RINA TAMBO JENSEN
Case Study—This is a case about how Mozilla, the open source browser company, set out to reconnect with ‘collaborating in the open’ to regain its competitive advantage. This case describes how a multi-disciplinary research team used ethnographic, market, and data analysis to articulate and clarify the problem, and build a strategy towards revitalizing Openness at Mozilla. It will aim to prove that the subsequent change achieved could only have been accomplished by a mixed method research approach. And importantly show, how the team used data to prove the distribution of findings, coupled with ethnography to shine light on the why and how of those findings. The case study will do this by discussing the key insights and how these fueled recommendation and subsequent change in the organisation.
The project presented many problems: from convincing stakeholders of the need to fully explore the problem, to connecting widely different research methods and gleaning insights that built strongly on all strands...
Those who work in research know that we live in a world that is strongly influenced by what Tricia Wang has called the quantification bias. More so than other forms of information, numbers have incredible formative power. In our culture, numbers are seen as trustworthy representations of reality that are strongly associated with objectivity and untainted by human bias and shortcomings. Recently, data science, big data, algorithms, and machine learning have fueled a new wave of the quantification bias. One of the central fascinations of this wave has been the promise that humans now have the power of prediction at their fingertips. In this paper, I reflect on what it means to make predictions and explore the differences in how predictions are accomplished via quantitative modeling and ethnographic observation. While this is not the first time that ethnographic work has been put in conversation and in contrast with quantified practices, most theorists have framed the role of ethnography as providing context...
by SARA BELT and PETER GILKS, Spotify
Sara Belt and Peter Gilks respectively lead the Creator and Free Revenue Product Insights teams at Spotify. In this article, Sara will explore the practice of User Research at Spotify, and Peter will lay out how Data Science and User Research work together to drive product decisions.
Part 1. User research at Spotify
Sara Belt, Head of Creator Product Insights
When I say I work in user research at Spotify, folks' minds tend to travel in two directions: they figure I research either the kinds of music people listen to or the music itself: melodies, harmonies, rhythms, and how they impact people. Because, you know, what else is there to research with the world’s biggest music player?
Over the past few years, Spotify has grown to be much more than that, and the research scope has grown with it. My team, for example, is focused on artists and the music industry ecosystem—how Spotify can help artists grow an audience, express their creativity, and thrive. We research fandom and how it...