An apparatus for providing customized advertisements includes a database that stores a plurality of electronic advertisements, receives biometric information of a client from at least one biometric device of the client, and receives receptivity information of the client responding to the plurality of electronic advertisements, as well as a processor that accesses the database, and maps the biometric information and the receptivity information and analyzes the mapped information to generate customized marketing data. The processor also calculates a receptivity probability for each of the plurality of electronic advertisements based on the customized marketing data by using current biometric state of the client, selects an electronic advertisement from the plurality of electronic advertisements based on the calculated receptivity probabilities, and outputs to the client the selected electronic advertisement.

Systems and methods for user engagement are provided. The methods may include retrieving user information of a user from a local registry on a user computing device; delivering a notification of personalized content to the user using an interface of the user computing device, wherein the notification is generated based at least on the user information retrieved from the local registry; and delivering the personalized content to the user based on an interaction of the user with the notification. The systems may include a user computing device including a user interface configured to interact with a user, a storage device configured to store thereon a local registry, and a user engagement software module configured to retrieve user information of a from the local registry; and a content server configured to store a personalized content.

Machine learning techniques to optimize user interface template selection are provided. In one technique, a first set of feature values pertaining to a first entity is identified. Multiple sets of feature values are also identified, each set of feature values pertaining to a different user interface (UI) template for rendering content items on a computer screen. For each set of feature values of the multiple sets, the set of feature values and the first set of feature values are inserted into a machine-learned model to generate a score, which is added to a set of scores, which set of scores is initially empty. Based on the set of scores, a particular UI template is selected for a content item. The content item is transmitted over a computer network to be presented on a screen of a computing device of the first entity according to the particular UI template.

A method of providing a content and advertisement customized to a passenger boarding a vehicle includes: receiving passenger information about a passenger from a user terminal of a passenger in a vehicle by a server; determining a passenger's preference based on the passenger information, and selecting a plurality of pieces of content information and a plurality of pieces of advertisement information according to the determined passenger's preference by the server; calculating an estimated travel time for the vehicle to arrive at a destination based on the passenger information by the server; creating a playlist by selecting the content information and the advertisement information such that the total sum of the replay time is at least equal to the estimated travel time among a plurality of pieces of selected content information and a plurality of pieces of selected advertisement information; and transmitting the created playlist to the passenger terminal.

Methods and apparatuses are described for automated optimization and personalization of customer-specific communication channels using feature classification. A server captures historical interaction data comprising a channel type, a user identifier, an interaction date, and a user response value. The server generates a channel feature vector for each combination of channel type, user identifier, and interaction date. The server identifies features from the channel feature vectors for each different channel type and aggregates the features into a common feature vector. The server executes a trained classification model on the common feature vectors to select user identifiers for each different channel type that have an engagement probability value at or above a corresponding threshold. The server determines, for each different channel type, a distance value between the engagement probability value and the corresponding threshold and communicates with a remote computing device via a channel that is associated with an optimal distance value.

Methods and apparatus for delivering content to an audience member via one or more mediums based on an audience member profile are disclosed. Profile data for audience members may be initially collected from an offline source, such as a registration or subscription database. The profile data may be stored in a dedicated database. The initial profile data may be supplemented periodically with data reflecting online activity by the audience member. The combined offline and online profile data may be used to group the audience members into segments. Audience member segments may be used to identify audience members who are targeted to receive like content. An audience member's inclusion in a segment may be indicated by storing a segment-targeting cookie on the audience member computer. Content may be delivered to the audience member based on identification of the segment in the segment-targeting cookie.

In a method for delivering targeted television advertisements based on online behavior, IP addresses indicating online access devices and IP addresses indicating television set-top boxes are electronically associated for a multitude of users. Using user profile information derived from online activity from one of the online access IP addresses, a television advertisement is selected, such as by using behavioral targeting or demographic information, and automatically directed to the set-top box indicated by the set-top IP address associated with that online access IP address. Preferably neither the user profile information nor the electronic association of online access and set-top box IP addresses includes personally identifiable information.

A first server controls insertion of media content into a media stream, and transmits the media stream from the first server to a media player. The first server also transmits, to a second server, information linking a particular consumer to both a third-party service and to the media player. Control of inserting media content into the media stream is passed from the first server to the second server. The second server receives consumer-specific content associated with the particular consumer from the third-party service, and maps the consumer-specific content to the media player based, at least in part, on the information linking the particular consumer to both the third-party service and to the media player. The second server then inserts the consumer-specific content into the media stream during a time period the second server is in control of inserting media content into the media stream.

A smart rollout recommendation system uses a modernization score that indicates a likelihood of accepting a change in an existing product to identify at least one tenant of a plurality of tenants eligible to receive the change and the timing of the rollout. The modernization score is generated using a set of attributes extracted from tenant profiles and a machine learning model.

Aspects of the subject disclosure may include, for example, a method that include combining an offer with media content to generate a notice within a portion of the media content, providing the notice in the designated portions of the media content with the notice during a media content presentation at equipment of a user, receiving an indication of interest in the portion, retrieving marketing information for the portion of the media content based on the user profile including location based information, time based information, and activity information, monitoring an activity of the mobile communication device to determine when the mobile communication device information satisfies a location, time, and activity information that matches the marketing information and determining a communication for the user based on the marketing information and the monitoring of the activity of the mobile communication device. Other embodiments are disclosed.