Abstract: Systems and methods are disclosed for analyzing a fused sample of viewership data to determine a behavior profile of online viewers who watched and/or didn't watch certain TV advertisements, where the TV advertisements are aligned with campaign targeting characteristics desired by an advertiser/client working with a demand side platform. Then, a campaign targeting plan is developed for dividing an advertising budget between digital media and TV impressions. The digital media portion of the campaign profiles Media Properties (MPs) contained in a historical database from past digital advertising campaigns across multiple digital formats and screens, and aligns digital ad placement with MPs having desired targeting characteristics. An optimized apportionment is automatically produced between TV and digital media spending based on an advertiser/client's goals of duplicating or not duplicating viewership of an advertisement between TV and digital media, or alternately based on cost alone.

Abstract: Systems and methods are disclosed for analyzing a fused sample of viewership data to determine a behavior profile of online viewers who watched and/or didn't watch certain TV advertisements, where the TV advertisements are aligned with campaign targeting characteristics desired by an advertiser/client working with a demand side platform. Then, a campaign targeting plan is developed for dividing an advertising budget between digital media and TV impressions. The digital media portion of the campaign profiles Media Properties (MPs) contained in a historical database from past digital advertising campaigns across multiple digital formats and screens, and aligns digital ad placement with MPs having desired targeting characteristics. An optimized apportionment is automatically produced between TV and digital media spending based on an advertiser/client's goals of duplicating or not duplicating viewership of an advertisement between TV and digital media, or alternately based on cost alone.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods are disclosed for optimizing an online advertising campaign both before the campaign begins, and dynamically during the campaign. Optimizations are performed comparatively between a plurality of MPs (Media Properties) based on their relative cost-per-engagement. Comparisons are performed by first stack ranking MP inventory including any of sites, feeds, and verticals, based on cost per engagement. Once ranked, scores are assigned to the targeted inventory and a mean score is determined. Then, the inventory is rated as high, normal, or low impact based on their scores compared with the mean and a standard deviation for all scores. Higher impact sites with scores at least a standard deviation above the mean are initially favored, and the MP targeting strategy is dynamically adjusted during the campaign based on periodically re-evaluating the MP rankings, frequencies of engagement, and campaign progress relative to fulfillment in an allotted run time.

Abstract: Systems and methods are disclosed for employing supervised machine learning methods with activities and attributes of viewers with truth as input, to produce models that are utilized in determining probabilities that an unknown viewer belongs to one or more demographic segment categories. Using these models for processing viewer behavior, over a period of time a database of known categorized viewers is established, each categorized viewer having a probability of belonging to one or more segment categories. These probabilities are then used in bidding for online advertisements in response to impression opportunities offered in online media auctions. The probabilities are also used in predicting on-target impressions and GRPs (Gross Rating Points) in advance of online advertising media campaigns, and pricing those campaigns to advertiser/clients.

Abstract: Systems and methods are disclosed for characterizing websites and viewers, for predicting GRPs (Gross Rating Points) for online advertising media campaigns, and for pricing media campaigns according to GRPs delivered as opposed to impressions delivered. To predict GRPs for a campaign, systems and methods are disclosed for first characterizing polarized websites and then characterizing polarized viewers. To accomplish this, a truth set of viewers with known characteristics is first established and then compared with historic and current media viewing activity to determine a degree of polarity for different Media Properties (MPs)—typically websites offering ads—with respect to gender and age bias. A broader base of polarized viewers is then characterized for age and gender bias, and their propensity to visit a polarized MP is rated. Based on observed and calculated parameters, a GRP total is then predicted and priced to a client/advertiser for an online ad campaign.

Abstract: Various user behaviors are passively monitored and recorded when a user/viewer interacts with a network video player, e.g. a web video player, while watching an online video clip. For one embodiment, a data collection agent (DCA) is loaded to the player and/or to a web page that displays the video clip. The DCA passively collects detailed viewing and behavior information without requiring any specific input or actions on the part of the user. Indications of user preferences are inferred by user actions leading up to viewing the video, while viewing the video, and just after and still related to viewing the video. The DCA periodically sends this information to a central server where it is stored in a central database and where it is used to determine preference similarities among different users. Recorded user preference information may also be used to rate a video itself.

Abstract: Various user behaviors are passively monitored and recorded when a user/viewer interacts with a network video player, e.g. a web video player, while watching an online video clip. For one embodiment, a data collection agent (DCA) is loaded to the player and/or to a web page that displays the video clip. The DCA passively collects detailed viewing and behavior information without requiring any specific input or actions on the part of the user. Indications of user preferences are inferred by user actions leading up to viewing the video, while viewing the video, and just after and still related to viewing the video. The DCA periodically sends this information to a central server where it is stored in a central database and where it is used to determine preference similarities among different users. Recorded user preference information may also be used to rate a video itself.

Abstract: Presentation of a video clip is made to persons having a high probability of viewing the clip. A database containing viewers of previously offered video clips is analyzed to determine similarities of preferences among viewers. When a new video clip has been watched by one or more viewers in the database, those viewers who have watched the new clip with positive results are compared with others in the database who have not yet seen it. Prospective viewers with similar preferences are identified as high likelihood candidates to watch the new clip when presented. Bids to offer the clip are based on the degree of likelihood. For one embodiment, a data collection agent (DCA) is loaded to a player and/or to a web page to collect viewing and behavior information to determine viewer preferences. Viewer behavior may be monitored passively by different disclosed methods.

Abstract: Various user behaviors are passively monitored and recorded when a user/viewer interacts with a network video player, e.g. a web video player, while watching an online video clip. For one embodiment, a data collection agent (DCA) is loaded to the player and/or to a web page that displays the video clip. The DCA passively collects detailed viewing and behavior information without requiring any specific input or actions on the part of the user. Indications of user preferences are inferred by user actions leading up to viewing the video, while viewing the video, and just after and still related to viewing the video. The DCA periodically sends this information to a central server where it is stored in a central database and where it is used to determine preference similarities among different users. Recorded user preference information may also be used to rate a video itself.