First and second cookies are obtained from first and second devices, respectively. The first cookie includes a first ID corresponding to a registered user ID, which is in turn associated with first information indicating activities related to interactions of a registered user with a first website. The second cookie includes a second ID corresponding to an anonymous user ID, which is in turn associated with second information identifying activities related to interactions of an anonymous user with a second website. The first and second user IDS, and the first and second information, are stored in a database, and correlated to determine commonalities. In response to identifying at least one commonality a determination is made, based on the at least one commonality, that the registered user and the anonymous user are a single user. The database is updated to include a unified listener identifier (ULID) that links the registered user ID with the anonymous user ID.

A method for managing advertisement that is to be performed by a server includes a step of receiving a piece of advertisement type information from an enterprise device, a step of selecting pieces of candidate user information from among pieces of user information stored in the server based on the piece of advertisement type information, a step of receiving an advertisement material from the enterprise device, a step of selecting pieces of target user information that corresponds respectively to target users from among the pieces of candidate user information, and a step of performing an advertisement-pushing procedure with respect to the target users.

A user of a mobile device can specify particular news, weather, traffic, or other perishable content received on a particular Internet radio station or other media channel received by the mobile device. The user can customize the station so that wherever the user is currently located, they can receive on their mobile device perishable content related to a preferred geographic location or other category. In some embodiments, a user can specify that a customized station plays local news from one city at the top of the hour, traffic from another city at 10 minutes past the hour, and music or other content at other times. The user can also customize original content from the personal libraries of specified users. Thus, a user can customize not only the genre of content or select a particular local station, but can also modify the perishable content provided by the customized station.

Systems and methods for data communication using audio patterns are described. The methods include encoding a message into an audio pattern, mixing the audio pattern with an audio content to obtain a mixed audio content, wherein the audio pattern is inaudible in the mixed audio content, and transmitting the mixed audio content.

A method is disclosed for enabling a network location to provide an ordering process for data relevant to connected network devices' activities. The method includes assembling the data, utilizing the activity data, and associating the data, such that information is derived to enable a desired expansion of at least one designated activity. Another method is disclosed for managing an object assignment broadcast operations for a network location based on a network device's previous activities. This second method includes tracing a network device's conduct to determine that a network device prefers a particular class of content. The method also includes tagging a network device's profile with the respective observation and deciding by a network location as to the classification factor for a network device to be targeted for an object assignment broadcast.

A system and method for performing a purchase transaction using rewards points by receiving a purchase request using a financial account with associated account information, determining based on the account information that the financial account is associated with a rewards program with rewards points, and providing the option to pay for the purchase using the financial account or at least a portion of the rewards points. The purchase may be paid for using rewards points from multiple rewards programs and/or multiple financial accounts.

In general, embodiments of the present invention provide systems, methods and computer readable media for a predictive recommendation system based on an analysis of previous consumer behavior. One aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving data representing a user, the data including user identification and historical data; receiving a set of promotions recommended for the user; assigning the user to a consumer lifecycle model state based in part on the historical data and the user identification; selecting a ranking algorithm associated with the consumer lifecycle model state; and ranking the received set of promotions based on a predicted promotion relevance value associated with each promotion, the predicted promotion value being calculated using the ranking algorithm.

Certain aspects of the present disclosure provide techniques for training predictive risk models based on user transaction history. An example method generally includes extracting, from a transaction history data set for a plurality of users of a software application, a plurality of features for each user of the plurality of users having records in the transaction history data set. A training data set is generated based on the extracted plurality of features for each user of the plurality of users. A plurality of predictive risk models is trained to generate a risk propensity score indicating a likelihood that a specified event will occur based on the training data set. Generally, monotonicity of one or more constraints is implemented in the model.

A method is provided, comprising: identifying a plurality of communications sessions and obtaining a respective transcript of each of the plurality of communications sessions, each of the plurality of communications sessions involving a respective one of a plurality of customers; generating a plurality of label sets, each of the label sets corresponding to a different one of the plurality of customers; generating a plurality of customer signatures, each of the plurality of customer signatures being generated based on a different one of the plurality of label sets; classifying, with a first neural network, each of the plurality of customer signatures into one of a “re-engage” category and a “do-not-re-engage” category; and returning a customer re-engagement list that identifies respective ones of the plurality of customers who are associated with customer signatures that are classified in the “re-engage” category.

Methods, computer-readable media, systems, and/or apparatuses are provided for providing offer and insight generation functions. User input requesting an offer or insight may be received and an image of a photographic identification of a user may be requested. The image of the photographic identification may be captured and stored. A self-captured image of the user may be captured (e.g., via an image capture device of the computing device) and compared to an image of a user from the photographic identification. Responsive to determining that the images match, displaying an instruction to capture a vehicle identification number. The vehicle identification number may be captured. Data, including location data, may be extracted and an archive including the extracted data may be generated and the data may be transmitted to an entity computing system for processing. The entity computing system may evaluate the data and generate one or more insights and/or outputs.