[24]7.ai, Inc.

24/7 Customer, Inc.

Kranthi Mitra Adusumilli

Ravi Vijayaraghavan

Subhash Ramchandra Kulkarni

Pallipuram V Kannan

Ravi Prakash

Darshan Hegde

A computer-implemented method and a system facilitate social recognition of agents. A first user interface (UI) is presented to a customer on a device in proximity to the customer subsequent to a completion of an interaction of the customer with an agent. The first UI comprises one or more survey questions related to a performance of the agent. A determination of whether the performance of the agent satisfies a predetermined condition is performed based on an input received from the customer in response to the one or more survey questions. A second UI is presented to the customer to request the customer to provide an endorsement for the agent if the performance of the agent satisfies the predetermined condition. A posting of the endorsement on one or more social media profiles of the agent is effected upon receiving the endorsement for the agent from the customer.

Method and apparatus for building a user profile, for personalization using interaction data, and for generating, identifying, and capturing user data across interactions using unique user identification

A context-aware computing system for delivering surveys to a customer. The choice of which survey to send to a customer may be tailored based on a click path (route), customer history, and customer interests. A customer browsing a Web page initiates the survey decision process. A control module selects a survey to send to a customer based on the criteria above and customer intent. Customer responses are then harvested from the Web-based survey.

Proactive surveys based on customer information

User intent is identified while the user browses online and recommendations are provided to the user. The recommendations are based on the identified intent, interests, and preferences of the user who is performing the searches. The determination of user intent and interests is based on a statistical model derived from data compiled from the user and a plurality of other users. Other resources may also be determined to be relevant, for example, because of past interactions of the user, memberships of the user in ecommerce websites, the user's interests and preferences are similar to those of other users, and so on. The result of the user search is a ranked set of recommendations that is provided to the user.

Intent prediction based recommendation system using data combined from multiple channels

Online browsing behavior is used to predict the intent of online users dynamically. The category of online user is predicted and the prediction can be used to provide assistance to the user, if required. Such prediction is based on a combination of a Na&#xef;ve's Bayes classifier and a Markov model.

Dynamic prediction of online shopper's intent using a combination of prediction models

A method and apparatus enables identification of customer characteristics and behavior, and predicts the customer's intent. Such prediction can be used to adopt various business strategies to increase the chances of conversion of customer interaction to a sale, and thereby can increase revenue, and/or enhance the customer's experience.

Method and apparatus for intent modeling and prediction

A computer-implemented method and an apparatus for facilitating staffing of resources receives customer data corresponding to a plurality of customers of an enterprise. At least one intention is predicted for each customer to configure a plurality of intentions. An expected volume of interactions is estimated for at least one time period based on the plurality of intentions. Each interaction in the expected volume of interactions is associated with interaction attributes. Resource data corresponding to a plurality of resources of the enterprise is received. Each resource is associated with a plurality of resource attributes. At least one resource is mapped to each interaction based on a match between resource attributes associated with the at least one resource and the interaction attributes associated with the each interaction. A staffing of the plurality of resources is facilitated based on the mapping of the at least one resource to the each interaction.

Method and apparatus for facilitating staffing of resources

A user profile is creates, and personalization is provided, by compiling interaction data. The interaction data is compiled to generate a value index or score from a user model. Parameterized data is used to build tools which help decide an engagement strategy and modes of engagement with a user. Several facets relating to the user, such as user behavior, user interests, products bought, intent, chat language, and so on, are compiled to create a user profile based personalization technique. In another embodiment, a unique ID is provided that can be mapped across multiple channels for use by the user to contact various organizations across multiple channels, and thus upgrade the user's experience.

Embodiments of the invention relate to managing user interactions and, more particularly, to performing analysis on data generated by user interactions. Embodiments of the invention use text mining to extract personal information of users from user interactions automatically. A topic model is used to reduce the number of dimensions required to represent the text, yet all the information of interest is highly pronounced. This enables a lower dimensional representation of the data leading to significantly faster computations.

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Hyderabad Telangana, India

Kranthi Mitra Adusumilli