Described herein are various embodiments for intelligent advertisement generation and distribution. An embodiment operates by determining website history information about a plurality of consumers and an ad history information about the plurality of consumers. The advertisement is provided for display on a computing device for each consumer of at least a subset of consumers of the plurality of consumers responsive to a first bid opportunity for the advertisement to be displayed. Website actions of the subset of consumers to whom the advertisement was provided for display are tracked. A predictive model that the bid opportunity lead to a conversion is generated based on the website history, ad history, and the tracking of website actions. A price to bid for a second bid opportunity is generated. The generated price to bid is submitted for the second bid opportunity to display the advertisement to a marketplace.

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.

A method of determining effectiveness of an advertising campaign comprising: bidding on available advertising inventory; obtaining online impression data from at least one advertisement for at least one successful bid; matching, via a data cloud, the online impression data to a plurality of unique consumers; matching, via the data cloud, a plurality of unique consumers to at least one unsuccessful bid; determining characteristics of consumers for impression data who performed a desired event; determining characteristics of consumers for impression data who did not perform the desired event; determining characteristics of consumers from the at least one unsuccessful bid who performed the desired event; determining characteristics of consumers from the at least one unsuccessful bid who did not perform the desired event; determining consumer characteristics likely to lead to the desired event; and measuring the impact of the at least one successful bid.

Aspects of the subject technology receiving, by a campaign client module and via an advertisement network server, a hierarchical campaign identifier including one or more sub-identifiers. The campaign client module also accesses, from an aggregation server, anonymity data. The campaign client module also selects a sub-identifier from the one or more sub-identifiers of the hierarchical campaign identifier based on the anonymity data, generates a reporting data structure including a set of data based on the sub-identifier, and transmits the reporting data structure to the advertisement network server.

This application relates to apparatus and methods for maintaining a database. In some examples, a processor receives a request for a first dataset that includes a definition. In response to obtaining the first audience dataset from a database, the processor automatically re-generates the first dataset when a first predetermined time period has elapsed. The first dataset includes a first segment dataset. The processor automatically re-generates the first segment dataset when a second predetermined time period has elapsed. The first segment dataset includes a first dynamic feature dataset. The processor automatically re-generates the first dynamic feature dataset when a third predetermined time period has elapsed. After updating the first dataset (and/or subcomponents thereof), the processor transmits the first dataset to a requesting device.

Various examples are directed to systems and methods for adaptively generating leads. A marketing system may determine that a first lead score for a first lead is greater than a first lead score threshold and determine that a second lead score for a second lead is less than the first lead score threshold. The marketing system may generate a set of filtered leads including the first lead information from the first lead. The marketing system may determine a scrub rate that describes a portion of first execution cycle data having lead scores greater than the first lead score threshold and determine that the scrub rate is greater than an analysis window scrub rate by more than a scrub rate threshold. The marketing system may select a second lead score threshold that is lower than the first lead score threshold.

In one embodiment, a method includes obtaining, from a demand-side platform (DSP), impression data specifying service providers and consumer tokens representing consumers who have received digital impressions of a set of advertising campaigns. A set of tokenized claims data records related to a prescription of a product is then received from a database server. A result set of integrated measurement records specifying measured campaigns linking the tokenized claims data records with impression data associated with consumer tokens and/or service provider identifiers is further received from the database server. Aggregated analytics reports based on the integrated measurement records are generated and presented. A machine learning model may be trained using a training dataset comprising features selected from the impression data and tokenized claims data records, to predict bid values or other parameters for use in updating, optimizing or modifying operation of the DSP for the original campaign or for other campaigns.

In one embodiment, a method includes obtaining, from a demand-side platform (DSP), impression data specifying service providers and consumer tokens representing consumers who have received digital impressions of a set of advertising campaigns. A set of tokenized claims data records related to a prescription of a product is then received from a database server. A result set of integrated measurement records specifying measured campaigns linking the tokenized claims data records with impression data associated with consumer tokens and/or service provider identifiers is further received from the database server. Aggregated analytics reports based on the integrated measurement records are generated and presented. A machine learning model may be trained using a training dataset comprising features selected from the impression data and tokenized claims data records, to predict bid values or other parameters for use in updating, optimizing or modifying operation of the DSP for the original campaign or for other campaigns.

In one aspect, an example method includes (i) obtaining historical content consumption data for a content-presentation device; (ii) determining, using the historical content consumption data, a first probability of the content-presentation device viewing at least a first amount of a first advertisement segment and a second probability of the content-presentation device viewing at least a second amount of a second advertisement segment; (iii) determining a first estimated value of serving the first advertisement segment based on the first probability and a first cost of the first advertisement segment; (iv) determining a second estimated value of serving the second advertisement segment based on the second probability and a second cost associated with the second advertisement segment; (v) selecting the first advertisement segment based on the first estimated value being greater than the second estimated value; and (vi) causing the first advertisement segment to be transmitted to the content-presentation device.

Systems, methods, and storage media for calculating a reach of a media item using a plurality of universes are disclosed. In some implementations, a method of calculating a reach comprises receive a first data set from a first cross media panel indicative of a number of times the media item was exposed to users having access to a first device type and one or more other device types including a second device type; determine a first reach using the first data set; receive a second data set from a second cross media panel indicative of a number of times the media item was exposed to users having access to a second device type and the one or more other device types including the first device type; determine a second reach using the second data set; receive a third data set from a first panel indicative of a number of times the media item was consumed by users on the first device type and the second device type; determine a third reach associated with the first device type and second device type from the third data set; estimate a first device type only reach using the first reach and the third reach; estimate a second device type only reach using the second reach and the third reach; and estimate the total reach.