Yu Liang, Aditya Ponnada, Paul Lamere, Nediyana Daskalova
Minimizing change aversion through mixed methods research: a case study of redesigning Spotify’s Your Library
Launching a radical change to a habitual feature, used by millions every day, presents a challenge both to the end user and the organization making the change. Change aversion to habitually used features is a known factor in users experience design, but at times major changes are needed to encompass feature growth ultimately benefiting users. In this case study, we present examples of how data science and user research collaborated during the redesign the mobile Library feature at Spotify. Major adaptions were needed in order to enable future opportunities. The challenge required a high degree of sensitivity to users’ needs within what is often considered their space in the world of streaming. We believe that close collaboration between the product, design, engineering, data science, and user research disciplines, with a focus on quantitative and qualitative mixed methods insight work in early development phases, enabled the experience to be launched with the users’ experience at the forefront and thus minimising change aversion.
Katariina Martikainen, Jussi Karlgren, Khiet Truong
TastePaths: Enabling deeper exploration and understanding of personal preferences in recommender systems
Savvas Petridis, Nediyana Daskalova, Sarah Mennicken, Samuel F. Way, Paul Lamere, Jennifer Thom