Systems and methods are described herein for resolving advertisement placement conflicts. Specifically, a number of parameters may be entered into a system in order to distribute advertisements into advertisement slots. In many instances, a combination of these parameters causes a conflict in the system where all the parameters cannot be applied in order to place advertisements into advertisement slots. The conflict may be resolved by using an advertisement assignment model to determine which parameters may be relaxed in order to arrive at an optimal solution that violates a smallest number of parameters having the least priority. When such a solution is found, the advertisement assignment model may be modified and advertisements may be placed into advertisement slots based on the modified advertisement assignment model.

The technology is generally directed to determining whether a consumer being presented with a digital component is a disengaged and/or qualifying disengaged consumer at the time the digital component is being selected. An artificial intelligence model may be trained and used to predict whether the consumer is a disengaged and/or qualifying disengaged consumer. The prediction may be a probability representing the likelihood that the user is a disengaged and/or qualifying disengaged consumer for a given merchant. The probability may be used to dynamically adjust the merchant's bid at the time of auction to have the merchant's digital component selected. The probability may, in some examples, may be used to dynamically adjust the conversion values after the merchant's digital component is provided for output.

A feedback apparatus and method for dynamically controlling e-commerce platforms comprising a central engine is configured to connect with an e-commerce platform associated with a plurality of communication devices and a plurality of engagement engines and fulfillment engines that interact with the communication devices; the central engine is configured to dynamically add, remove or adjust the computational resources in the e-commerce platform in order to achieve a target efficiency for engagements with the communication devices and fulfillments from the communication devices. A graphical interface for controlling the feedback apparatus is also provided.

A system and method for multi-channel dynamic advertisement testing. The system comprises a multi-platform adaptive ad campaign manager, a dynamic advertisement engine, a campaign database, and an omnichannel text-based communicator. The system receives customer interactions with two advertisement test variants, establishes a real-time media stream between a customer device and a second user device, and monitors the media stream to collect data related to effectiveness of the advertisement variants. The system may analyze media stream data together with a plurality of other data types to statistically determine which of the two advertisement variants resulted in better performance based on a variety of advertisement metrics. The system may use the plurality of data and the statistical analysis to suggest an advertisement element to be altered in the next round of advertisement variant testing. This system can combine data collection and analytics for an ad campaign together into one system.

According to an embodiment, the present application provides a method for marketing campaigns utilizing a tokenization framework, which relies on smart contracts for its operations. This method starts by accepting a campaign budget, which is for initiating the setup of a smart contract associated with this budget. The smart contract is responsible for the conversion of this budget into tokens, which are earmarked for distribution to users who enroll in the campaign. Operations of the smart contract include the periodic identification and verification of user-generated content relevant to the campaign. This is achieved by examining specific reference tags linked to the content. The smart contract evaluates the effectiveness of the content by analyzing engagement metrics, such as the number of views, likes, and comments. Based on these metrics, the smart contract calculates the appropriate token rewards for each participant.

The present disclosure describes techniques of implementing a collaborative advertising mechanism. The techniques comprise receiving a plurality of pieces of content associated with a mission event from a first plurality of client computing devices associated with a first plurality of users, evaluating the plurality of pieces of content based on one or more predetermined rules of the mission event and information indicative of viewer reactions to the plurality of pieces of content, determining at least one subset of the plurality of pieces of content based on a plurality of evaluation results corresponding to the plurality of pieces of content and user input from a client computing device associated with one of a second plurality of users, and distributing the at least one subset of the plurality of pieces of content.

Systems and methods are provided for object discovery and object mapping through an application with a Graphical User Interface (GUI). Objects are discovered through an object recommendation channel and through user input of key words and/or key phrases. Each object includes an Intensity of Interest (IoI), which is a relative preference for an object reflected by relative size in the object map. Additionally, the object map provides a visual display which indicates relationships among objects.

A system comprises an interface for enabling a user having a web property to input advertisement information including a web property advertisement, criteria indicating when presentation of the advertisement is desired by the user, a bid for the presentation of the advertisement when the criteria are met, and subsequent modifications to the advertisement information; geographically distributed servers, a plurality of the geographically distributed servers for storing the advertisement information, at least one of the geographically distributed servers for receiving a request for advertisement content from an advertising channel, at least one of the geographically distributed servers for determining whether the request meets the criteria, and at least one of the geographically distributed servers for forwarding the advertisement to the advertising channel in response to the request when the criteria have been met; and a distribution component for propagating the advertisement information to the geographically distributed servers according to an arrangement.

A machine may be configured to facilitate following A/B tests generated by an A/B testing system. For example, the machine accesses a subscription record associated with an experiment identifier of an experiment in a database. The experiment may pertain to a service provided on a Social Networking Service (SNS). The subscription record identifies one or more users who requested to follow the experiment. The accessing is based on the experiment identifier. The machine identifies, based on the subscription record, a user identifier of a user of the one or more users who requested to follow the experiment. The machine generates a digital content item that references the experiment followed by the user based on a presentation template and data pertaining to the experiment followed by the user. The machine causes a presentation of the digital content item in a user interface of a client device associated with the user.

A sales promotion processing device according to an embodiment includes an input device, a display device, a communication interface, and a processor. The input device receives input of one or more criteria for a planned sales promotion. The processor generates a new sales promotion based on the received one or more criteria. The processor retrieves, from a sales promotion database that stores data related to past sales promotions via the communication interface, one or more past sales promotions having at least a predetermined number of criteria that are similar to the received one or more criteria. The processor predicts a result of the generated new sales promotion by evaluating the generated new sales promotion relative to feature data extracted from the criteria of the retrieved one or more past sales promotions by machine learning. The display device displays the predicted result.