A media management system that handles a plurality of agreement information and a plurality of promotional campaigns for a specified upcoming time-frame, receives input parameters for each of the plurality of agreement information that corresponds to an upfront inventory utilization type and commercial operator break (COB) inventory utilization type of a plurality of inventory utilization types. Reserve inventory units for each of the plurality of promotional campaigns are determined based on historical data of an amount of inventory units utilized previously under the promotion inventory utilization type for the specified upcoming time-frame. An initial allocation of inventory units for the promotion inventory utilization type is done based on the determined reserve inventory units. Inventory units from a defined amount of inventory units are dynamically allocated among each inventory utilization types of the plurality of inventory utilization types.

A system, computer program product, and method for activation-based marketing are presented. In one embodiment, the system includes one or more data storage devices configured to store demographic data, healthcare utilization data, and response data associated with a target individual. The system may include a server coupled to the one or more data storage devices. The server may be suitably programmed to determine a life stage associated with a target individual, determine an attitudinal segment associated with the target individual, and determine a response model associated with the target individual. The server may assign the target individual to at least one of a predetermined set of segmentation groups in response to the life stage, the attitudinal segment, and the response model associated with the individual. The system may generate a personalized communication modality tailored to the target individual in response to the segmentation group assigned to the target individual.

Methods and systems for determining an individual gaze value are disclosed herein. An exemplary method involves: (a) receiving gaze data for a first wearable computing device, wherein the gaze data is indicative of a wearer-view associated with the first wearable computing device, and wherein the first wearable computing device is associated with a first user-account; (b) analyzing the gaze data from the first wearable computing device to detect one or more occurrences of one or more advertisement spaces in the gaze data; (c) based at least in part on the one or more detected advertisement-space occurrences, determining an individual gaze value for the first user-account; and (d) sending a gaze-value indication, wherein the gaze-value indication indicates the individual gaze value for the first user-account.

Aspects of the subject disclosure may include, for example, providing to a user device a video content item including at least one scene which includes a plurality of advertisement placement opportunities and determining a preference profile for an individual associated with the user device. Aspects further include selecting a group of matching advertisements having advertisement profiles that match the preference profile for the individual and determining a relative prominence score for each advertisement placement opportunity. Aspects further include ordering the matching advertisements according to prominence information specified for each matching advertisement, wherein the prominence information corresponds to a relative desired prominence specified by an advertiser associated with the matching advertisement. Aspects further include providing the ordered matching advertisements to the user device according to the respective prominence information so that a matching advertisement having a greatest desired prominence is displayed in the video content item at an advertisement placement opportunity having a highest relative prominence score. Other embodiments are disclosed.

Social media networking applications, web sites, and services creates implicit relationships between users based on their interest or participation in real-world and optionally virtual or online activities in addition to explicitly defined peer relationships. User profiles, activity entities, and expressions may be associated with metadata to assist in searching and navigation. Metadata is implicitly associated with user profiles, activity entities, expressions, or other data entities based on user behavior using metadata collector. A metadata collector is a poll, survey, list, questionnaire, census, test, game, or other type of presentation adapted to solicit user interaction. A metadata collector is associated with metadata elements. When users interact with a metadata collector, their user profiles and the data entities included in their interactions become associated with the metadata elements of the metadata collector. These metadata element associations may then be used for any purpose.

Systems and methods are presented for carrying out a task to an expanded group of users on behalf of a third party. In operation, an online service receives task information corresponding to a task from a third party. Additionally, the online service receives a seed group of users from the third party. The online service determines an intersection between the seed group of users and the online service's corpus of users. This intersection of users is then used to identify an expanded set of users of the online service's corpus of users. The online service then carries out the task on behalf of the third party, targeting the expanded set of users.

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.

Promotional materials are presented to a user according to user-experience as defined by certain parameter values detected during operation of a computing device. Operation of the computing device may include playing a video game. The operation of the computing device is monitored for non-conforming parameter values with respect to ideal parameter values. The promotional items disclosed in the promotional materials are identified as a way to improve the current user experience if added to the current computing system.

The present invention provides methods and systems for facilitating promotion of a content based on playing an audio. The present invention includes a process of receiving a promotional content from a user device; transmitting the promotional content to a second user device; selecting a promotional content based on the at least one indication; transmitting a promotional content to a promoter device; generating a promotional content notification, wherein the promotional content notification includes statistical representations of the audience data; and transmitting the promotional content notification to the user device, the promoter device and the second user device.

A system is provided that determines reserve inventory units required for each promotional campaign. Based on one of input parameters to meet defined parameters for defined amount of inventory units for one or more specified durations until end of a specified upcoming time-frame, inventory units are allocated from defined amount of inventory units among each inventory utilization type. Incremental value of revenue from each inventory utilization type is optimized and ratings for previously allocated inventory units assigned to a promotion inventory utilization type is increased. Previously allocated inventory units are periodically adjusted and re-distributed among each inventory utilization type based on difference in demand value of an estimated inventory units forecasted for upcoming specified duration and actual value of the inventory units for current duration. Based on remaining inventory units and each inventory utilization type, schedule of a channel is communicated to a user device, via a network.