Adobe, Inc.

Kevin Thakkar

Jason Lopatecki

John Hughes

Adam Rose

John M Trenkle

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 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.

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Sunnyvale, CA, United States of America

Kevin Thakkar