This disclosure describes systems, methods, and devices related to network resource management and content delivery. A method may include a first device determining a first bandwidth demand associated with a second device sending a first addressable advertisement using a data over cable service interface specification (DOCSIS) bandwidth using a first advertisement spot for a first linear segment addressability (LSA) television channel, and determining a second bandwidth demand associated with the second device sending a second addressable advertisement using the DOCSIS bandwidth for presentation using a second advertisement spot. The method may include determining that the first bandwidth demand is below a threshold bandwidth, and determining that the second bandwidth demand exceeds the threshold bandwidth. The method may include sending a reference to the first addressable advertisement over the DOCSIS bandwidth for presentation using the first advertisement spot and, and determining a recommendation associated with presentation of the second addressable advertisement.

Methods, systems, and computer programs are presented for the determination of optimal communication scheduling. One method includes an operation for training a machine-learning program to generate a frequency model that determines a frequency for sending communications to users. The training utilizes training data defined by features related to user information and responses of users to previous communications to the users. The method further includes determining, by the frequency model and based on information about a first user, a first frequency for the first user. The first frequency identifies the number of communications to transmit to the first user per period of time. Further, the method includes operations for receiving a communication request to send one or more communications to the first user and determining send times for the one or more communications to the first user based on the first frequency. The communications are sent at the determined send times.

Methods and systems for determining reach information. Watching habits of viewers in a designated market can be analyzed using machine learning algorithms to obtain campaign spot plans. The campaign spot plans can be applied to single viewer data to calculate campaign spot plan Television Average Ratings Points (TARP) pattern information. The TARP pattern information can be translated into reach information determining how many people were uniquely exposed to each campaign spot.

A method including obtaining historical revenue per click (RPC) data. The method also can include generating hourly RPC prediction data for a predetermined time period based on the historical RPC data. The method additionally can include determining (i) time intervals from within the predetermined time period and (ii) a respective modifier for each of the time intervals, based on the hourly RPC prediction data. The acts method can include uploading the time intervals and the respective modifiers for the time intervals to a dayparting system of a search engine. Other embodiments are described.

A method and digital signage player for managing display of a distributed digital signage content. The digital signage player stores the distributed digital signage content, and a local placement target for the distributed digital signage content. The digital signage player displays the distributed digital signage content in accordance with the local placement target. The digital signage player exchanges messages with a neighbor digital signage player, for increasing the local placement target of the distributed digital signage content at the request of the neighbor digital signage player. The neighbor digital signage player also displays the distributed digital signage content in accordance with its own placement target, which needs to be decreased. The digital signage player ultimately increases the local placement target of the distributed digital signage content based on the messages exchanged with the neighbor digital signage player.

Offers and optimization have co-existed since long and industry has several solutions to address this need. However, identifying the right customer to target offers, assigning the right offer only when customer requires it and presenting offers even on long tail product is crucial to a successful offer assignment. Present application provides systems and methods that creating potential segments where in each user fits into any of the segments based on purchase history, navigation/behavior through e-commerce portal, demographics, and look-ahead scores. When a trigger is received in real-time, the system generates optimal real-time product recommendations for the users by eliminating popularity bias, based on his/her most recent product clicks in the e-commerce portal using a recommender system. Further, offers are mapped to the optimal real-time product recommendations using a scoring mechanism to generate and provide next optimal offers wherein the next optimal offers are generated based on configurable constraint(s).

Systems and methods are disclosed for associating a plurality of Internet-enabled devices with a common user profile for targeting Internet content or advertising. One method includes: receiving, from a plurality of Internet-enabled devices, a plurality of requests for electronic content or advertising; extracting, from each of the plurality of requests, a source IP address and a unique identifier associated with the respective Internet-enabled device; identifying each possible pair of devices from which requests were received; calculating for each possible pair of devices a probability that the pair of devices are owned or operated by a common user; and prompting a user to either confirm a characteristic of a prior browsing session or to log-in to an account associated with the common user based on a comparison of the calculated probability to one or more thresholds.

A prediction data input unit 91 inputs prediction data that is one or more explanatory variables that are information likely to affect future sales. An exposure pattern generation unit 92 generates an exposure pattern which is an explanatory variable indicating the content of a commercial message scheduled to be performed during a period from predicted time to future prediction target time. A component determination unit 93 determines the component used for predicting the sales, on the basis of a hierarchical latent structure that is a structure in which latent variables are represented by a tree structure and components representing probability models are located at nodes of a lowest level of the tree structure, gating functions for determining a branch direction in the nodes of the hierarchical latent structure, and the prediction data and the exposure pattern. A sales prediction unit 94 predicts the sales on the basis of the component determined by the component determination unit 93 and of the prediction data and the exposure pattern.

Systems and methods for the optimized allocation of content on a content distribution system according to a content distribution plan are described. In some embodiments, a management system may be configured to generate an optimized schedule for content distribution, such as an advertising campaign. For a television distribution schedule, an impressions forecast may be generated based on the available inventory and historical audience measurement information, such as television ratings for a television advertising campaign. An optimized schedule may be generated based on the impressions forecast and content may be distributed according to the optimized schedule. Measurement information relating to the distribution according to the optimized schedule may be analyzed and used to generate a re-optimized schedule, for example, that may be used to schedule the advertising campaign on a subsequent day.

Aspects of the subject disclosure may include, for example, transmitting a signal during a media presentation, wherein the signal is transmitted prior to an advertising timeslot during the media presentation, receiving a first indication that the signal is detected by a user device, and receiving a second indication of a status of the user device, wherein the status coincides with when the user device detects the signal. Other embodiments are disclosed.