is generated. The logical data is provided to a development environment, which provides access to portions of the logical data representing the identified attributes. A specification that specifies at least one of the identified attributes in performing an operation is received from the development environment. Based on the specification and the relationships among the identified attributes represented by the logical data, a computer program is generated to perform the operation by accessing, from storage, at least one dataset having the at least one of the attributes specified in the specification.

A digital platform for aggregating ad campaign data relating to one or more ad campaigns that is configured to allow users to generate customized data sets and perform user-customized operations on those data sets. The platform enables users to create and perform these operations on data from various sources through use of standardized data formats that is applied to the data retrieved from such sources.

Systems and methods are provided for generating a predicted number of viewers in support of selecting an advertisement to be displayed during an advertisement break. The method includes: generating, by an advertisement break class identifier module, a plurality of advertisement break classes based on historical advertisement break information and prediction errors generated by a regression model analysis of advertisement break data samples; evaluating, by the advertisement break class identifier module, the plurality of advertisement break classes; redefining and rearranging, by the advertisement break class identifier module, advertisement break classes from the plurality of advertisement break classes based on the step of evaluating which results in an updated set of advertisement break classes; forwarding, by the advertisement break class identifier module, the updated set of advertisement break classes and trained model information to a prediction module; receiving, modifying and returning prediction model information to the prediction module by a training function in the advertisement break class identifier module; generating, by the prediction module, the prediction of a number of viewers for the advertisement break, wherein a regression model uses the updated set of advertisement break classes and data associated with a specific advertisement break to generate the prediction; and sending, by the prediction module, the prediction of a number of viewers for the advertisement break to a scheduler of advertisements.

Devices, systems, and methods for allocating consumer communications can include obtaining consumer activity data, designating a number of consumer communication campaigns concerning consumer features based on the consumer activity data, entering consumer activity data as inputs to one machine learning model for each determined consumer communication campaign, each machine learning model configured determine a campaign consumer activation profile based on the entered consumer activity data, and assigning the consumer communication campaigns to consumers based on the determined campaign consumer activation profiles.

Described are systems and methods to determine advertisements to be presented to a user. To determine the advertisements to be presented to the user, the described systems and methods utilize localized contextual information to select the advertisements and the relative positioning of the advertisements to be presented, so as to select and present more relevant advertisements based on the content that is surrounding and proximate to the presentation of the selected advertisements.

Various methods, apparatuses/systems, and media for automating sponsored-search data pipelines are disclosed. A processor generates keyword-level metrics data based on received bidder input data that includes cost-per-acquisition (CPA) data and total spending data for each keyword; determines campaign-level CPA threshold data chosen at previous iteration of search campaign and a target CPA data used for current search campaign; calculates, campaign-level metrics data that includes the CPA data and adjusted total spending data; quantifies a final campaign-level reward data based on the calculated campaign-level metrics data, adjusted total spending data, and the target CPA data; updates a distribution corresponding to CPA-threshold data chosen at previous iteration using the final campaign-level reward data; samples CPA-threshold distributions and determines CPA-threshold data chosen at current iteration; executes campaign-level heuristics using the keyword-level metrics data, campaign-level metrics data, and the CPA-threshold data chosen at current iteration; and displaying final heuristic-execution data onto a GUI.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automated management of campaigns using scripted rules.

Disclosed herein are system, method, and computer program product embodiments for analyzing second party advertising data. An embodiment operates by determining a set of dimensions that a source uses to aggregate data for an advertising campaign. The embodiment creates a subunit advertising campaign based at least in part on the advertising campaign, the determined set of dimensions, and a dimension of interest. The embodiment receives measurement data associated with an execution of the subunit advertising campaign. The embodiment then analyzes the measurement data.