An information processing apparatus according to an embodiment of the present technology includes a plan creation section. The plan creation section creates a plan of movement of a mobile object to a destination of the mobile object in association with advertisement effectiveness on the basis of the destination and a desired time of arrival at the destination, the advertisement effectiveness being provided by advertisement content played back outward from the mobile object. This makes it possible to create the movement plan enabling the advertisement vehicle to arrive at the destination by the desired arrival time, and achieving a highest degree of the advertisement effectiveness. This results in being able to achieve a high degree of advertisement effectiveness in advertisement using the mobile object.

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.

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.

A computer implemented method of adapting an application according to user interaction comprising using one or more processors for executing a code for collecting autonomously a plurality of action events describing a plurality of actions taken by a plurality of users to navigate through a plurality of pages presented by an application to accomplish one or more goals of the application, the plurality of pages are presented on a GUI at a plurality of user devices used by the plurality of users, analyzing the action events to identify one or more behavioral patterns of at least some of the users for accomplishing the goal(s) and generating automatically one or more recommended adaptations for the application according to the behavioral pattern(s) to adapt a layout of the application in order to increase a probability for one or more users to successfully accomplish the goal(s).

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing application program interface calls.

Embodiments of the present disclosure relate to data correlation of data pertaining to content distributed through distinct content delivery networks including offline networks and data related to user interaction with an online presence on the distributed computer network and uses for such correlated data, including to measure and quantify latent effects of the content distributed through a distinct content delivery network, such as an offline network, on user's interactions with the online presence on the distributed computer network.

Disclosed are methods and systems for generating targeted advertising campaigns for a business-to-business (B2B) company. The method comprises first retrieving an ideal customer profile (ICP), and generating candidates leads that match the ICP. Next, generating test campaigns, scoring each test campaign based on a number of acquired leads and a campaign cost per lead (CPL), where the number of acquired leads is calculated based on feedback information received from each test campaign, and where each test lead who responds affirmatively to one of the test campaigns is marked as an acquired lead, and generating a targeted advertising campaign to a larger subset of the candidate leads based on the test campaign scores. The present invention utilizes a closed-loop approach to customize campaign audiences, to optimize targeted advertising campaigns and ICPs, and as a result, to produces high-quality, low-cost, targeted advertising campaigns for B2B companies.

Online advertising campaigns are operated responsive to keyword trends. The keywords used by a group of browsers is analyzed periodically over time. A list of the most frequently used keywords is separated into those that have previously appeared on the list, referred to herein as the stable keywords, and those that are newly emerging, referred to herein as the trending keywords. The advertiser selects at least one advertising creative that the advertiser associates with the stable keywords, referred to herein as the stable creative, and at least one advertising creative that the advertiser associates with the trending keywords, referred to herein as the trendy creative. The advertising system then operates the online advertising campaign to deliver the respective stable and trendy creatives in proportion to the frequency of use of the trending versus the stable keywords.

Systems and methods for selecting visual elements to insert into content items based on sentiment analysis are detailed herein. A data processing system can establish a performance prediction model for content items correlating constituent visual elements to sentiment performance metrics using a training dataset. The data processing system can identify a content item and candidate visual elements to insert. The content item can have constituent visual elements. The data processing system can determine a total sentiment performance metric for the content item using the performance prediction model. The data processing system can determine a combinative performance metric between the candidate visual element and the visual elements using the performance prediction model. The combinative performance metric can indicate a predicted effect on the total performance metric. The data processing system can select a candidate visual element to insert into the content item based on the combinative performance metric.