Dell Products Inc.

Tejas Naren Tennur Narayanan

Sathish Kumar Bikumala

Karthik Ranganathan

Gautam Kaura

Amit Sawhney

Shalu Singh

Harshita Dubey

David Thomas Kirkpatrick

Avanthika Sankararaman

Stephen Ray Young

Harshit Mehta

Systems and methods for assessing the skills of a customer support agent using one or more Artificial Intelligence/Machine Learning (AI/ML) models are disclosed. In at least one embodiment, one or more benchmarks against which the performance of the customer support agent is to be measured are established. The one or more benchmarks may be derived through direct and/or indirect analysis of historical customer service data by an AI/ML benchmark model. In at least one embodiment, data relating to performance of the customer support agent during a customer call is monitored. In at least one embodiment, the AI/ML benchmark model is used to determine one or more benchmark scores identifying whether the customer support agent is meeting the one or more benchmarks.

Intelligent scoring model for assessing the skills of a customer support agent

In one aspect, an example methodology implementing the disclosed techniques includes, in response to receipt at a call center of a call by a caller, generating a compatibility score for the caller, the compatibility score for the caller optimizes for caller satisfaction and potential upsell opportunities. The method also includes generating a compatibility score for individual agents available to handle the call, the compatibility score for the individual agents optimize for caller satisfaction and potential upsell opportunities. The method also includes matching the compatibility score for the caller to the compatibility scores for the individual agents and assigning an agent to the call based on the matching of the compatibility scores for the caller and the individual agents.

Assigning customer calls to customer care agents based on compatability

A machine learning (ML) module that analyzes multiple language-influencing factors to correct textual content in a more meaningful and efficient manner. In the context of product support, the ML module considers the factors such as a customer's persona that shapes the words a customer chooses while speaking/writing to a customer support agent, current social trends that create new words in the social media/social platforms related to the customer's support issue, and the device used by the customer to input the textual content because different words may be input by the customer when using a smart phone versus a desktop/laptop personal computer with a traditional keyboard. The ML module also may analyze the agent's persona to modify agent's response to the customer because the agent's persona can influence the content of the agent's text. The ML module automatically corrects textual content in real-time before it is sent to the relevant recipient.

AI driven content correction built on personas

In some examples, a server may receive usage data from a computing device, determine a usage profile of the computing device, and determine that a component of the computing device is predicted to fail and at what time. If the time is within a warranty period of a current warranty, then the server may recommend purchasing an upgraded warranty and provide a cost-benefit analysis of purchasing the upgraded warranty. If the time is outside the warranty period, then the server may recommend purchasing an extended warranty and provide a cost-benefit analysis of purchasing the extended warranty. The server may use the usage data to make a recommendation to upgrade one or more components (e.g., memory, disk drive, network card, or the like) of the computing device. The server may use the usage data to make a recommendation to upgrade from the computing device to a different computing device.

Using machine learning to predict a usage profile and recommendations associated with a computing device

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Austin, TX, United States of America

Tejas Naren Tennur Narayanan