A digital advertising system includes at least one processor configured to execute a plurality of functional modules including an analytics module to receive and analyze client attributes associated with a website visitor and a requested website to define an analytics event. The analytics module ingests and enriches data within the analytics event and provides it to a machine learning module that generates prediction models for potential bids. A management platform receives the bidding prediction and generates candidate configs. An optimization module receives the candidate configs and applies weights and additional features to select a config and generate an optimized script for the selected config. A deployment module receives the optimized script and delivers the script to the website visitor.

Systems and methods for incorporating intelligent virtual assistants into advertisements on social networking platforms are provided. When a user interacts with a content item, an intelligent virtual assistant is selected and put into contact with the user. The intelligent virtual assistant is provided with a context that includes information about the user in the social networking platform, information about the user in a customer relationship management platform, and information about the product, service, or entity associated with the content item. The context allows the intelligent virtual assistant to converse with the user in a way that feels natural and relevant to the user and allows the intelligent virtual assistant to answer any questions about the product, service, or entity associated with the content item.

A method for optimizing media content to be displayed across a network of DOOH advertising media and digital advertisements. The method includes dispersing sensors at locations for characterizing viewers of the media and advertisements at that location, receiving information from each device detected at each location in a database. Parameters are processed including age, gender, affinity, location, number of viewers, frequency of exposing viewers, and proportion of viewers to be targeted by specific advertisements. A model created by using prior collected information from other advertising campaigns, and from at least one sensor dispersed in at least one additional location. The model determines a list of new locations for the advertiser to target. Media content is displayed at the new location and mobile advertisements are displayed for each device. A set of locations where the advertisement will next be displayed and mobile devices are determined to be targeted. An apparatus that utilizes the method is also provided.

An on-line marketplace provides systems and methods for a mutually beneficial messaging campaign with one or more merchants, in order to promote the merchants' products. One aspect of the messaging campaign includes augmenting the merchants' budgets across multiple commercial channels. The channels may include merchant-operated channels, marketplace-operated channels, and/or third party channels. The marketplace selects when and how to augment the merchants' budgets. The augmented budget comprises an overall content budget for the messaging campaign, including a first budget portion provided by the merchant(s) and a second budget portion provided by the on-line marketplace. The augmented budget may be determined based on a weighted combination of goals of the marketplace and one or more merchants. The augmented budget may be segmented into static and dynamic portions, in which the dynamic portion is varied between channels to determine variables to optimize budget allocation and message performance.

Disclosed are methods, systems, and non-transitory computer-readable medium for targeting bid and position for a keyword. For instance, the method may include obtaining information about the keyword, the information about the keyword including observations of value with respect to position for the keyword. The method may further include applying a Gaussian Process Model on the observations to obtain a prediction function and associated uncertainties, the prediction function and the associated uncertainties relating positions to expected values; applying a Thompson sampling reinforcement learning model on the expected values and the positions to obtain a target position; and applying a bid model to the target position to obtain bid information for the keyword. The method may also include transmitting a bid message to a search engine, the bid message including the bid information.

Aspects of the subject disclosure may include, for example, determining conversions associated with an advertising campaign, identifying consumers associated with the conversions. Further embodiments can include determining an attribution window for the advertising campaign, identifying first advertisements of the advertising campaign exposed to the consumers during the attribution window. Additional embodiments include identifying an advertising medium for each of the first advertisements resulting in a plurality of advertising mediums, and adjusting the advertising campaign according to the conversions and the plurality of advertising mediums resulting in an adjusted advertising campaign. Also, embodiments include delivering, over a communication network, second advertisements associated with the adjusted advertising campaign to communication devices associated with target households. A portion of the second advertisements is presented on each of the communication devices. Other embodiments are disclosed.

Described are a system, method, and computer program product for generating a synthetic control group. The method includes receiving transaction account data and transaction data associated with transactions completed by a first set of transaction accounts with a target merchant. The method also includes generating a synthetic control group including a subset of transaction accounts sampled from the first set of transaction accounts. The method further includes determining, for each transaction account of the synthetic control group, a propensity score. The method further includes assigning an entropy balancing weight to each transaction account of the synthetic control group. The method further includes altering, based on the synthetic control group, at least one operational parameter of a computer-implemented advertisement program to be executed.

The present disclosure describes a dynamic optimization of delivery of one or more advertising messages of an advertising campaign. The various apparatuses and methods disclosed herein deliver the one or more advertising messages to a targeted audience of the advertising campaign. Thereafter, these apparatuses and methods monitor exposure of the targeted audience to the one or more advertising messages to generate an advertisement exposure list. This list can include members of the targeted audience that have not been exposed to the one or more advertising messages, members of the targeted audience that have been exposed to the one or more advertising messages less than an optimal frequency of exposure, and/or members of the targeted audience that have been exposed to the one or more advertising messages greater than or equal to the optimal frequency of exposure. Finally, these apparatuses and methods optimize the delivery of the one or more advertising messages to the members of the targeted audience that have not been exposed to the one or more advertising messages and/or the members of the targeted audience that have been exposed to the one or more advertising messages less than the optimal frequency of exposure as indicated in the advertisement exposure list when the one or more advertising messages fail to reach the members of the targeted audience that have not been exposed to the one or more advertising messages and/or are exposed to the one or more advertising messages less than the optimal frequency of exposure.

Techniques and arrangements for generating, distributing and managing promotion campaigns are described. A promotion server may generate the promotion campaigns based on input from a merchant. The input may include a request to generate the promotion campaign, a merchant preference, or other information shared between the merchant and the promotion server. The promotion campaign may include promotions, such as coupons, discounts, or the like, to encourage transactions with a merchant. The promotions may be generated based on merchant specified criteria, a merchant transaction history, customer preferences, a customer transaction history, and/or other information processed by the promotion server. The promotions of the promotion campaign may be distributed via one or more channels, such as electronic mail, web site publication, receipts, etc. Each promotion of the promotion campaign may be linked to a particular customer, thereby limiting the number of times a particular customer can take advantage of the promotion campaign.

A method, computer system, and computer program product for asynchronous content embedding is provided. The embodiment may include receiving a plurality of text and data associated with an element of media content. The embodiment may also include displaying the plurality of received text on a display screen. The embodiment may further include caching the received data until all data to fully display the element is received. The embodiment may also include identifying an insertion point for the element within the plurality of displayed text. The embodiment may further include displaying the element at the insertion point.