Adobe, Inc.

Tubemogul, Inc.

John Hughes

Jason Lopatecki

Adam Rose

Brett Wilson

John M Trenkle

Kevin Thakkar

Boaz Ram

Greg Collison

Christopher Cleophus Bell

Narayan Kinhal

Darren Sue

Matthew Ellinwood

Yen Pai

Thomas Riordan

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)&#x2014;typically websites offering ads&#x2014;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.

Systems and methods for predicting and pricing of gross rating point scores by modeling viewer data

Systems and methods are disclosed for conducting media lift studies for online advertising concurrently with operating an advertising campaign. While operating an advertising campaign for a first advertiser/client focused primarily on a set of intended ads and a specific targeted viewer audience, a non-intended ad is occasionally substituted to run in an ad slot, and is tracked as belonging to the first advertiser/client. The non-intended ad can be for example one of: an ad for a second advertiser/client; an alternate ad for the first advertiser/client; or a blank/unrelated ad. After the campaign, attribution results for the intended ads are adjusted according to those for non-intended ads to provide an indication of net media lift resulting from the intended ads&#x2014;typically at no additional cost to the first advertiser/client. Analysis results may also be compared between different attribution data providers to determine which provide the more accurate attribution data.

Conducting dynamic media lift studies concurrently with operating online advertising campaigns

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. Strategies are also disclosed for fulfilling a campaign when an available inventory of known categorized viewers is not adequate to fulfill a campaign in a required runtime.

Methods for viewer modeling and bidding in an online advertising campaign

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.

Real-time bidding system that achieves desirable cost per engagement

The present disclosure relates to a content campaign system that improves the design and implementation of content campaigns. In particular, the content campaign system can receive television viewer information corresponding to television client devices and online activity information corresponding to client computing devices. Further, the content campaign system can identify a correspondence between the television viewer information and the online activity information for individual users and/or households. Based on the correspondence, the content campaign system can automatically generate targeting parameters for audiovisual content campaigns. For example, the content campaign system can recommend audiovisual content (e.g., a television advertisement) to provide to a target audience of users via a television broadcast based on the correlated television viewer information and online activity information of a particular user.

Digital audiovisual content campaigns using merged television viewer information and online activity information

Systems and methods for operating placebo-based experiments are described for online advertisements. One or more embodiments of the disclosed systems and methods utilizes an ad swapping approach to offer placebo media exposures, at no additional cost to an advertiser. One or more embodiments further provide a native experimentation platform that allows users to run tests of ad placements to measure the effectiveness of ads and view results displayed on a user interface. The disclosed systems and methods can assign viewers into a test group if shown the test ad or a control group if shown a control ad. The control ad can be provided at no cost to the advertiser for embodiments where the placebo ad belongs to an alternative advertiser. Effectiveness of third party attribution can also be evaluated. The disclosed systems and methods can define experiment parameters, including control frequency, test viewer groups, and control viewer groups.

Real-time bidding through placebo-based experimentation

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.

Targeting videos based on viewer similarity

Real-time bidding system and methods thereof for achieving optimum cost per engagement

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.

Passively monitoring online video viewing and viewer behavior

Systems and methods for conducting dynamic media lift studies concurrently with operating online advertising campaigns

Video viewer targeting based on preference similarity

A multistage online auction for electronic advertising is described including simulation. A first stage auction occurs internally within a demand-side platform where multiple advertiser clients compete to determine whose advertisement is submitted to at least one external auction site. Within the internal auction, an advertiser client optionally simulates their participation. All bidders enter a bid and campaign targeting parameters. A simulating bidder's campaign is processed in real time just as those of real bidders. Ad placement opportunities received from an external auction site are filtered for each campaign producing a list of targetable impressions. For each targetable impression, the client placing the highest real bid has their ad and bid submitted to the second stage external auction. A simulating bidder receives a report on targetable impressions for their campaign. A targetable impression for a simulated bid that is higher than any real bid is reported as a winnable impression.

Growing community of inventors

Lafayette, CA, United States of America

John Hughes