Employing AI to extract actionable insights from qualitative user feedback/comments

Everyday, thousands of DocuSign users leave qualitative product reviews via in-product surveys. These reviews represent a trove of rich data on user pain points and potential product innovations, but this information has been largely untapped due to the lack of a process for sifting actionable, product-specific comments from the larger dataset. In our presentation, we will describe how we built and deployed a multi-layer ensemble of models to organize and analyze qualitative user comments, allowing internal end users to drill down on relevance, sentiment, and topic

Indrasis is currently working at DocuSign as a Sr. Director where he is focusing on building the data science, product insights, and experimentation functions, practices, and capabilities to enable a culture of Data-Driven product development. Before DocuSign, Indrasis was at Hulu where he led a global team of engineers, architects, product managers, data scientists, and analysts to develop Data Products and Data Solutions across the organization. Prior to Hulu, Indrasis worked at Getty Images where he led the data product and engineering teams and helped develop Getty's big data strategy and platform to enable data democratization, data monetization, and self-service analytics capabilities. A passionate enthusiast of building culture and products to drive data-driven decision making, Indrasis has had years of experience in leading both technology and product teams around big data, data science, and machine learning. Indrasis holds an Executive MBA from Seattle University's Albers School of Business and Economics, and an MS in EE with a concentration in statistical signal processing from Illinois Institute of Technology, Chicago.