User mapping in audio streaming services
Examensarbete på grundnivå
Abstract The aim of this project has been to provide insights on how users utilize streaming services for listening to audio in their daily lives and to anchor the insights in how technologies work to provide them with the user experience they desire. Additional objectives of the project have been to ideate around possible improvements to the user experience based on the insights and also to examine how implementation might affect privacy and social sustainability. Several methods for data gathering were used, including street and personal interviews and an online questionnaire as well as methods for analyzing gathered data, such as the KJ-methodology, brainwriting and brainstorming. Insights from the user research show that, within the target audience of people between 20-30 years old who reside in an urban environment, music is by far the most common form of audio consumed, followed by the relatively new form of audio known as podcasts. Since users of streaming services now have access to a large variety of music, they listen to different kinds of music as a tool in their daily lives to steer their mindset and mood in a way they desire. Music has to some degree become a background to whatever else they are doing, helping them perform the way they want. They have also given a lot of control over their music consumption to the streaming services, giving the services access to data in order to understand their taste and preferences so that it can play music that they like without having to put much effort into finding and choosing themselves. There is also a desire amongst the users for these services to become better at finding the right music for them. At the same time, there is a fear of giving up one’s privacy and losing control over what data that is being shared with companies that provide audio streaming services. Taking trends within technology into consideration, like big data and the vast data mining and analyzing power that comes with AI and machine learning, the conclusions made from this project points towards the possibility of creating an automated listening experience that answers specific user desires, but that also offers a more transparent and intuitive way of giving users a better overview and control over how their data is processed.