Various methods, apparatuses/systems, and media for automating sponsored-search data pipelines are disclosed. A processor generates keyword-level metrics data based on received bidder input data that includes cost-per-acquisition (CPA) data and total spending data for each keyword; determines campaign-level CPA threshold data chosen at previous iteration of search campaign and a target CPA data used for current search campaign; calculates, campaign-level metrics data that includes the CPA data and adjusted total spending data; quantifies a final campaign-level reward data based on the calculated campaign-level metrics data, adjusted total spending data, and the target CPA data; updates a distribution corresponding to CPA-threshold data chosen at previous iteration using the final campaign-level reward data; samples CPA-threshold distributions and determines CPA-threshold data chosen at current iteration; executes campaign-level heuristics using the keyword-level metrics data, campaign-level metrics data, and the CPA-threshold data chosen at current iteration; and displaying final heuristic-execution data onto a GUI.

When a content item is initially served to a client device, the content item may result in an impression effect. As time elapses, the initial impression may fade. Such a decay of the impression effect may be predicted through the use of a predictive model. In some implementations, one or more impression effect parameters may be accessed and used with the predictive model to determine a decay factor or predicted value that incorporates the impression effect decay for a content item. A value, such as a score, may be determined based on the decay factor or the predicted value and a bid associated with a content item. A content item may be selected based on the determined value and data to effect presentation of the content item may be provided.

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 to 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.

The present invention relates to methods for evaluating the effectiveness of all types of communication carried on offline communication media so as to generate data equivalent to the data obtained in online communication media. The present invention uses tangible tools such as fixed-line telephones, cellphones, computers, tablets and any other wearable mobile device to generate that data that will be used in media plans that are much more precise and efficient than the plans currently known. Finally, the present invention relates to said media plan obtained using one of said methods as well as the purchase of optimized media.

Systems and methods for identifying an audience set are disclosed. A request receive identifying a future time period and item class is received and a conversion value for each of a set of user identifiers is generated by implementing a trained statistical machine learning model using historical transaction data. A first subset of user identifiers and a second subset of user identifiers are identified based on threshold values of the conversion value. The subsets are each associated with targeted advertisement types corresponding to a particular level of specificity associated with the requested item class. The first targeted advertisement type is presented to user devices associated with the first subset of user identifiers and the second targeted advertisement type is presented to user devices associated with the second subset of user identifiers.

A method and associated system of managing advertising spending in an advertising campaign for an online marketplace seller, including, under control of one or more processors configured with executable instructions, defining a sales goal; setting a daily advertising budget and a bid value for an advertising campaign of the product; executing the advertising campaign; automatically collecting sales data relating to the product on the online marketplace; executing a machine learning component of an adaptive machine learning platform to generate a machine learning component output, at least in part based on the sales data; generating, based at least in part on the machine learning component output of the machine learning component, one or more sales milestones for the product on the online marketplace; comparing the sales data to the one or more sales milestones; and adjusting the daily advertising budget or the bid value to meet the sales goal.

An event plan acquirer is configured to acquire event planning information which is planning information on an external event in a future target period. The external event is an event of which implementation is plannable in advance, of which implementation or notification of the implementation may affect results of programmatic advertising, and which is different from programmatic advertising delivery. A target condition acquirer is configured to acquire target condition information indicating a target condition related to results in a target period of the programmatic advertising. A planner predicts the results of the programmatic advertising in the target period based on the event planning information, the programmatic advertising delivery plan in the target period, and a prescribed prediction model, and creates the programmatic advertising delivery plan in the target period so that the predicted results approach results indicated in the target condition information.

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.

A method implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include monitoring periodically whether a respective recommended bidding price update for a campaign type for a user is required for a respective department of campaign departments based on a respective landscape distribution of respective bidding prices for the campaign type for the respective department. The method further can include, after determining that the respective recommended bidding price update is required for the campaign type for the respective department, determining a respective recommended bidding price for a respective target of the respective department based at least in part on the campaign type for the respective target by: (a) determining a respective bidding function for the respective target of the respective department based on the campaign type; and (b) determining the respective recommended bidding price by solving, by using the one or more processors, the respective bidding function for the respective target of the respective department based at least in part on a respective campaign demand, a respective expected performance, a respective winning rate, and a respective cost for the respective target for the user. The method additionally can include, after determining that the respective recommended bidding price update is not required for the campaign type for the respective department, determining that the respective recommended bidding price for the respective target of the respective department for the user is a respective prior bidding price for the respective target without solving the respective bidding function. Other embodiments are described.

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.