24/7 Customer, Inc.

[24]7.ai, Inc.

Andrew Chang

Pallipuram V Kannan

Ravi Vijayaraghavan

Vaibhav Srivastava

R Mathangi Sri

Michael Bodell

Patrick Nguyen

Matthew Oshry

Quang Nguyen

A Kranthi Mitra

Dan Reed

Ravi Garikipati

Vedavyas Kadudas

Matt Oshry

Tajinder Singh

James Ming Li

John Philip Bandhauer

Customer journey prediction and resolution is accomplished via a predictive model in which each user is mapped onto all available user journey information corresponding to a specific business. The predictive model is analyzed to understand the characteristics, preferences, and lowest effort resolution for the user related to the services that are subscribed to by the user. The predictive model is analyzed to predict the service or collection of services for each user. Embodiments interact with, provide and receive information from, and react to and/or deliver action to the customer across channels and across services. All customer and system behavior, data, and action is tracked and coordinated and leveraged for continuous feedback and performance improvement.

Customer journey prediction and resolution

Embodiments of the invention relate to chat and, more particularly, to determining an that is to be action taken based on the type of chat session. The resolution of the chat is categorized to decide the necessary steps taken and also to monitor the agent's performance. A chat filter extracts relevant portions of a chat session. The relevant factors are taken into consideration and scored based on the feature vectors. A model is built and the type of resolution is determined. An analysis of the chat session is then performed taking into consideration several factors.

Segregation of chat sessions based on user query

Unique customer identification and behavior is linked between either concurrent or sequential channels of engagement. Unique identifiers are created, captured, and/or passed between these multiple contact channels, e.g. Web, mobile, IVR, phone, automotive, television, to identify and tag the customer and their context, e.g. history, pass behavior, steps progressed, obstacles and/or issues encountered, etc., uniquely.

Multi-channel customer identification

Embodiments of the invention provide techniques that quantize community interactions with social media to understand and influence consumer experiences. Embodiments include a customer predictive experience platform. The platform can include an outcome engine configured for information mining and applying rules and analytics to the information, and an ops module configured for providing agent performance management, average handling time analytics, workflow management, and voice of the customer facilities. The platform can also include a chat module, a social media dialog engine, and a solution client configured for effecting predictive self-service, active auto sentiment management and rapid response to counteract negative sentiment, a customer experience ticker, a pre- and post-launch pulse, enhanced brand ambassadors, integration into corporate messaging and marketing, a social media dashboard, and a live portal configured for social media engagement and feedback.

Method and apparatus for analyzing and applying data related to customer interactions with social media

Embodiments of the invention provide techniques that quantize community interactions with social media to understand and influence consumer experiences.

A customer support system diverts a customer to an integrated support service to serve the customer better in situations where the use of a single mode of interaction is insufficient. Embodiments of the invention find use where an email or SMS is sent to a customer's smart devices with a link to visual content which helps customer better understand the information.

Method and apparatus for diverting callers to Web sessions

Growing community of inventors

Palo Alto, CA, United States of America

Andrew Chang