This specification describes technologies relating to content presentation. In general, one aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving ad information from a seller; generating, using one or more processors, a single conversion ad using the received ad information, where the single conversion ad has an available inventory of one such that only a single conversion of the ad can be performed; transmitting the single conversion ad to one or more potential buyers; receive an input from one of the one or more potential buyers; and notifying the seller of the user input. Other embodiments of this aspect include corresponding systems, apparatus, and computer program products.

Methods and apparatus to correct for deterioration of a demographic model to associate demographic information with media impression information are disclosed. An example method includes estimating first and second ages of audience members based on demographic information; estimating a third age of an audience member who is not included in the audience members; applying a window function to the second ages to determine a distribution of ages based on the third age; multiplying window values by the first ages to determine corrected first age components; dividing a total of the corrected first age components by a sum of the window values to determine an estimated age of the audience member at a first time; and determining the corrected age of the audience member at a second time based on the estimated age of the audience member at the first time and a time difference between the first and second times.

A method including training a logistic regression model to create a trained model to provide probabilities of users clicking on emails of one or more email campaigns within each of multiple different time periods. Input predictor variables of the logistic regression model include (i) user feature data including personal user features and online activity history for users in the multiple different time periods and (ii) email feature data including sent times and item category data for multiple different emails in the one or more email campaigns. Output dependent variables of the logistic regression model include responses by the users to the one or more email campaigns. The method also includes triggering sending a first email of the one or more email campaigns to a first user of the users at a selected time period based at least in part on the trained model. Other embodiments are disclosed.

Systems and methods for building a device graph for cooperative device identification are disclosed. Various information is received at a computing system over a communications network, include information defining a relationship between (i) a unique identifier associated with a first device of a user and (ii) a unique identifier associated with the user, and information defining a relationship between (i) a unique identifier associated with a second device of the user and (ii) the unique identifier associated with the user. The unique identifiers associated with the devices are each mapped to the platform-wide identifier based at least in part on the unique user identifier. A device graph comprising a plurality of device nodes is constructed, with related device nodes connected by one or more edges. Nodes representing the devices are linked based on a relationship identified between them using the platform-wide identifier.

An information processing device includes a video obtaining unit that obtains a video of the outside of a vehicle as taken by a camera installed in the vehicle; a position information obtaining unit that obtains the position information of the vehicle; a person detecting unit that detects persons based on the face images included in the video obtained by the video obtaining unit; an eye gaze direction determining unit that determines the direction of eye gaze of each person detected by the person detecting unit; a degree-of-interest determining unit that, based on the directions of eye gaze as determined by the eye gaze direction determining unit, determines the degrees of vehicle interest with respect to the vehicle; and a route setting unit that associates the position information and the degrees of vehicle interest, and sets a route that should be taken.

A method, computer system, and computer program product for billboard aesthetics and safety rating generator are provided. The embodiment may include receiving, by a processor, a plurality of information related to a billboard. The embodiment may also include extracting specific billboard information from the received data. The embodiment may further include generating ratings on safety, aesthetics and legal conformance levels of the extracted billboard information. The embodiment may also include generating a 3D model of the billboard that incorporates updates based on the generated ratings. The embodiment may further include generating improvement recommendations designed to increase the generated ratings.

A system and method for quantifying impact of script error exceptions on performance of an online retail platform. A method includes selecting at least one performance metric for a webpage, wherein the selected performance metric has an impact due to at least one script error exception encountered on the webpage visited by a first user device; retrieving a normal value for each of the at least one selected performance metric for the webpage visited by a second user device; retrieving an abnormal value for each of the at least one selected performance metric for the webpage visited by the second user device; comparing the abnormal value to the normal value of a respective selected performance metric; and determining a performance impact score based on the comparison, wherein the performance impact score is indicative of a reduction in a performance metric of each of the least one selected performance metric.

A system and methods for advertisement campaign tracking and management using near-field communications. The system is a cloud-based network containing an advertisement campaign database, a redirection server, an short message service server, near-field communication devices, a mobile application, user mobile and compute devices. Taken together or in part, said system optimizes advertising campaigns across multiple platforms, provides strong analytics for all advertising types while allowing users to engage with advertising quickly and easily through various call to action types.

A an exchange of rewardable consumer engagement opportunities includes generating a rewardable consumer engagement opportunity (RCEO) having one or more characteristics based on received entity information in response to an entity request, generating a bank of one or more rewards based on the one or more characteristics of the rewardable consumer engagement opportunity, receiving a user-initiated request for a reward based on a reported performance of a user from a communications device, determining a location of the user performance, identifying a reward in the bank of one or more rewards compatible with the reported user performance and a determined location of the user performance, transmitting the identified reward to the communications device, receiving a response from the communications device confirming acceptance or rejection of the reward and adjusting the reward bank to reflect acceptance or rejection of the reward.

A media creative attribution method includes determining a response profile within an attribution time window, the response profile being a portion of a unique visitor (UV) curve associated with a website. In some cases, a shadow baseline analysis is run on every media creative that aired within an extended time window to determine whether to adjust the response profile. A total lift within the attribution time window is determined utilizing a baseline of the UV curve. A weight for each media creative that aired within the attribution time window is determined. Utilizing the weight, the total lift is allocated to individual media creatives that aired within the attribution time window. The allocated attribution can be utilized to generate performance metrics relating to the individual media creatives that aired within the attribution time window. The performance metrics such as cost per visitor can be visualized through a user interface or dashboard.