24/7 Customer, Inc.

[24]7.ai, Inc.

7.ai, Inc.

Ravi Vijayaraghavan

Pallipuram V Kannan

R Mathangi Sri

Kranthi Mitra Adusumilli

Vaibhav Srivastava

Nitin Hardeniya

Subhash Ramchandra Kulkarni

Mathangi Sri Ramachandran

Suchana Seth

Mohit Jain

Sumit Kumar

Andrew Chang

Bhupinder Singh

Gangadharan Kumar

Robin Dautricourt

Dinesh Ajmera

Debashish Panda

Pankaj Ghanshani

Ravi Prakash

Prashant V Ullegaddi

Ravi Garikipati

Ponnusamy Sam Albert

Vijayalakshmi Amudhan

Nitin Kumar Hardeniya

Improvement of customer experiences during online commerce is accomplished by providing unique experiences to customers as a result of anticipating customer needs, simplifying customer engagement based on predicted customer intent, and updating system knowledge about customers with information gathered from new customer interactions. In this way, the customer experience is improved.

Method and apparatus for an intuitive customer experience

A mechanism for facilitating customer interactions within a customer service environment provides prompt and accurate answers to customer questions. A smart chat facility for use in a customer service environment to predict a customer problem examines a customer chat transcript to identify customer statements that set forth a customer issue and, responsive to this, can route the customer to an agent, an appropriate FAQ, or can implement a problem specific widget in the customer UI. Customer queries are matched with most correct responses and accumulated knowledge is used to predict a best response to future customer queries. The iterative system thus learns from each customer interaction and can adapt to customer responses over time to improve the accuracy of problem prediction.

Predictive customer service environment

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

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 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

In accordance with an example embodiment a computer-implemented method and an apparatus for predicting and tracking of mood changes in textual conversations are provided. The method includes determining, by a processor, one or more mood metrics in each of two or more chat stages of a real-time textual conversation between an agent and a customer. Changes in the one or more mood metrics across the two or more chat stages of the real-time textual conversation are tracked by the processor. Further, the method includes determining, by the processor, at least one action associated with the real-time textual conversation based on the changes in the one or more mood metrics.

Method and apparatus for improving goal-directed textual conversations between agents and customers

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

User interactions are categorized into predefined hierarchical categories by classifying user interactions, such as queries, during a user interaction session by labeling text data into predefined hierarchical categories, and building a scoring model. The scoring model is then executed on untagged user interaction data to classify the user interactions into either action-based or information-based interactions.

Categorization of user interactions into predefined hierarchical categories

With regard to searches and, more particularly, to searches performed on information repositories belonging to an enterprise, a centralized management system is used by the enterprise to manage the predictive search experience for users. A system offers a rich resolution experience to the end users based on user intent as determined from a variety of mechanisms, such as keywords, end user journey, clustered journey, etc. Also disclosed herein is a system that derives and offers various suggestions to end users to help them accomplish their objectives.

Method and apparatus for predictive enrichment of search in an enterprise

A method and apparatus for a computer-implemented technique for maximizing customer satisfaction and first call resolution, including converting telephone calls into online chats, while minimizing cost is provided. Techniques for incorporating analytics as applied to customer data into particular strategies for call deflection, targeting particular individuals to increase chat acceptance rate, and computing a customer's wait time are also provided.

Method and apparatus for optimizing customer service across multiple channels

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.

Growing community of inventors

Bengaluru, India

Ravi Vijayaraghavan