Systems, methods and media for adaptive real time modeling and scoring are provided. In one example, a system for automatically generating predictive scoring models comprises a trigger component to determine, based on a threshold or trigger, such as a detection of new significant relationships, whether a predictive scoring model is ready for a refresh or regeneration. An automated modeling sufficiency checker receives and transforms user-selectable system input data. The user-selectable system input data may comprise at least one of email, display or social media traffic. An adaptive modeling engine operably connected to the trigger component and modeling sufficiency checker is configured to monitor and identify a change in the input data and, based on an identified change in the input data, automatically refresh or regenerate the scoring model for calculating new lead scores. A refreshed or regenerated predictive scoring model is output.

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.

In accordance with some embodiments of the disclosed subject matter, methods, systems, and media for setting and using an advertisement frequency cap based on causal conversions or impact of advertisements are provided.

The present disclosure provides for management of impressions in advertisement campaigns. Impressions may be moved between different impression media based on performance metrics and historical data. Impression budgets may be modified in an active campaign based on forecasts determined using current performance data. Impression budgets may be reallocated between simultaneously active advertisement campaigns.

The present disclosure provides for management of impressions in advertisement campaigns. Impressions may be moved between different impression media based on performance metrics and historical data. Impression budgets may be modified in an active campaign based on forecasts determined using current performance data. Impression budgets may be reallocated between simultaneously active advertisement campaigns.

Systems and methods are disclosed for parsing resume documents using computer vision and optical character recognition technology in combination with a user feedback interface system to facilitate user feedback to improve the overall processing quality of the resumes that are imported into computer resume processing systems. In at least one embodiment, the system and method prompt a user to upload an input resume document, which is processed with a first parsing pass to generate initial resume data by extracting a plurality of resume text blocks. Further processing identifies an initial set of bounding blocks and to visually displays the initial resume data for user review and feedback to regroup one or more of the initial set of bounding blocks into a regrouped bounding block. Additional processing consolidates into a group text block each of the resume text blocks corresponding to the regrouped one or more of the initial set of bounding blocks.

A computer-implemented method may include: tracking performance data for a content makers, the performance data identifying outputs attributed to the content makers responsive to digital content created by the content makers; causing to be displayed an interactive graphical user interface (GUI); receiving, from the user computing device via the interactive GUI, operation data associated with an operation, the operation data including a plurality of data objects associated with the operation, the plurality of data objects input by the user via the interactive GUI; applying the plurality of data objects to the tracked performance data for the content makers to select a subset of the content makers for the operation; causing to be displayed outputs associated with the selected subset of the content makers; and receiving, via the GUI, a selection of one or more content makers of the subset from a device associated with the user.

A digital ad-buying platform uses counterfactual-based incrementality measurement by implementing randomization and/or a correction for auction win bias to avoid the need to identify counterfactual winner types in the control group. This approach can estimate impact at the individual consumer level. Confidence levels can be determined using Gibbs sampling in the context of causal analysis in the presence of non-compliance.

A push marketing campaign is configured to convert users into purchasers of a product or service. A graph containing nodes and edges connecting the nodes is constructed. The graph represents paths for users of a product to transition from an entry state indicated as an entry node, through engagement states indicated as engagement nodes, to an objective state indicated as an objective node. Weights for the engagement nodes and a first set of probabilities for the edges are determined based on a first data set obtained without stimulus messaging. Each probability indicates a probability of users transitioning a corresponding edge of the graph. A second set of probabilities for the edges is determined based on a second data set that included stimulus messaging. A set of stimulus nodes of the graph for sending stimulus messages to users is selected to maximize the users that transition to the objective state.

A machine may be configured to perform A/B testing on customized experiment units. For example, the machine receives a specification of an experiment unit that identifies a type of subject of an experiment for execution on a social networking service (SNS), and a value of the experiment unit. The machine generates, for the value of the experiment unit, tracking data that tracks user interactions by one or more users of the SNS, via one or more browsers, with content provided during an execution of the experiment. The machine generates, for the value of the experiment unit, metric data that measures an attribute associated with the experimental unit. The machine generates an experiment report based on the tracking data and the metric data. The machine causes a presentation of the experiment report in a user interface of a client device.