Abstract: How did Salesforce use Einstein to rapidly build an application that helps salesforce provide an optimal product experience to their customers and maximize adoption?
The Data Intelligence (Di) team within Salesforce seamlessly merged Einstein technology and design thinking to build an application driven by a Net Adoption Score, used to both automatically identify and suggest guidance for customer success managers to help customers accelerate product time to value, and to inform product executives where to double down to make products customers love.
In this session, the Di team will present a case study of how they developed this application The talk will highlight the cutting edge technology (including deep-learning and advanced data visualization) and processes that solved the challenges inherent in enabling executives to compare product adoption across several billion dollar businesses, monetized in completely different ways, ultimately driving customers success.
Bio: Robin Glinton is Vice President of Data Science Applications at Salesforce.com. He leads a team dedicated to understanding adoption of Salesforce products as well as applied research in Machine Learning driven CRM offerings. Robin has held a number of positions across startups, industry, and academia including support of omnichannel marketing across Sears, Kmart, and the 50M member ShopYourWay loyalty program. At startup kWantera he implemented end-to-end analytics as a service systems to support energy procurement for large corporations. His research at the Robotics Institute, Carnegie Mellon resulted in over 30 refereed papers and journals on the subject of distributed AI.
Vice President of Data Science Applications | Salesforce