24/7 Customer, Inc.

Microsoft Technology Licensing, LLC

[24]7.ai, Inc.

Prashant V Ullegaddi

Vaibhav Srivastava

R Mathangi Sri

Ravi Vijayaraghavan

David S M Freeman

The stages of an interaction between a potential customer (the user) and a sales representative (the agent) during a sales interaction are identified to understand the interaction factors that drive sales and, by doing so, to serve the customer better and thus increase sales. Initially, a user makes contact with an agent via a communications network. During the interaction, a dropping point is reached, i.e. the point in the interaction at which either the user or the agent ends the interaction. The dropping point and other interaction factors is analyzed. Based upon such analysis, various recommendations are made to the agents to improve the user's sales experience.

Stage-wise analysis of text-based interactions

Techniques for restricting access to certain members of an online service are provided. Member accounts include one or more images. Attributes of member accounts that share the same image are analyzed to determine whether to label the image 'bad.' In one technique, a model is created and trained to learn the attributes or features (and their corresponding weights) that are associated with 'bad' user accounts that share the same image. As a result, member accounts that are associated with a &#x201c;bad&#x201d; image may be restricted in one or more ways.

Identifying illegitimate accounts based on images

A computing method and system is disclosed for analyzing interactions between a user and a customer support agent. Typical interactions include inquiries about a product or service, and a service call. When the user purchases a good or service, or successfully completes a service call, the customer converts, e.g. the sales pitch or service solution was successful. If the customer does not convert, then the interaction between user and agent is analyzed to determine why the user did not convert and whether the user should be categorized for potential retargeting.

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Bengaluru, India

Prashant V Ullegaddi