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.

For products that are sold online, the manner in which a product is displayed in an image can affect sales of the product. Embodiments of the present disclosure relate to computer-implemented systems and methods to provide a user with recommendations when generating an image of a product. A method includes obtaining a product image and determining parameters of the product image. A recommendation for modifying the product image is then generated using a model to relate these parameters to market success of the product image. The recommendation is displayed on the user device, and a user can potentially improve subsequent product images by following the recommendation.

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.

A system provides advertising by using a device discovery process to automatically determine an information about a home network system of a user. When it is determined that a first advertisement has been caused to be presented via a first content providing service or a first media access device, the information is used to automatically prevent a second content providing service or a second media access device from causing a second advertisement to be presented.

Methods and systems of using reinforcement learning for promotions. A first promotion is offered to a customer for a product and/or service. A first reward or penalty is determined, via a reinforcement machine learning model, based on the customer's reaction to the first promotion, wherein the reinforcement machine learning model is at a first state. Feedback to the reinforcement machine learning model is provided based on the first reward or penalty. A state of the reinforcement machine learning model is changed, based on the feedback, from the first state to a second state.

Aspects of the invention provide methods, systems, apparatuses and computer program products for implementing a dynamic experimentation service. An example of a method for implementing a dynamic experimentation service includes receiving a set of experiment configuration parameters, determining, by a processor and based on the experiment configuration parameters, an experiment model, the experiment model comprising electronic data indicating an audience selected from a plurality of consumers and a plurality of levels for controlling a communication service in operation to generate an electronic marketing communication for transmission to at least one of the plurality of consumers, receiving a request, from the communication service, the request comprising a communication identifier, determining, based on the communication identifier, at least one of the plurality of levels, and causing the electronic marketing communication to be generated as a treatment comprising the at least one of the plurality of levels.

A computing system is configured to analyze historic data generated by an optimization system to provide recommended weightings for placement of creatives on publisher's pages. The weightings may be generated by providing forecasting the likelihood that a particular creative will lead to greater conversion or revenue compared to other creatives. The creatives may be grouped into one or more phases based on the amount of statistical data available for analyzing the particular creatives such that new creatives are given sufficient weighting to receive impressions despite the lack of historical data for a creative. Performance of placed creatives may be tracked by the passing of URLs with information attached to identify the particular creative and placement.

A system and method generation of adjustable automated forecasts for a promotion. The method includes: determining, using a machine learning model, a set of forecasts each based on different parameters; determining at least one set of optimized parameters that maximize an outcome measure of the forecast for the promotion; generating a graphical representation of the forecast; receiving an adjustment to at least one parameter from a user; determining an adjusted outcome measure of the forecast for the promotion by applying the adjustment to the machine learning model; generating an adjusted graphical representation of the forecast; and displaying the adjusted graphical representation to the user.

A media distribution clearinghouse system is provided with a site selection subsystem having a site table listing a plurality of potential geographic locations, and an interface to accept value weighted geographic location selections and to supply a media enablement signal to a media unit in response to the selected geographic location, so that a media message is displayed at the selected geographic location. The media units may be stationary or mobile. The site table lists locations may be fixed stationary locations, predetermined travel routes, or non-predetermined travel routes. A media message subsystem includes a message table listing a plurality of media messages, and an interface to accept media message selections and supply the media message enablement signal for a selected media message, to the media unit. In some aspect the media message subsystem has an interface to transmit the selected media message to the media unit.

Members of an audience of a visually dynamic event are clustered using a plurality of sources. A current point of interest (POI) of the visually dynamic event and a future POI of the visually dynamic event are identified across the member clusters. An effectiveness score for given content and a given member cluster is computed for the current point of interest and an effectiveness score for a given content and a given member cluster is computed for the future POI by tracking a position, a speed, and a direction of movement of the current POI. A location of a background area is determined and ranked for each member cluster based on the current point of interest. Electronic displays that correspond to the ranked background areas are identified and ranked. Content is distributed across the electronic displays for each current time period based on the ranked display areas.