Text analysis includes determining one or more global analysis parameters based on backtranslation of a first corpus between supported languages. A new text analysis model is determined for an unsupported language based on the one or more global analysis parameters and a text analysis model for a first supported language. An input text is analyzed in the unsupported language with the new text analysis model.

Systems and methods described herein can automatically route an inbound call from an identified customer to one of a plurality of agents, the agent being selected on the basis of likelihood of a favorable outcome. The method determines a predictive model appropriate for the identified customer, with model variables including call center data, and targeted marketing data based upon risk data for the customer. An analytical engine calculates outcome predictions by applying the predictive model to values of model variables over a recent time interval. In a time-series analysis, this calculation is repeated while dynamically adjusting the recent time interval, until identifying a call routing option that satisfies a favorable outcome criterion. This method may be used to select the agent to handle the incoming call, and optionally to select a product for that agent to discuss with the identified customer.

Electronic systems for promotional offers are disclosed. An illustrative electronic system may include a computing device and a storage medium. The storage medium may contain one or more programming instructions that, when executed, cause the computing device to generate scores for customers from a customer database for distribution of limited quantities of promotional offers. Each score may be associated with one customer and one promotional offer, and each score may measure a probability that the associated customer will make a purchase in accordance with the associated offer. The programming instructions may further cause the computing device to identify a highest score, determine a customer associated with the highest score, determine a promotional offer associated with the highest score, assign the promotional offer to a personalized offer list for the customer if the promotional offer satisfies one or more constraints.

A system that analyzes a user's communications to select a promotion that is presented to the user. The analysis may occur in two stages: a first stage analyzes a single communication from a user to determine whether the user is a potential target for a promotion; for potential targets, a second stage analyzes a history of communications from the user to generate a user profile. The system may then select a promotion based on the profile. The profile may include a set of profile tags that are considerably more detailed and granular than traditional demographic data; tags may for example indicate user affiliations with groups or ideas (such as religions or political parties), or user life cycle stages. Using these rich, detailed user profile tags, the system may achieve promotion response rates far above those from traditional advertising, which relies on cookies or simple demographic categories.

Embodiments disclosed herein provide systems and methods for the filtering, selection and presentation of vendors accounting for both user characteristics and vendor characteristics, such that the systems and methods may be used by both customer and vendor alike to better match customer needs with the resource-constrained vendors with whom a successful sale has a higher probability of occurring. Embodiments may include filtering, selecting and/or presenting vendors to a user sorted by the probability that the particular vendor will possess the characteristics that appeal to a particular customer and therefore result in a large probability of sale and suppress presentation of those vendors that are unlikely to be selected by the customer since their characteristics are less consistent with those needed by the customer and, therefore, are unlikely to result in a sale.

The present invention is directed to machine-learning based methods and systems related to dynamically inserting items multimedia content into media broadcasts. By using machine-learning based models, the performance of different items of multimedia content with different audiences can be automatically simulated, resulting in recommendations for where, when and how to optimally distribute those items of multimedia content. The multimedia content can be distributed by dynamically integrating that multimedia content into a streaming video feed. The reaction of an audience to the multimedia content is then automatically monitored, collected, and analyzed using machine-learning techniques, allowing the reaction of the audience to the multimedia content to be automatically determined. This reaction can then be input back into the machine-learning based simulator, further refining future predictions for the performance of items of multimedia content with audiences.

A system includes a memory comprising a first preference profile and a second preference profile, a correlation module configured to determine a correlation value between the first preference profile and the second preference profile, and a module configured to take an action as a function of the correlation value. The action is changing a physical configuration of signage from a first physical configuration to a second physical configuration.

There is disclosed a method and system for selecting one or more targeted messages to be included within a web resource. The method comprises: receiving a first request for a targeted message, the request including a target context parameter and a target floor price; executing an MLA configured to generate a first confidence parameter vis-a-vis a first server and a second confidence parameter vis-a-vis a second server; transmitting a second request to a selected one of the first server and the second server based on the first confidence parameter and the second confidence parameter; receiving the targeted message from the selected on of the first server and the second server; transmitting the targeted message to the a web server for inclusion within the web resource.

Systems and methods of dynamically varying the intensity of providing content items in a remarketing campaign based on tracking client device interactions are provided. The system can assign an account identifier to a first segment for a pre-conversion model, responsive to receiving a first interaction associated with a content provider from a client device. The system can assign the account identifier to a second segment for the pre-conversion model, responsive to receiving a second interaction. The system can assign the account identifier to a third segment, responsive to receiving a third interaction. The third interaction can include a conversion event. The system can generate a post-conversion model based on the third segment and the pre-conversion model. The system can determine an intent index for the account identifier based on the post-conversion model. The system can store the account identifier into an interest cluster based on the intent index.

A telecommunication system implements a method for providing a targeted on-line advertisement to a user accessing a content provider node of the system. An ad is requested from a user node. A content provider is identified at a right of first refusal ad service based on the ad request. At least one demographic corresponding to the user node is determined. Whether an ad corresponds to the determined demographic is determined. A default ad service is determined based on the content provider. The ad request is passed to a default ad service. Retrieved content is processed.