Tuesday 12pm, 19 February 2019
Data-Driven Design: Beyond A/B Testing
Assistant Professor - UIUC
A/B testing has become the de facto standard for optimizing design, helping designers craft more effective user experiences by leveraging data. A typical A/B test involves dividing user traffic between two experimental conditions (A and B), and looking for statistically significant differences in performance indicators (e.g., conversion rates) between them. In this talk, I'll introduce three other data-driven methods --- complementary to A/B testing --- that can tie design choices to desired outcomes. Mining interaction data from existing designs can provide comparative insights about patterns found in the wild, exposing designers to a greater space of divergent solutions than A/B testing. Lightweight prototypes with tight user feedback loops, or experimentation engines, can bootstrap product design involving technologies that are actively being developed (e.g., artificial intelligence, virtual/augmented reality), where both use cases and capabilities are not well-understood. Finally, generative models trained on a set of effective design examples can support predictive workflows that allow designers to rapidly prototype new, performant solutions.
Ranjitha Kumar is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), where she leads the Data-Driven Design group. She is the recipient of a 2018 NSF CAREER award, and UIUC’s 2018 C.W. Gear Outstanding Junior Faculty Award. Her research has won best paper awards/nominations at premier conferences in HCI, and is supported by grants from Google, Amazon, and Adobe. She received her PhD from the Computer Science Department at Stanford University in 2014, and was formerly the Chief Scientist at Apropose, Inc., a data-driven design company she founded that was backed by Andreessen Horowitz and New Enterprise Associates.