The illustrative embodiments described herein provide systems and methods for managing advertisements for advertisers. In one embodiment, a method includes receiving a set of advertising preferences from an advertiser. The set of advertising preferences includes advertiser-definable criteria controlling when to initiate an advertisement associated with the advertiser. The method also includes monitoring a set of events associated with event data to determine whether to initiate the advertisement during an event. The set of events are viewable on an interfacing device associated with a user. The method also includes initiating the advertisement during the event in response to the event data for the event meeting the criteria defined by the advertiser.

Method and system for localizing an element present in a piece of content having a plurality of elements. A cost of localizing an element with respect to each of one or more localization sources is first computed. At least one criterion based on which a localization source for localizing the element is to be determined is obtained. A localization source for localizing the element is then selected based on an assessment with respect to the at least one criterion. The element of the content is then localized using the selected localization source.

A real-time messaging platform allows advertiser accounts to pay to insert candidate messages into the message streams requested by account holders. To accommodate multiple advertisers, the messaging platform controls an auction process that determines which candidate messages are selected for inclusion in a requested account holder's message stream. Selection is based on a bid for the candidate message, the message stream that is requested, and a variety of other factors that vary depending upon the implementation. The process for selection of candidate messages generally includes the following steps, though any given step may be omitted or combined into another step in a different implementation: targeting, filtering, prediction, ranking, and selection.

Methods and systems are described for providing advanced programmatic advertising targeting. In one embodiment, a system includes a storage medium to store instructions of one or more performance based algorithms and processing logic coupled to the storage medium. In response to receiving a function call from an advertising entity, the processing logic is configured to execute the instructions of the one or more performance based algorithms to analyze parameters of the function call including an advertising cost budget for an ad campaign and analyze advanced targeting data and parameters and rules for determining a customized ad campaign for the advertising entity. The advanced targeting data and parameters include device characteristics and user characteristics including at least one of conversion rate, ad engagement rate, installed software application (app) type on a user's device, and in-app purchase (IAP) activity of a user from any ad source.

An automatic system facilitates selection of media properties on which to display an advertisement, responsive to a profile collected on a first media property, where a behavioral-targeting company calculates expected profit for an ad correlated with the profile and arranges for the visitor to be tagged with a tag readable by the selected media property. The profit can be calculated by deducting, from the revenues that are expected to be generated from an ad delivered based on the collected profile, at least the price of ad space at a media property where the BT company might like to deliver ads to the profiled visitor. When the calculated profit is positive (i.e., not a loss), the BT company arranges for the visitor to be tagged with a tag readable by the selected media property through which the BT company expects to profit.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and cause the one or more processors to perform (1) receiving a request to generate one or more campaigns; (2) determining one or more predicted bids for one or more keywords in the one or more campaigns; (3) adjusting the one or more predicted bids for the one or more campaigns; (4) pacing the one or more predicted bid, as adjusted, for the one or more campaigns; and repeating (2)-(4) at one or more periodic intervals. Other embodiments are disclosed herein.

The demand-side platform (DSP) is a technological ingredient that fits into the larger real-time-bidding (RTB) ecosystem. DSPs enable advertisers to purchase ad impressions from a wide range of ad slots, generally via a second-price auction mechanism. In this aspect, predicting the auction winning price notably enhances the decision for placing the right bid value to win the auction and helps with the advertiser's campaign planning and traffic reallocation between campaigns. This is a difficult task because the observed winning price distribution is biased due to censorship; the DSP only observes the win price in the case of winning the auction. For losing bids, the win price remains censored. In this invention, we generalize the winning price model to incorporate a gradient boosting framework adapted to learn from both observed and censored data. This yields a boost in predictive performance in comparison to classic linear censored regression.

Methods and system are provided herewith for providing information services. A data service in according with the methods and systems disclosed herein may provide content for the data service and/or may provide content to subscribers via the service and the content provider may provide a credit or other benefit to the content provider.

Methods, systems, and media for identifying automatically refreshed advertisements are provided. In some embodiments, a method for modifying advertisement spending is provided, the method comprising: receiving advertisement delivery information associated with a plurality of advertisements displayed on a web page; generating a distribution of an amount of time that the plurality of advertisements were displayed on the web page using the advertisement delivery information; identifying a deviation in the generated distribution; determining whether the deviation correlates to an automatic refresh command performed by one or more browser applications; and providing an indication corresponding to the plurality of advertisements that were displayed on the web page in response to the automatic refresh command based on the determination.

Disclosed herein are a method and system that utilize a programmed content predictor to dynamically select electronic publishing content. In particular, the content predictor applies a selection model to select content for one or more selected webpages presented during an electronic transaction. The selection model utilizes a set of one or more machine learning models to select content based on calculated quality scores. The nature of the quality scores determined by the quality score model depend on the particular application. The predictor generates and populates a permutation quality table based on a set of selected content items and page variant, wherein the page variant defines locations of content positions within a webpage. The predictor then consumes the selection model to select a best permutation of content item-content position combinations to be returned for display on a webpage.