In some examples, a computing device includes at least one processor and at least one module, operable by the at least one processor to receive, from a client device of a user, a request for one or more advertisements to display at the client device with a set of messages. The set of messages is associated with the user in a social network messaging service. The at least one module may be further operable to determine a probability that the user will select a candidate advertisement using a machine learning model based on point-wise learning and pair-wise learning. The at least one module may be further operable to determine, based on the probability that the user will select the candidate advertisement, a candidate score for the candidate advertisement, determine that the candidate score satisfies a threshold, and send, for display at the client device, the candidate advertisement.

Inventory data is stored, wherein the inventory data represents for each of a plurality of broadcast stations a corresponding inventory of audio advertising spots available from that broadcast station to be filled by an advertising placement system. An advertising campaign data representing a set of advertising objectives of an advertising campaign is received. The inventory data is used to iteratively select advertising spots to be associated with the advertising campaign, based at least in part on a respective computed fitness of match between each selected advertising spot and said advertising objectives.

Disclosed embodiments relate to performing an advertisement campaign filtering process while protecting the privacy of both an advertiser and a user of a personal computing device. Techniques include maintaining a plurality of sets of advertising competition rules, the plurality of sets of advertising competition rules being associated with a plurality of discrete advertising campaigns; for a set of advertising competition rules from the plurality of sets of advertising competition rules: identifying advertisement targeting criteria associated with the set of advertising competition rules, differentiating, from within the advertisement targeting criteria, between advertisement-sensitive targeting criteria and advertiser-insensitive criteria, and transforming the advertisement-sensitive sensitive targeting criteria; and transmitting, to the personal computing device, at least a portion of the transformed advertisement-sensitive targeting criteria.

Techniques for predicting an offsite entity interaction rate are provided. One approach involves using a first machine-learned model that includes a first plurality of features that correspond to entity and campaign attributes. The approach also involves training a second machine-learned model that includes a second plurality of features that includes a particular feature corresponding to predicted entity interaction rates. Thus, output of the first machine-learned model is input to the second machine-learned model. The second machine-learned model includes multiple weights that include a particular weight for the particular feature. A content request is received and a set of campaigns is identified based on an entity identifier associated with the content request. Scores are generated based on the first and second machine-learned models. Based on the scores, a campaign is selected and campaign data associated with the campaign is transmitted over a computer network.

Disclosed are various implementations directed to systems, processes, methods, and other implementations for cross-channel call attribution by analyzing features and aspects of the incoming callnot just the call-in phone numberto more accurately determine the advertising channel actually attributable for the consumer calling in and/or allocate attribution for a consumer call-in to one or more channels accordingly.

Disclosed are various implementations directed to systems, processes, methods, and other implementations for cross-channel call attribution by analyzing features and aspects of the incoming callnot just the call-in phone numberto more accurately determine the advertising channel actually attributable for the consumer calling in and/or allocate attribution for a consumer call-in to one or more channels accordingly.

Disclosed are various implementations directed to systems, processes, methods, and other implementations for cross-channel call attribution by analyzing features and aspects of the incoming callnot just the call-in phone numberto more accurately determine the advertising channel actually attributable for the consumer calling in and/or allocate attribution for a consumer call-in to one or more channels accordingly.

The present disclosure provides operations for determining an effectiveness of an advertisement. The operations may include determining a first rate of engagement with a product, service, or message by a target group that includes one or more subjects who had an opportunity to view an advertisement for the product, service, or message at a specified location during a first time period. The operations may further include determining a second rate of engagement with the product, service, or message by a control group that includes one or more subjects present at the specified location during a second time period disjoint from the first time period and who did not have an opportunity to view the advertisement for the product, service, or message. The operations may further include determining the effectiveness of the advertisement using the first rate of engagement and the second rate of engagement.

Disclosed are various implementations directed to systems, processes, methods, and other implementations for cross-channel call attribution by analyzing features and aspects of the incoming callnot just the call-in phone numberto more accurately determine the advertising channel actually attributable for the consumer calling in and/or allocate attribution for a consumer call-in to one or more channels accordingly.

A method of classifying a product placement in a video using rule sets is disclosed. Each rule of the rule set includes a value and one or more defining rule elements. An attribute rule set is created with attribute values and attribute elements that define levels of audio visual prominence of a product in the video. An integration rule set is created with integration values and integration elements where the integration elements define levels of integration of the product with video continuity. The video is partitioned at product scene changes to create product blocks. For each product block, an attribute value is selected based on the attribute elements and an integration value is selected based on the integration elements. An impact parameter for the video is derived as a function of the selected attribute values and integration value.