Music has tremendous cultural and commercial significance for people the world over. It is one of the oldest human inventions and is among the most popular consumption activities on the planet. The music industry is also of great economic importance with 19 billion dollars in revenue worldwide in 2019. Despite music’s importance and significance, little work has been devoted to understanding what makes some types of music more popular than others or on the implications of success on artists’ subsequent productivity. Earlier studies have investigated psychological and economic aspects of music, but marketing as a field has devoted little attention to understanding the drivers of musical success and the dynamics of the music industry.
In this dissertation, I leverage modern Bayesian non-parametric approaches, machine learning, and novel data to study the dynamic drivers of musical success and the implications of that success. The dissertation is composed of two essays devoted to investigating these complementary questions. In the first essay, I examine the dynamics of success of albums over the last fifty years. I then leverage the results to construct well-balanced playlists that will appeal to different generations of music listeners. My empirical investigation is based on a novel dataset I collected from diverse online sources. The dataset is comprised of albums' movements up and down Billboard magazine’s annual Top 200 lists of albums, marketing and standard descriptors of the albums such as genre and artist popularity, acoustic descriptors of the albums' tracks such as the songs’ acoustic fingerprints, and user-generated tags describing the albums’ and songs’ consumption context and the experience perceived by listeners. I develop a novel Bayesian non-parametric model that fuses the diverse data modalities and predicts the dynamic patterns of musical success over the years. The model generates results regarding how musical acoustic qualities and genres have waxed and waned in popularity over time. It also uses tags listeners generate online to uncover themes that categorize albums in terms of sub-genres, consumption contexts, emotions, evocation of nostalgia, and other aspects of the musical experience. The model yields insightful results about the evolution of album success in the music industry. These insights are relevant to artists and music professionals who recommend albums, design new releases, and construct well-balanced playlists aimed at various generations of listeners.
The second essay is devoted to quantifying the effects of winning the Grammy for Best New Artist on artists’ productivity and musical variety. The causal identification strategy is based on comparing subsequent outcomes in terms of both productivity and diversification of musical styles and elements winners of and contenders for the award. This strategy allows the model to control for ability bias and improves confidence in the estimated causal effects. The study is based on a dataset I collected from diverse online sources that spans the entire history of the Best New Artist award and contains integral album discographies of the nominees, most of their released songs, and their acoustic descriptors. I use a two-way fixed effects approach to measure the causal effect of the award and incorporate heterogeneity in the treatment effects. The results yield interesting insights into positive effects of the award on productivity. Interestingly, my investigation also reveals that the effects of winning the award are heterogeneous in terms of gender and that male solo singers benefit more than female solo singers and groups, male groups, and mixed-gender groups. In contrast, winning the award does not affect artistic variety on average, though winners tend to explore new artistic dimensions that are congruent with their musical specialties than contenders do.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-6rpy-2t28 |
| Date | January 2020 |
| Creators | Boughanmi, Khaled |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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