Systems and methods for scoring promotions are provided. A set of training offers are received, which include combinations of variable values. These combinations of variable values are converted into a vector value. The offers are paired and the vectors subtracted from one another, resulting in a pair vector. Metrics for the success of offers is collected, and are subtracted from one another for the paired offers to generate a raw score. This raw score is then normalized using the pair vector. The normalized scores are utilized to generate a model for the impact any variable value has on offer success, which may then be applied, using linear regression, to new offers to generate an expected level of success. The new scored offers are ranked and the top-ranked offers are selected for inclusion in a promotional campaign.

There are provided systems and methods for iteratively improving an advertisement response model. A payment provider may perform operations that include training an advertisement response model using a training data set. The operations include determining that a first accuracy value corresponding to the advertisement response model is less than a accuracy value threshold. The operations further include identifying, based on executing the advertisement response model using a target data set that is different from the training data set, one or more units from the target data set for which to run the advertising campaign. The operations also include receiving one or more responses corresponding to a run of the advertising campaign with respect to the identified one or more units from the target data set and updating the training data set based on the one or more responses. The operations further include training an advertisement response model using resulting training data and repeating the operations as long as the accuracy value of the resulting model stays below the threshold or until the increase in the accuracy value with each iteration becomes unprofitable with respect to the costs of acquiring responses from further units from the target dataset.

The present disclosure provides a detailed description of techniques used in methods, systems, and computer program products for simulating user web page visits to generate an advertising campaign target audience list. The claimed embodiments address the problem of efficiently and quickly processing voluminous amounts of user data to enable rapid initiation of an advertising campaign. More specifically, the claimed embodiments are directed to approaches for a receiving user web page visit records (e.g., user ID, URL, timestamp, etc.), preparing batches of the visit records, and iterating through the batches of visits to simulate a user's re-visit to the web page. The simulated user visits and associated user attributes (e.g., from on line and off line activities) can then be tested against advertising campaign specifications to generate a target audience list.

A system and method for managing advertisements in a syndicated feed is described. One embodiment includes a method for expiring an advertisement space in a feed. This method includes the following actions: receiving a data item associated with a feed from a publisher; determining whether an advertisement space should be associated with the data item; inserting a markup of the advertisement space into the data item; providing the data item for a user; receiving a request from the user to view the advertisement corresponding to the advertisement space and the data item; determining whether the data item is older than a threshold age; providing an advertisement to the user if the data item is not older than a threshold age, wherein the advertisement is viewable in the advertisement space; and providing a blank advertisement to the user if the data item is older than a threshold age.

An online system automatically and dynamically determines an audience for content by clustering users across various dimensions, and refining targeting criteria for the content. The online system receives content and initial targeting criteria from a content provider. The content is provided to a group of users that meet the initial targeting criteria. The system collects content response data from the group of users that were provided the content, including user responses to the content and dynamic data relating to time and location of the user responses. The content response data is further integrated with user characteristics, content presentation data, and social response data to generate integrated user-content data of the content. Clusters of users are generated based on features of the integrated user-content data, and refined targeting criteria are identified based on the generated clusters that can then be used for more accurate targeting of the content to users.

Increasing a number of advertising impressions in a system configured to facilitate user interaction with an interactive environment containing one or more advertising targets is described. A scene of a portion of the simulated environment is displayed from a camera point of view (camera POV) on a video display. The scene may change as the camera POV changes in response to movement of the camera POV along a camera path during the user's interaction with the simulated environment. Modifying the camera path or a camera field of view can place more of an advertising target within the scene displayed on the video display to increase a likelihood of generating an advertising impression compared to a likelihood of generating an advertising impression without the modification.

A computer-implemented method for intelligent casting of social media creators includes receiving, from a provider of goods or services, a request to match a social media post topic with a social media creator among a plurality of social media creators, accessing a database comprising a plurality of social media posts by the plurality of social media creators, analyzing each social media post among the plurality of social media posts, selecting a predetermined number of social media creators among the plurality of social media creators according a best match of the request to one or more attributes of each social media creator among the plurality of social media creators and the analysis of the plurality of social media posts, and returning a list of the selected social media creators.

Methods and systems for providing an automated virtualized signal marketplace or exchange for signals from distributed data sources for use in advertising. Systems and methods to automatically discover and recommend which of the signals controlled by multiple entities may be most effective toward a given objective associated with advertising campaigns. Signals are indicators of data that are derived from data sources and abstracted to protect the underlying data. Each entity that sells data in the virtual marketplace first converts data into a “signal” or indicator that represents the data without disclosing it or providing it. Signal sellers determine if they want share signals based upon buyer, price, and other rules, including limitations on signal use; signal buyers determine signal value based upon their objectives.

A system and method of predicting a value of a key performance indicator (KPI) of a target advertisement campaign may include receiving a plurality of campaign data elements, such as campaign types, campaign geographies, campaign dates, and historic KPI values corresponding to a respective plurality of campaigns; processing the plurality of campaign data elements to produce one or more training batches; training a machine-learning (ML) model to predict a value of a campaign KPI, based on the one or more training batches. In a subsequent inference stage, embodiments may receive at least one new campaign data element, corresponding to a target campaign; and applying the trained ML model on the at least one new campaign data element to predict a value of a target KPI of the target campaign.

There is provided a method for an out of home advertising campaign, the method comprising: supplying creative for the campaign; determining criteria for the campaign, the criteria comprising targeting a demographic; selecting one or more boards for display of the creative, the selecting based on static data, projected data, and optionally real-time data; and displaying the creative on the one or more boards. There is also provided an out of home digital ad server comprising: at least one digital board; a digital feed provider to provide each board with creative to be displayed; a computer processor for analysing data to optimize board selection based on a demographic; and a communication network to direct creative from the ad serving processor to the at least one digital board based on the selection of creative by the computer processor.