Abstract
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 interactiondata from existing
designscan 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 modelstrained
on a set of effective design examples can support predictive workflows
that allow designers to rapidly prototype new, performant solutions.
Bio
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.