Abstract: A processing device receives set-top box data, sales data, and advertising data. The processing device associates set-top boxes from the set-top box data with viewers and calculates advertising exposure events for advertisements. The processing device further calculates, for each viewer, an advertising weight as a function of number of exposures of the advertisement as applied to the viewer based on the advertising exposure events. The processing device further calculates, for each viewer, a score representing a degree-of-targetedness between an advertisement campaign and the viewer. The processing device manages the advertisement campaign based on the advertising weight and the degree-of-targetedness.

Abstract: This paper presents a practical method for measuring the impact of multiple marketing events on sales, including marketing events that are not traditionally trackable. The technique infers which of several competing media events are likely to have caused a given conversion. The method is tested using hold-out sets, and also a live media experiment for determining whether the method can accurately predict television-generated web conversions.

Abstract: Systems and methods are disclosed for optimizing data element usage according to user-defined objectives, comprising receiving a plurality of user-defined objectives associated with a group of data elements; receiving one or more constraints associated with the group of data elements, wherein at least one of the constraints comprises resources apportionable to each data element in the group of data elements; apportioning at least a portion of the resources to each data element in the group of data elements in a manner that meets the one or more constraints; receiving metrics associated with the performance of the group of data elements in meeting the plurality of user-defined objectives; determining an effectiveness of each data element in the group of data elements for meeting the plurality of user-defined objectives; and automatically revising the at least a portion of resources associated with each data element in the group of data elements.

Abstract: Television is the largest advertising category in the United States with over 65 billion spent by advertisers per year. A variety of different targeting algorithms are compared, ranging from the traditional age-gender targeting methods employed based on Nielsen ratings, to new approaches that attempt to target high probability buyers using Set Top Box data. The performance of these different algorithms on a real television campaign is shown, and the advantages and limitations of each method are discussed. In contrast to other theoretical work, all methods presented herein are compatible with targeting the existing 115 million Television households in the United States and are implementable on current television delivery systems.

Abstract: Systems and methods are disclosed for targeting of advertising content for a consumer product, by obtaining consumer demographic data, the consumer demographic data including a plurality of demographic attributes for each person; identifying a plurality of media slots; and obtaining program information for a respective identified program aired in each media slot among the plurality of media slots, the program information including viewing data of a plurality of viewing persons viewing the program and each viewing person being among the plurality of persons.

Abstract: Systems and methods are disclosed for optimizing distribution of resources to data elements, comprising receiving a designation of one or more data elements for distribution; receiving a designation of a plurality of electronic event types detectable over a network, the plurality of electronic event types being associated with receipt or electronic display of at least one data element of the one or more data elements, to be tracked; forecasting a return on investment, associated with distribution of the one or more data elements, based on a forecast of occurrences of the plurality of electronic event types; and optimizing distribution of resources to the one or more data elements based on the forecasted return on investment.

Abstract: Systems and methods are disclosed that measure web activity bursts after ad broadcasts that may be sent to multiple persons. One system uses a cookie-less/cookie-optional, anonymous/personal-identification-not-required, method for web-based conversion tracking that will work on broadcast media systems such as television, and could also be applied to measuring spikes from email, radio, and other forms of advertising where an episodic ad event is broadcast to multiple parties, and where responses occur in a batch after the broadcast.

Abstract: The present disclosure describes a system that attempts to reconcile diverse goals and re-cast the goals into something that is quantifiable and optimizable. One way to reconcile diverse goals is by converting these “constraints”—with the huge problems of feasibility—into errors that can be minimized. This disclosure also presents solutions for rate constraints which previously have not been dealt with. The resulting system enables advertisers to dynamically adjust their campaign based on the needs of the moment. Such a system can have advantages in terms of controllability, smoothness, as well as avoiding hard stop conditions that plague the constraint-based approach. In order to achieve this result, solutions are presented for problems of pacing, viewability prediction, and most particularly, error minimization.

Abstract: A method of targeting of advertising content for a consumer product is disclosed. The method comprises obtaining consumer demographic data from a first server over a network, the consumer demographic data including a plurality of demographic attributes for each person among a plurality of persons; obtaining product purchaser data for a plurality of product purchasers of the consumer product from a second server over the network, each product purchaser among the plurality of product purchasers being among the plurality of persons; and enriching the purchaser data with the consumer demographic data. The method further comprises enriching viewing data with consumer demographic data; and selecting viewed media among the aggregated viewed media having the highest similarity to the product purchasers as target media for the advertising content.

Abstract: Systems and methods are disclosed for optimizing distribution of resources to data elements, comprising receiving one or more user-defined objectives associated with a group of data elements, wherein at least one of the user-defined objectives includes an objective related to a selected target group; receiving one or more constraints associated with the group of data elements, wherein at least one of the constraints comprises resources apportionable to each data element in the group of data elements; developing a first prediction of a performance of the group of data elements during a time period based on the one or more user-defined objectives and the one or more constraints; and apportioning at least a portion of the resources to each data element in the group of data elements based on the first prediction once the time period has started.

Abstract: An ad player presents ads in association with a video player by evaluating an associated ad script. The ad player transforms data included in the ad script into operational instructions. Hence, the ad player flexibly and dynamically configures itself and presents ads in accordance with the contents of the ad script, enabling a publisher to modify advertising aspects simply by modifying the ad script. The ad script can comprise a script in a tag-based markup language that is readable by the ad player. For example, the ad script can include one or more tags, each tag including one or more attributes that are each set to a value. The ad player determines the values of the attributes and presents ads in accordance with associated ad characteristics or behaviors.

Abstract: Systems and methods are disclosed for targeting of advertising content for a consumer product, by obtaining consumer demographic data, the consumer demographic data including a plurality of demographic attributes for each person; identifying a plurality of media slots; and obtaining program information for a respective identified program aired in each media slot among the plurality of media slots, the program information including viewing data of a plurality of viewing persons viewing the program and each viewing person being among the plurality of persons.

Abstract: A processing device selects a population of persons and measures sales metrics from the population over a time period and measures an advertising weight over the time period. The processing device determines an effect that the advertising weight has on the sales metrics and additionally calculates values for a degree of targetedness for the advertisement to the population of persons. The processing device determines an effect that the degree of targetedness has on the sales metrics and generates a multi-dimensional model that measures the combined effects of the advertising weight and the degree of targetedness on the sales metrics.

Abstract: A processing device receives set-top box data, sales data, and advertising data. The processing device associates set-top boxes from the set-top box data with viewers and calculates advertising exposure events for advertisements. The processing device further calculates, for each viewer, an advertising weight as a function of number of exposures of the advertisement as applied to the viewer based on the advertising exposure events. The processing device further calculates, for each viewer, a score representing a degree-of-targetedness between an advertisement campaign and the viewer. The processing device manages the advertisement campaign based on the advertising weight and the degree-of-targetedness.

Abstract: A method of targeting of advertising content for a consumer product is disclosed. The method comprises obtaining consumer demographic data from a first server over a network, the consumer demographic data including a plurality of demographic attributes for each person among a plurality of persons; obtaining product purchaser data for a plurality of product purchasers of the consumer product from a second server over the network, each product purchaser among the plurality of product purchasers being among the plurality of persons; and enriching the purchaser data with the consumer demographic data. The method further comprises enriching viewing data with consumer demographic data; and selecting viewed media among the aggregated viewed media having the highest similarity to the product purchasers as target media for the advertising content.

Abstract: Many conventional advertisements designed for display as part of a web page executed by a web browser are not suitable for display in an online video player that, though included in a web page, is executed by a multimedia platform. Such conventional advertisements are referred to herein as web page advertisements. A system display a web page advertisement in the frame of such an online video player by determining characteristics of the video player in the context of the multimedia platform and converting those characteristics into a context compatible with the web browser. The system can also include a virtual web browser that executes either on the multimedia platform on a server device that is communicatively coupled to the multimedia platform. The system can also include an ad tag translator that executes either on the multimedia platform on a server device that is communicatively coupled to the multimedia platform.

Abstract: Systems and methods are disclosed that measure web activity bursts after ad broadcasts that may be sent to multiple persons. One system uses a cookie-less/cookie-optional, anonymous/personal-identification-not-required, method for web-based conversion tracking that will work on broadcast media systems such as television, and could also be applied to measuring spikes from email, radio, and other forms of advertising where an episodic ad event is broadcast to multiple parties, and where responses occur in a batch after the broadcast.

Abstract: Systems and methods are disclosed for optimizing distribution of resources to data elements, comprising receiving a designation of one or more data elements for distribution; receiving a designation of a plurality of electronic event types detectable over a network, the plurality of electronic event types being associated with receipt or electronic display of at least one data element of the one or more data elements, to be tracked; forecasting a return on investment, associated with distribution of the one or more data elements, based on a forecast of occurrences of the plurality of electronic event types; and optimizing distribution of resources to the one or more data elements based on the forecasted return on investment.

Abstract: Systems and methods are disclosed for targeting of advertising content for a consumer product, by obtaining consumer demographic data, the consumer demographic data including a plurality of demographic attributes for each person; identifying a plurality of media slots; and obtaining program information for a respective identified program aired in each media slot among the plurality of media slots, the program information including viewing data of a plurality of viewing persons viewing the program and each viewing person being among the plurality of persons.

Abstract: Systems and methods are disclosed for optimizing distribution of resources to data elements, comprising receiving a selection of a first objective and a second objective, the first objective and second objective comprising goals associated with distribution of a plurality of data elements; receiving an indication that the first objective has a higher priority than the second objective; receiving a first goal metric associated with the first objective and a second goal metric associated with the second objective; determining a first forecasted metric based on the first goal metric associated with the first objective; determining a second forecasted metric based on the second goal metric associated with the second objective; and allocating resources for the distribution of a plurality of data elements based on the first goal metric, the second goal metric, the first forecasted metric, the second forecasted metric, and the indication that the first objective has a higher priority than the second objective.