Music Preferences as Predictors of Consumer Behaviour

With the increasing role of the Internet and online products promotion, it is important to know the target audience and understand its specific consumer behaviour in an online environment for creating and driving successful marketing campaigns. The new trends include personalization systems, and this research focuses on individual profile user’s data for proper analysis and understanding customer’s actions. This work is focused on the analysis of people’ music preferences as a predictor mechanism of their consumer behavior. Music is a powerful instrument that reflects the character of a person in comparison with demographic data, so that music can be used as a behaviour predictor. The paradigm of this paper is predictive analytics and research analysis based on data investigation. The research is done using data from Yandex. Market and Yandex. Music platforms in order to find the connection between users' music preferences and their online purchases. In the first chapter of the master thesis we provide a theoretical overview on consumer behaviour (it’s stimulus and responses), the power of user profile information in online marketing and music as an important phenomenon and its influence on social and psychological spheres of our lives. The second chapter consists of methods description and data collection that are used for analysis. The third chapter we build nineteen predictive models, provide empirical analysis results of music preferences' influence on consumer behavior. The result of the paper shows the predictive power of a user profile revealed through his music preferences and purchases on Yandex platforms.