Migrating millions of users from voice- and email-based customer support to a chatbot
Who is this presentation for?
- Data scientists, machine learning engineers, architects, and product managers
At MakeMyTrip, a large number of customer care agents were dedicated to handling customer support issues over voice and email. Given the popularity of WhatsApp in India, conversational interfaces have become mainstream for the Indian consumer. MakeMyTrip decided to capitalize on this trend and revamped customer support channels using a chatbot named Myra. Myra is adept at handling the languages of the Indian consumer including English, Hindi, and a mix of the two popularly known as Hinglish.
Madhu Gopinathan and Sanjay Mohan explain the high-level architecture and the business impact Myra created. Myra handles user input using a natural language understanding framework that solves problems such as intent classification and slot extraction using the latest advances in deep learning. In the intent classification problem, “Please cancel my ticket” should be mapped to a label "full cancellation.” The intent classification model supports over 120 intents and is sophisticated enough to distinguish between fine-grained intents such as full cancellation, partial cancellation, and flight cancellation by airlines. In the slot extraction problem, from, “Please cancel my ticket to New York,” the value, “New York” should be extracted to fill the destination slot.
For curating the data required for training deep learning models, MakeMyTrip employed multiple methods, such as manual tagging, creating language models from existing email conversations, progressive refinement of the models, and natural language generation. In this case study, Madhu and Sanjay dive into the insights from developing deep learning models to power Myra, revamping of agent tools, and processes to support Myra and the ensuing business impact.
What you'll learn
- Discover insights from deploying a conversational bot and associated agent systems to improve postsale service
- Dive into architecture, strategies for data curation to train deep learning models, and the business impact from deploying a chatbot based on these models
Madhu Gopinathan is the vice president of data science at MakeMyTrip, India’s leading online travel company. Previously, he started his career in the San Francisco Bay Area developing large-scale software systems in the telecom industry; spent about 10 years at companies such as Covad Communications and Microsoft; built a machine learning at team at Infosys Product Incubation Group; which was followed by couple of startups. He collaborated with researchers at Microsoft Research, General Motors, and the Indian Institute of Science, leading to publications in prominent computer science conferences. He earned his PhD in computer science from the Indian Institute of Science, working on the mathematical modeling of software systems, and his MS in computer science from the University of Florida, Gainesville. He has extensive experience developing large scale systems using machine learning and natural language processing and has been granted multiple US patents.
Sanjay Mohan is the group chief technology officer at MakeMyTrip. He leads the overall technology for MakeMyTrip, Goibibo, and redBus. He’s responsible for developing and executing global technology strategy and planning ongoing technology innovations for continued success. Previously, Sanjay was at Yahoo, IBM, Infosys, Oracle, and Netscape in the US and in India; he was the chief technology officer of MakeMyTrip prior to the merger with ibibo Group. With an overall experience of 25+ years, Sanjay brings along significant expertise in product and platform engineering, including architecture, user experience, site operations, product management, and product strategy. Sanjay earned his master’s degree in computer science from the University of Louisiana and his bachelor’s degree in engineering from the Birla Institute of Technology Mesra.
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