A system and methods for facilitating transactions relating to slots in a content promoter's performance. The system allows content promoters to publish available slots in their performances and receive submissions from artists requesting their content be presented in those slots. The system facilitates transactions between promoters and artists using an escrow and “proof of play” system. The escrow system charges an artist in advance for content submitted to be presented, but holds payment of those charges until after the promoter has presented the content and proof of play has been confirmed. Proof of play may be directly confirmed by the artist, may be confirmed by the system, or may be provided to the artist in an audiovisual recording made by the promoter. The escrow and proof of play system helps protect promoters and artists against potential default by either party to the transaction.

The disclosure provides systems and methods for analysis of financial data and customization of financial products. Time-dependent data is obtained for a relatively large set of users, and from the data are determined a relatively small set of trajectories that model user behaviour. New users are characterized according to the trajectories and financial products are automatically personalised to suit the new user.

Systems and methods are presented for managing the cadence (e.g., frequency or rate) that electronic promotion correspondence is sent to a consumer. A system may access a target cadence indicator associated with a consumer that indicates of a target rate for sending electronic promotion correspondence to the consumer. The system may also determine an actual cadence indicator for the consumer over a predetermined period of time and analyze a potential electronic promotion correspondence for sending to the consumer. The system determines whether to send the electronic correspondence to the consumer based on the target cadence indicator, the actual cadence indicator, and the analysis of the electronic promotion correspondence.

Aspects of the subject disclosure may include, for example, embodiments for detecting a navigation application operating on a first communication device, and identifying a location of the first communication device. Further embodiments can include obtaining a first advertisement and a second advertisement according to the location of the first communication device as well as determining a first priority associated with the first advertisement and a second priority associated with the second advertisement. Additional embodiments can include determining the first priority is higher than the second priority resulting in a determination to present the first advertisement prior to the second advertisement, and providing the first advertisement, the second advertisement, and the determination to the first communication device. The first communication device presents the first advertisement within navigation content utilizing the navigation application prior to presenting the second advertisement within the navigation content based on the determination. Other embodiments are disclosed.

Systems, methods, and computer-readable media for controlling the timing of customer interactions (e.g., targeted product or service offerings to customers) in order to determine optimally efficient usage of a seller's available inventory are disclosed. A customer selection problem (CSP) may be formulated and solved to implement a customer selection policy for controlling the timing with which customers from a customer pool will be targeted with offerings. The customer selection policy may include dynamic optimization to account for intertemporal effects of customer decisions.

The present disclosure provides a method, computer system and computer program product for determining interruption points based on emotion values in a content. According to the method, emotion values of one or more emotion catalogs at one or more time points of the content to be interrupted can be determined, and one or more interruption points can be determined based on the determined emotion values.

Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.

A location-based advertising system has a display device that is attached to a portion of an automobile. The display device renders a plurality of advertisements according to a dynamic advertisement schedule. Furthermore, the display device has a flexible display screen that adjusts to a shape of the portion of the automobile. The location-based advertising system also has a location-based sensor that is positioned at the automobile, and the location-based sensor determines a geographical position of the automobile along an advertising trip performed by the automobile. Also, the location-based advertising system has a controller that is positioned at the automobile such that the controller is in operable communication with the location-based sensor. The controller activates the rendering of each of the plurality of advertisements based upon a corresponding advertising location being reached by the automobile along the advertising trip.

Methods, systems, and computer programs are presented for the determination of optimal communication scheduling. Send Time Optimization (STO) uses machine learning (ML) to recommend a personalized send time based on a recipient's past engagement patterns. The purpose of the ML model is to learn patterns in the data automatically and use the patterns to make personalized predictions for each recipient. The send time recommended by the model is the time at which the model believes the recipient will be most likely to engage with the message, such as clicking or opening, and use of the send time mode is expected to increase engagement from recipients. Additional customizations include communication-frequency optimization, communication-channel selection, and engagement-scoring model.

A method, system, and computer program product enables automation and control of the sales, management, accounting, reporting, and traffic functions necessary to manage advertisement sales, content management, and administrative processes for media production environments, including, but not limited to, broadcast television, radio and webcasting stations, and newspapers. The advertisement management and automation system operates on a PC-based platform and is networked for communications with local area networks, wide area networks, and the Internet, including both wired and wireless appliances. Distribution and tracking is enabled for both local and national advertisement and content management, whereby data, advertisements, content, and reports are pushed and pulled from individual nodes (e.g., television stations) to a central network hub or point that feeds and/or gathers data and communicates with the individual nodes. Traditional broadcast methods for over-the-air, DBS, terrestrial cable, wireless, and live Internet webcasting are combined with on-demand Internet and wireless appliances for monetizing and streamlining the sales, management, accounting, reporting, and traffic processes for single and multi-distribution content production environments.