[24]7.ai, Inc.

24/7 Customer, Inc.

Bhupinder Singh

Pallipuram V Kannan

Ravi Vijayaraghavan

R Mathangi Sri

Suchana Seth

Mathangi Sri Ramachandran

The disclosure is related to mining of text to derive information from the text that is useful for a variety of purposes. The text mining process can be implemented in a service oriented industry such as a call center, where a customer and an agent engage in a dialog, e.g., to discuss product/service related issues. The messages in dialogues between the customers and the agents are tagged with features that describe an aspect of the conversation. The text mining process can mine various dialogues and identify a set of features and messages based on prediction algorithms. The identified set of features and messages can be used to infer an intent of a particular customer for contacting the agent, and to generate a recommendation based on the determined intent.

Systems and methods for facilitating dialogue mining

A computer-implemented method and an apparatus facilitate user engagement on enterprise interaction channels. Information related to a current journey of a user on one or more enterprise interaction channels is received. The user is categorized as one of a hot lead, a warm lead, and a non-hot lead based, at least in part, based on the received information related to the current journey of the user. If the user is categorized as the non-hot lead, a user interface (UI) displayed to the user is modified. The UI is modified to facilitate user engagement for converting the user from a non-purchasing entity to a purchasing entity.

Method and apparatus for engaging users on enterprise interaction channels

A computer-implemented method and an apparatus for personalizing customer interaction experiences receives an input corresponding to at least one of a business objective and a customer interaction channel. A customer classification framework is selected based on the input. The customer classification framework is associated with a plurality of persona types, where each persona type is associated with a set of behavioral traits. A persona type for a customer is predicted from among the plurality of persona types during an interaction on the customer interaction channel. A propensity of the customer to perform at least one action is predicted based on the persona type. A provisioning of personalized interaction experience to the customer is facilitated based on the predicted propensity of the customer to perform the at least one action.

Method for personalizing customer interaction experiences by routing to customer interaction channels

A computer-implemented method and an apparatus for modeling customer interaction experiences receives interaction data corresponding to one or more interactions between a customer and a customer support representative. At least one language associated with the interaction data is detected. Textual content in a plurality of languages is generated corresponding to the interaction data based at least in part on translating the interaction data using two or more languages different than the at least one language. At least one emotion score is determined for text corresponding to each language from among the plurality of languages. An aggregate emotion score is determined using the at least one emotion score for the text corresponding to the each language. An interaction experience of the customer is modeled based at least in part on the aggregate emotion score.

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Bengaluru, India

Bhupinder Singh