Dell Products Inc.

Gautam Kaura

Sathish Kumar Bikumala

Tejas Naren Tennur Narayanan

Amit Sawhney

Shalu Singh

Harshita Dubey

Stephen Ray Young

Mallory Anne Kolodzey

Anish Arora

Avanthika Sankararaman

Prateek Mathur

Harshit Mehta

The described technology is generally directed towards processing various customer input data to extract frequently recurring customer experience themes, including positive and negative sentiment regarding customer experiences. Natural language processing, image processing, speech recognition and/or computer vision techniques can be used on customer-related data to determine themes, tests and scenarios, as well as discover insights that can be used to improve customer experiences. The technology can be used to recreate a customer engagement, journey and overall experience by designing test scenarios around failure themes.

Generating test scenarios by detecting failure patterns and themes in customer experiences

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

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

Gautam Kaura