A display characteristic determination machine receives a request to display seller-related content that references an item available for sale. The display characteristic determination machine identifies a display characteristic available from a web server, determines that the seller-related content is to be displayed using the display characteristic, and communicates to the web server that the seller-related content is to be displayed using the display characteristic and with primary content. The display characteristic determination machine may function as an allocation device to allocate use of the display characteristic. This functionality may be scaled, for example, to include determining use of multiple display characteristics, available from multiple web servers, by multiple instances of seller-related content requested for display by multiple sellers of multiple items. The display characteristic determination machine, therefore, may function as a “broker” for display characteristics available from multiple web servers.

Methods and systems are described for providing programmatic bidding of advertisement inventory. In one embodiment, an advertising system includes an ad bidding component or module of an ad server and a storage medium coupled to the ad server. The storage medium stores instructions including instructions of the ad bidding component or module. Processing logic is configured to execute the instructions to receive a bid campaign function call for an ad campaign from an advertising entity, determine objectives for the advertising entity including life time value (LTV) for users and return on investment (ROI) for the ad campaign, determine targeted users having characteristics appropriate for satisfying the objectives of the advertising entity, and autonomously determine a dynamic ad bid price parameter and associated group of targeted users that satisfy the objectives of the advertising entity based on having characteristics that satisfy at least three different parameters.

A computerized method of content verification comprising using a server for receiving a first data from a host monitoring code embedded in a webpage or an application loaded from a content server and executed by a client device, the host monitoring code is executed by the client device during an execution of the webpage or the application which further embeds nesting element(s) for loading nested content from nested content server(s), the first data is indicative of the execution, receiving a second data indicative of the execution from a guest monitoring code embedded in the nested content, combining the first data and second data for compliance verification of the execution with one or more rules associated with the nested content and initiating action(s) according to the verification. Wherein the first data is not available to the guest monitoring code and the second data is not available to the host monitoring code.

A system includes an offer datastores including one or more offers from one or more merchants, a registered card module to register one or more payment cards to be used for a purchase transaction, a transaction matching module to identify the one or more merchants from a collection of purchase transaction data and to match the purchase transaction of the identified one or more merchants with one or more offers in the offer datastore from the identified one or more merchants, and a rewards module to determine an incentive to be applied to the one or more payment cards based on any offer associated with the matched merchant and generate a qualified transaction data to be transmitted to an issuer of the one or more payment cards.

This disclosure describes systems, methods, and computer-readable media related to retargeting online advertisement campaign recommendations for advertisements with multiple items or services. Bids may be based on a combined advertisement creative comprising two or more items or services. Dynamically selecting multiple items at bid time using a retargeting model to determine a potential revenue generation amount associated with an event may increase the probability of a conversion event based on the creative that includes the selected items. In some embodiments, a machine-learned retargeting model may be used to select multiple items to be displayed in an advertisement. The retargeting model may be applied to items that were previously viewed by the consumer and may determine a value for each of the items using factors. A bid may be calculated for each of the selected items using the values determined by the retargeting model.

In one embodiment, a method includes accessing data about past performance of an online advertising campaign with respect to one or more online-advertising metrics; generating a first visualization of the past performance of the online advertising campaign as a function of an independent variable and a second visualization of past bid adjustments for online advertisements in the online advertising campaign corresponding to the past performance of the online advertising campaign as a function of the independent variable; receiving user input from a user specifying future bid adjustments for online advertisements in the online advertising campaign relative to the past bid adjustments as a function of the independent variable; and applying the user input to future bid adjustments for online advertisements in the online advertising campaign relative to the past bid adjustments as a function of the independent variable.

A system for receiving data associated with a mobile content is configured to calculate an expected value of the mobile content based at least in part on the data received, and determine a bid amount for a sponsorship of the mobile content based at least in part on the expected value.

A system for classifying and managing keywords across multiple campaigns comprises a classification manager, a bid management system, a web analytics tool and a search engine. The classification manager interfaces with the bid management system and the web analytics tool to provide user interfaces for reviewing data about specific keywords across multiple campaigns. The classification manager includes a classification keyword repository, a classification creation module, a classification measurement module and a classification user interface module for generating the user interface. This is particularly advantageous because statistics corresponding to keywords can be classified across multiple campaigns to provide greater flexibility in the analysis of web data. The present invention also includes a novel method for the classification of keywords across multiple campaigns.

A dynamically regulated advertising delivery control system. A campaign is operated by sending bids to an exchange responsive to receiving bid requests from the exchange, each bid request representing an opportunity to expose a browser to content. Won bid notifications are received from the exchange and exposure notifications are received from exposed browsers. Failed exposures are detected by detecting won bid notification identifiers without corresponding exposure notification identifiers. Responsive to the failed exposures exceeding an upper limit, the campaign is operated in a throttled mode by sending bids to the exchange in response to a fraction of the suitable bid requests received from the exchange and ignoring some suitable bid requests. Responsive to detecting successful exposures in the throttled mode, the operation of the campaign is dynamically regulated by increasing the fraction.

A computer-implemented method includes generating, using a transaction management computing subsystem of an advertising platform, a set of primary bid requests responsive to receipt of an advertising call. Each primary bid request includes information sufficient to characterize an impression consumer and information sufficient to characterize each of one or more impressions identified in the advertising call. The method also includes sending the set of primary bid requests from the transaction management computing subsystem to a first set of decisioning computing subsystems of the advertising platform. Each decisioning computing subsystem of the first set being operable to generate a bid response based on the information included in a primary bid request. The method further includes selecting, using the transaction management computing subsystem, a first bid response from among the bid responses generated by the first set of decisioning computing subsystems; and taking, by the transaction management computing subsystem, an action on the first bid response.