A server comprises a storage unit for storing a plurality of image data, a first end input request image data including a redisplay setting image for setting whether to display the image on the information processor again or not and a first end request image for ending the display of the image, a second end input request image data including a second end request image for rejecting the redisplay of the image and ending the display of the image, and association data in which one of the first end input request image data and the second end input request image data is stored in association with the types of the plurality of image data, and a controller for executing a process of transmitting any one image data of the plurality of image data and either the first end input request image data or the second end input request image data associated based on the association data corresponding to the image data to the information processor.

According to some embodiments, a method performed by an end user electronic device comprises: triggering a digital content display opportunity; generating an opportunity identifier that uniquely identifies the digital content display opportunity within a digital content display opportunity bidding system; obtaining demographic information associated with a user of the end user electronic device; and transmitting the opportunity identifier and the demographic information to a network node in the digital content display opportunity bidding system.

Disclosed herein is a platform utilizing data processing to accelerate sales by personalizing user interactions based on user behavior. Network-based sales platforms allow merchants to offer goods or services to customers online. However, existing technical solutions may lack the functionality that allows determining what goods and services would be of interest to a particular customer. The lack of such functionality may result in low conversion rates. As a result of the poor conversion rates, merchants incur unnecessary expenses in blanket advertising to customers who are unlikely to buy the advertised goods and services.

Methods, apparatuses, systems, and computer-readable media for identifying and executing one or more interactive condition evaluation tests and collecting and analyzing user behavior data to generate an output are provided. In some examples, user information may be received and one or more interactive condition evaluation tests may be identified. An instruction may be transmitted to a computing device of a user and executed on the computing device to enable functionality of one or more sensors that may be used in the identified tests. Upon initiating a test, data may be collected from the one or more sensors. The collected sensor data may be transmitted to the system and processed using one or more machine learning datasets. Additionally, user behavior data may be collected and processed using one or more machine learning datasets. The sensor data, the user behavior data, and other data may be used together to generate an output.

Methods and apparatus to associate transactions with media impressions are disclosed. An example method includes transmitting a request for commercial transaction information to a database proprietor, the request including an identifier corresponding to a media impression associated with media presented via a computing device; receiving the commercial transaction information in response to the request, the commercial transaction information comprising data associated with a commercial transaction conducted using an account accessed by the computing device; and associating the media impression with the commercial transaction.

A method is provided, comprising: identifying a plurality of communications sessions and obtaining a respective transcript of each of the plurality of communications sessions, each of the plurality of communications sessions involving a respective one of a plurality of customers; generating a plurality of label sets, each of the label sets corresponding to a different one of the plurality of customers; generating a plurality of customer signatures, each of the plurality of customer signatures being generated based on a different one of the plurality of label sets; classifying, with a first neural network, each of the plurality of customer signatures into one of a “re-engage” category and a “do-not-re-engage” category; and returning a customer re-engagement list that identifies respective ones of the plurality of customers who are associated with customer signatures that are classified in the “re-engage” category.

This application relates to apparatus and methods for automatically determining and providing digital advertisements to targeted users. In some examples, a computing device receives campaign data identifying items to advertise on a website, and generates campaign user data identifying a user that has engaged all of the items on the website. The computing device may then determine a portion of the users based on a relationship between each user and the campaign user data, and may determine user-item values for each of the items for each user of the portion of users, where each user-item value identifies a relational value between the corresponding user and item. The computing device may then identify one or more of the items to advertise to each user of the portion of users based on the user-item values, and may transmit to a web server an indication of the items to advertise for each user.

Systems and related methods providing for dynamic message creation are discussed herein. Circuitry may be configured receive generic configuration data that references work chunks for message generation and time data indicating a message send time. The work chunks may include references to message data, but not the actual message data. Based on the time data, the work chunks may be placed in a message queue. Circuitry may also be configured to retrieve a work chunk from the message queue and to generate one or more messages based on the retrieved work chunk. The references to message data in the work chunk may be used to retrieve the message data at the time of message generation.

Branded content, or a target asset, may be included in a set of ordered assets based on the category of an anchor asset. Fill rates, total views of the target asset, or a combination may be used in selecting an optimization strategy. A dual optimization may be used to reduce the burden of presentation based on historical yield rates and follow-on rates observed from category transition data. Serendipity may be incorporated in the process through use of a reserve pool of transitions.

Contextual data may be generated from assets in asset portfolios using metadata enrichment services. A recommendation engine may generate a set of recommended assets for presentation in a content stream based on the contextual data. Brand safety may be implemented using a brand safety policy that uses the contextual data as indicators of potentially offensive content. Advertisements included in the content stream may also be targeted based on the contextual data.