A computer-implemented method for conversion timing inference. A conversion timing model is model is configured to predict a likelihood of conversion based on an entity's elapsed time since a qualified entry event. The conversion timing model is constructed based on a distribution of the conversion timespans of converters. Each conversion timespan describes a length of time between a qualified entry event and a conversion event for a converted entity. A notification of an opportunity to expose a candidate entity to networked content is received and the likelihood of conversion for the candidate entity is determined by: determining an elapsed time since a qualified entry event for the candidate entity and applying the conversion timing model to the elapsed time. A response to the notification based on the likelihood of conversion for the candidate entity is prepared. Timely responses may include the selection of customized content, customized advertising content or bid values.

A digital medium environment is described to recommend advertisements using ranking functions. A ranking function is configured to compute a score by applying a user context vector associated with a user to individual ranking weight vectors associated with advertisements, and provide the advertisement with the highest score to the user. In order to learn the ranking weight vectors for the ranking function, training data is obtained that includes user interactions with advertisements during previous sessions as well as user context vectors. The ranking weight vectors for the ranking function associated with each advertisement can then be learned by controlling the score generated by the ranking function to be higher for positive interactions than the negative interactions. To do so, the ranking weight vectors may be learned by optimizing an area under the curve ranking loss (AUCL) for the ranking function.

One or more computing devices, systems, and/or methods for selecting content items for transmission to client devices are provided. A first content item may be transmitted to a first set of client devices. A first request for content associated with a first client device of a second set of client devices may be received. A first bid value associated with a second content item may be selected. The first bid value may be modified based upon a second bid value associated with the first content item to generate a third bid value associated with the second content item. The second content item may be selected from a first plurality of content items for presentation via the first client device based upon a plurality of bid values having the third bid value. The second content item may be transmitted to the first client device.

Approaches provide for evaluating lift associated with supplemental content based on a synthetic exposure event. Users may be separated into groups of exposed users that have interacted with supplemental content and an unexposed group that has not interacted with the supplemental content. Users within the unexposed group may be ranked and sorted into a subset control group. The subset control group may be presented with synthetic exposure events that monitor conversions for the supplemental content in the same manner as the exposed group. Thereafter, conversion rates may be compared to determine the impact of the supplemental content.

The disclosure includes a camera array comprising camera modules, the camera modules comprising a master camera that includes a processor, a memory, a sensor, a lens, a status indicator, and a switch, the switch configured to instruct each of the camera modules to initiate a start operation to start recording video data using the lens and the sensor in the other camera modules and the switch configured to instruct each of the camera modules to initiate a stop operation to stop recording, the status indicator configured to indicate a status of at least one of the camera modules.

Examples relating to attracting the gaze of a viewer of a display are disclosed. One example method comprises controlling the display to display a target object and using gaze tracking data to monitor a viewer gaze location. A guide element is displayed moving along a computed dynamic path that traverses adjacent to a viewer gaze location and leads to the target object. If the viewer's gaze location is within a predetermined divergence threshold of the guide element, then the display continues displaying the guide element moving along the computed dynamic guide path to the target object. If the viewer's gaze location diverts from the guide element by at least the predetermined divergence threshold, then the display discontinues displaying the guide element moving along the computed dynamic guide path to the target object.

An example metrics manager determines a first audience reach for a television audience, the television audience representative of audience members exposed to a media campaign via television media delivery, determine a second audience reach for a digital audience, the digital audience representative of audience members exposed to the media campaign via digital media delivery. An example deduplicator obtains an overlap multiplier based on the media campaign, the overlap multiplier is a ratio of (1) a product of a panel duplication reach and a did-not-view reach and (2) a product of a television panel reach and a digital panel reach, and determine a duplication factor for the media campaign based on the first audience reach, the second audience reach and the overlap multiplier. An example audience manager determines a total audience for the media campaign based on the first audience reach, the second audience reach and the duplication factor.

Methods, systems, and media for managing online advertising campaigns based on causal conversion metrics are provided. In some embodiments, the method comprises: receiving conversion information corresponding to test group including consumers that were presented with an advertisement using an advertising channel; receiving advertisement viewability information indicative of a probability that each of the consumers viewed the advertisement; determining that a subset of the consumers did not view the advertisement based on the probability; placing the consumers into a control group and a test group based on the probability corresponding to each of the consumers; calculating a causal conversion metric based on a comparison of the conversion information corresponding to consumers of the control group and conversion information corresponding to consumers of the test group; and determining whether to place an advertisement using the advertising channel based on the causal conversion metric.

In general, embodiments of the present invention provide systems, methods and computer readable media for ranking promotions selected for recommendation to consumers based on predictions of promotion performance and consumer behavior. In embodiments, a set of promotions to be recommended to a consumer can be sorted and/or ranked according to respective relevance scores representing a probability that the consumer's behavior in response to the promotion will match a ranking target. In embodiments, calculating scores is based on a relevance model (a predictive function) derived from one or more contextual data sources representing attributes of promotions and consumer behavior. In embodiments, an absolute relevance score represents an absolute prediction of a ranking target variable. In embodiments, absolute relevance may be used to determine personalized local merchant discovery frontiers; featured result set thresholding for impressions; and/or promotion notification triggers. In embodiments, predictive models based on gross revenue may be optimized using promotion category-dependent price boosting.

In general, embodiments of the present invention provide systems, methods and computer readable media for ranking promotions selected for recommendation to consumers based on predictions of promotion performance and consumer behavior. In embodiments, a set of promotions to be recommended to a consumer can be sorted and/or ranked according to respective relevance scores representing a probability that the consumer's behavior in response to the promotion will match a ranking target. In embodiments, calculating scores is based on a relevance model (a predictive function) derived from one or more contextual data sources representing attributes of promotions and consumer behavior. In embodiments, an absolute relevance score represents an absolute prediction of a ranking target variable. In embodiments, absolute relevance may be used to determine personalized local merchant discovery frontiers; featured result set thresholding for impressions; and/or promotion notification triggers. In embodiments, predictive models based on gross revenue may be optimized using promotion category-dependent price boosting.