A system and method for generating personalized multimedia content element clusters. The method includes determining, based on at least one interest, at least one personalized concept, wherein each personalized concept represents one of the at least one user interest; obtaining at least one multimedia content element related to a user; generating at least one signature for the at least one multimedia content element, each generated signature representing at least a portion of the at least one multimedia content element; determining, based on the generated at least one signature, at least one multimedia content element cluster, wherein each cluster includes a plurality of multimedia content elements sharing a common concept of the at least one personalized concept; and creating at least one personalized multimedia content element cluster by adding, to each determined cluster, at least one of the at least one multimedia content element sharing the common concept of the cluster.

Methods and systems for displaying a summary of a performance of an advertising campaign are described. A system identifies advertising campaign goals of an advertising campaign. The system identifies, based on the identified goal, metrics that drive a performance of the campaign. The system identifies, for each metric, dimensions corresponding to the metric that influence a performance of the metric. The system determines, for a subset of the dimensions, that a predetermined number of criteria corresponding to the dimension contribute at least a predetermined threshold percentage of the metric. The system generates, for each metric, cards corresponding to the subset of the identified dimensions. Each card corresponds to a respective dimension and includes a graphic that identifies, for each of the predetermined number of criteria, a contribution provided by the criteria towards the performance of the metric. The system displays a subset of the cards in response to a request.

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.

Information describing deliveries of content items and user actions associated with the content items is stored. Each delivery is performed by an online publisher to a user. A user action associated with a content item performed by a target user is detected. Information describing a set of online publishers that delivered the content item to the target user is retrieved. For each online publisher of the set, a likelihood that the user action would have occurred without the online publisher's delivery of the content item to the target user is determined. An estimated increase in the likelihood that the user action occurred due to the online publisher's delivery of the content item to the target user is determined. A performance metric is determined for the online publisher, wherein ratios of performance metrics for the set of online publishers are related based on corresponding ratios of the estimated increases in likelihoods.

Methods and apparatus to incorporate saturation effects into marketing mix models are disclosed. A disclosed method includes calculating adstocked gross rating points associated with an advertising campaign during segments of a period of time. The adstocked gross rating points are based on raw gross rating points corresponding to the advertising campaign. The raw gross rating points are delivered during the segments of the period of time. The example method further includes calculating an effective reach realized during the segments of the period of time for the advertising campaign. The effective reach realized is based on the adstocked gross rating points.

Methods, systems, and computer storage media are provided for tracking an advertisement based on the advertisement's context. When an ad event is received on a client-computing device, a single URL is determined to display an item and track a context of the ad event. A first parameter related to the ad event is encoded as a HTTP header, and a second parameter related to tracking the ad event is encoded as a query parameter appended to the URL. The URL with the HTTP header is called, causing a domain server named in the URL to extract the first parameter from the HTTP header and the second parameter from the query parameter in order to determine the context of the ad. The domain server asynchronously requests tracking of the ad based on the context. Additionally, content for a landing page is received from the domain server.

The subject personalized consumer advertising/ad placement system provides the ability for advertisers, ad agencies, and any other applicable organization to determine and electronically present their “ideal” consumer profile and have their advertisement/promotion placed in front of all consumers who match the profile based on the anonymous mining of the consumers actual spending across a broad base of spending categories.

Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium for dynamic contact information assignment. A method includes: identifying a pool of telephone numbers; assigning the telephone numbers to a pool manager; allocating by the pool manager, subsets of the telephone numbers to a plurality of allocators, each allocator responsible for allocating telephone numbers to an associated group of content sponsors; determining a first allocation of a first subset, the first allocation being distributed among the content sponsors associated with a first allocator, creating first pools each associated with a respective one of the content sponsors associated with the first allocator; reclaiming one or more telephone numbers from a pool of the first pools; and assigning ones of the reclaimed telephone numbers by the first allocator into either other pools of the first pools or back to the pool manager for allocation to other allocators.

One variation of a method for calculating advertisement effectiveness includes: posting an advertisement for a product to a social feed within a social networking system; tracking a view of the advertisement by a user; determining a proximity of the user to a store of a merchant; in accordance with a privacy setting of the user, selecting personal data of the user from data stored in the social networking system, the personal data including an identity of the user and an interest of the user; in response to the determined proximity of the user to the store, transmitting the selected personal data to the store; and, in response to a transaction between the user and the store, assessing an effectiveness of the advertisement according to a determined correlation between the transaction and the view of the advertisement by the user.

Sample size determination techniques in sequential hypothesis testing in a digital medium environment are described. The sample size may be determined before a test to define a number of samples (e.g., user interactions with digital marketing content) that are likely to be tested as part of the sequential hypothesis testing in order to achieve a result. The sample size may also be determined in real time to define a number of samples that likely remain for testing in order to achieve a result. The sample size may be determined in a variety of ways, such as through simulation, based on a gap between conversion rates for different options being tested, and so on.