A computer-implemented method for dynamic link redirection includes receiving a request to display online content to a user, determining a plurality of current content links in the online content, for each current content link among the plurality of current content links, performing operations including: obtaining current assessment parameters for the current content link, generating a current assessment for the current content link based on the current assessment parameters, obtaining a plurality of alternative content links based on the current content link, generating a plurality of alternative assessments for each alternative content link among the plurality of alternative content links based on respective alternative assessment parameters for the alternative content link, determining a selected content link among the plurality of alternative content links based on the current assessment and the plurality of alternative assessments, replacing the current content link in the online content with the selected content link, and displaying the online content to the user.

According to an embodiment, a promotion support server includes a storage unit, a communication interface, and a processor. The processor is configured to acquire promotion results data including a plurality of feature parameters for recipients of a promotion related to a product, identify feature parameters in the plurality of feature parameters that are in a multicore relationship, store the identified feature parameters in the storage unit in association with a product identification, cause a display to display a promotion target setting screen on which feature parameters of a recipient of another promotion related to the product can be set by an operator, and cause the display to display a notification when a designated feature parameter among the feature parameters on the promotion target setting screen is one identified as being in a multicore relationship.

Methods, apparatus, systems and articles of manufacture are disclosed to generate a media rank. An example disclosed method includes (a) identifying households in which the product of interest was purchased, (b) calculating consumption factors for respective ones of the households based on a consumption total of the product of interest for the corresponding household and a household weight, (c) selecting first candidate media of interest, (d) calculating a rating value indicative of a relevance of the product of interest to the candidate media of interest based on the mathematical product of the consumption factor and a viewing time for corresponding ones of the households, (e) repeating (c) and (d) for second media, and (f) ranking the first and second media for the product of interest.

Systems and methods are disclosed for planning, executing, reviewing, and reporting the results of an advertising campaign to be run on TV. A demand-side platform receives ad slot opportunities from TV programming sources, and analyzes the ad slots to produce a prioritized list of placement opportunities for the advertising campaign to be presented to advertiser/clients. Each ad slot is analyzed with respect to past viewership data and with respect to desired targeting characteristics that may include conventional age and gender targeting, or additionally strategic targeting characteristics. Scores are established for each ad slot with respect to numbers of projected on-target impressions and/or a cost for projected on-target impressions. The scores are sorted to produce the prioritized list. Projected results can be viewed with respect to any or all of network, day, and daypart. After a campaign has completed, viewership data representing actual results is acquired, processed, and reported.

Systems and methods for federating social network content items include determining an engagement value for content items based on previous interactions with content items from first and second content item sources stored in an electronic data storage, certain content items having selection metrics and value metrics according to varying selection schemas and a value schema. A utility value is generated for each of the at least some of the content items, the utility value including an engagement component and a monetary component. A user device displays the content items in an order based on the utility values.

A cross-channel advertising campaign system is provided. The system comprises a memory, a processor, and an application. The application is stored in the memory, executable by the processor, and configured such that the cross-channel advertising campaign system receives advertising content designed for display in a first advertising channel, and further configured such that the advertising content is converted into a design for display in a second advertising channel. The conversion includes associating an address in the first advertising channel and an address in the second advertising channel with a single user.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reducing latency in network communications and data presentation. In one aspect, a user session is initiated in which data related to an account is presented to the user. A user group to which the given user has been assigned is identified. A first dataset related to the account is selected based on the user group. A second dataset related to the account is selected based on types of data previously requested by various other users in the user group. A user interface for the account is updated to present at least a portion of the first dataset. Latency in updating the user interface is reduced when presenting additional portions of the first dataset or the second dataset by providing, to the client device, the second dataset prior to receiving a request for the second dataset.

A system and method for tracking users across a plurality of media platforms are provided. The method includes generating unified user-level data of each user across the media of advertising platforms; storing the unified user-level data generated for each user in a storage; taping into the plurality of advertising platforms to render pixel trafficking data; and analyzing raw data based on which the unified user-level data is generated.

A method for finding a solution to a problem is provided. The method includes storing candidate individuals in a candidate pool and evolving the candidate individuals by performing steps including (i) testing each of the candidate individuals to obtain test results, (ii) assigning a performance measure to the tested candidate individuals, (iii) discarding candidate individuals from the candidate pool in dependence upon their assigned performance measure, and (iv) adding, to the candidate pool, a new candidate individual procreated from candidate individuals remaining in the candidate pool. The method further includes selecting, as the winning candidate individual, a candidate individual having a best neighborhood performance measure, where the neighborhood performance measure of a particular candidate individual is given by the performance measures of (i) the particular candidate individual and (ii) K neighborhood candidate individuals which are nearest in the candidate pool to the particular candidate individual, and where K>0.

Techniques are provided for building a unified model for selecting content items of different types in response to receiving electronic content requests transmitted over a network. In one technique, in response to a request, multiple content items are identified. The multiple content items include a first content item of a first type and a second content item of a second type. A first engagement value that indicates a first level of engagement of an online resource for content items of the first type is determined. A first predictive user selection rate is generated for the first content item based on the first engagement value. A second predictive user selection rate is generated for the second content item. The multiple content items are ranked based, at least in part, on the predictive user selection rates. A particular content item is then selected based on the predictive user selection rates.