Techniques are disclosed for real-time bidding (e.g., for dynamic online content placement) using an optimized final bid. The final bid is determined based on a predicted clearing price and an initial bid. The initial bid represents a value to a prospective content provider, and may be computed based on campaign information. The predicted clearing price is a predicted amount paid, and may be predicted using a model trained using historical winning bids data. The clearing price may be predicted using a quantile regression model, where the quantile can be selected to control bid aggressiveness. In some cases, the quantile is determined based on pacing in an overall campaign. Once the initial bid and the predicted clearing price are calculated, the final bid is computed based on the initial bid and the predicted clearing price.

Disclosed here is a system that can obtain attributes of an advertisement, where an attribute has a continuous value, and a range of acceptable values is uncertain. The system can create a file including contents that when provided to a predetermined function produce a value of the attribute. Based on the file, the system can generate values corresponding to the attributes. Based on the generated values, the system can create the advertisement. The system can obtain a response data to the created advertisement and can fit a multidimensional function to the attributes and the user response data. Based on the multidimensional function, the system can determine next values and next ranges, where the next values and the next ranges indicate an improvement in the response data.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for presenting content items. In one aspect, a method includes identifying an opportunity to present a content item at a device and in a manner that precludes a user from accessing content of one or more applications executing on the device. Data relating to a status of each of the one or more applications are identified. A level of urgency for a user to access content of the one or more applications is determined based on the data. A response to the opportunity to present a content item is selected based on the level of urgency for the user to access content of the one or more applications. The response is selected from a first response that causes presentation of the content item and a second response that precludes presentation of the content item.

In some examples, a system comprises at least one programmable processor; and a machine-readable medium having instructions stored thereon which, when executed by the at least one programmable processor, cause the at least one programmable processor to execute operations comprising: receiving a first request from at least one user device to execute an instance of an application; transmitting a graphical user interface (GUI) to the at least one user device to be rendered on a display of the at least one user device; receiving a second request, via the GUI, from the at least one user device, to deploy a digital advertisement, the second request including a set of platforms of a plurality of platforms of a multi-platform integration system, a set of settings, a set of parameters, and a set of allocation data; interfacing with each one of the platforms in the set of platforms; and integrating a digital advertisement directly with each one of the platforms in the set of platforms based on the set of settings, the set of parameters and the set of allocation data.

Techniques and system are described for a real time bid platform to control output of digital marketing content to a potential consumer. In an example, impression data is generated by a physical retail environment or mobile device of the potential consumer based on a determined location of a user within a physical retail environment. The impression data is used by a digital marketing system to expose bid opportunities to a plurality of advertiser systems via the real time bid platform. The advertiser systems then generate bids based on impression data described as part of the bid opportunity, and may also be based on additional information the advertiser systems have about the potential consumer obtained from third-party systems. The bids are then used as a basis to control output of digital marketing content to the potential consumer.

According to an implementation, a method for digital information retrieval in a social media platform includes transmitting, over a network, information to render a timeline of social content for a user of a client application. The timeline of social content includes messages posted on the messaging platform by user accounts that are connected to a user account of the user in a connection graph. The method includes computing, using a machine-learning algorithm inputted with relevance signals, a relevance level between the user account of the user and a user account not linked to the user account of the user in the connection graph, and transmitting information about a profile of the user to a computing device associated with the user account not linked to the user account of the user in response to the relevance level being greater than a threshold level.

A method, a computer program product and an apparatus for advertising to a crowd. The method comprises obtaining an estimated aggregated demographic data of an estimated audience of a video configured to be provided to the plurality of members, and performing a crowd-matching between the estimated audience of the video and a campaign to determine a matched campaign. The crowd-matching is performed by an auction between a plurality of participating advertisers, each of which providing a bid for presenting a campaign within the video that defines profile-based compensations. The matched campaign is provided to an audience of the video based on the estimated aggregated demographic data and without performing personalized matching of the matched campaign to a specific member.

Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium for delivering content. A method includes: identifying an application that is installed on a user device; accepting a bid from a sponsor associated with the application for presenting customized content on the user device after installation of the application on the user device; identifying an opportunity to present content on the user device including receiving a request for content that identifies the user device; conducting an auction including evaluating the accepted bid along with other bids; determining that the accepted bid is the winning bid in the auction and providing, in response to the determining, a shell for the customized content; enabling the shell to be populated with data stored on the user device to produce the customized content; and providing the customized content for presentation on the user device in response to the identified opportunity.

A system for processing information requests associated with mobile devices comprises an evaluation module configured to determine at least one performance measure for each of a plurality of information documents using at least data in one or both of a requests database and events database. The at least one performance measure includes at least one of an impression-based performance measure, a click/call-based performance measure, and an off-line site-visit-based performance measure. The system further comprises an information server configured to select a first information document for transmitting to a first mobile device to fulfill a first request. The information server includes a volume control unit configured to derive an off-line site visit projection in response to the first document being selected based at least in part on an off-line site-visit-based performance measure and having been impressed on the first mobile device, and to adjust a budget associated with the first document using the off-line site visit projection.

A digital advertising system includes at least one processor configured to execute a plurality of functional modules including an analytics module to receive and analyze client attributes associated with a website visitor and a requested website to define an analytics event. The analytics module ingests and enriches data within the analytics event and provides it to a machine learning module that generates prediction models for potential bids. A management platform receives the bidding prediction and generates candidate configs. An optimization module receives the candidate configs and applies weights and additional features to select a config and generate an optimized script for the selected config. A deployment module receives the optimized script and delivers the script to the website visitor.