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: 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 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: 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 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 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: 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: 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 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: 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 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 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 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: 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: 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: Systems and methods are disclosed for demographic prediction based on aggregated training data. The predictions are based on auditing aggregated data associated with identified properties of web requests. The audited information is based on a batch of prior web requests that have the same property and have been audited by a measurement company.

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.