Aditya Guglani

COTA: Improving the customer support experience using Deep Learning

As the leading ridesharing platform, Uber receives hundreds of thousands of support tickets from users daily. Since Uber is a global company with multiple lines of business, we receive tickets in multiple languages and range from missing item from an EATS order in India to fare adjustment request in North America. To scale up the ticket resolution process, we built COTA (Customer Obsession Ticket Assistant) an intelligent system based on machine learning (ML) and natural language processing (NLP) techniques that is integrated with Uber's customer support platform. It provides customer support representatives, suggestions for the ticket type, appropriate reply and relevant actions to take based on ticket text and additional context such as trip information. It is also used for routing tickets more efficiently. As a result, we were able to reduce the average number of re-routings and the time an agent spends on a ticket, thus making the support experience a little more magical.

Aditya is currently a data scientist at Uber where he is using deep learning and natural language processing to provide magical support experiences to customers and driver partners. He has a background in engineering and a graduate degree in data science, which enables him to solve some hard problems using data and machine learning. His interests currently include neural networks, document semantics, and language understanding.

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