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Prediction of product success: explaining song popularity by audio features from Spotify data

Nijkamp, Rutger (2018) Prediction of product success: explaining song popularity by audio features from Spotify data.

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Abstract:This research investigates the relationship between song data - audio features from the Spotify database (e.g. key and tempo) - and song popularity measured by the number of streams a song has on Spotify. Previous research on the topic of new product success prediction have identified multiple approaches to asking this question. Especially for products in cultural markets like music, prediction modelling is very complex. A relatively novel approach, the attribute-approach was used to explore whether song attributes have an explanatory power on stream count. Research in this specific field called Hit Song Science (HSS) has not before measured similar audio features with song popularity in stream count, which is very important for record companies and which makes this research unique. Furthermore, beneficial implications from HSS can be far-reaching to consumers, record companies and for Spotify in new value creation. From the Spotify database API, a 1000 songs were analyzed from 10 different genres. By regression, a prediction model was built. We can conclude that our results suggest that audio features from Spotify have little to moderate explanatory power for a higher stream count, with this research design. Some significant relationships however were found, which lays a promising foundation for the research in prediction with these variables. This research contributes to further understanding in the field of HSS and the new product success prediction. Creating effective prediction models is an interesting next step to this research and so would be to expand on the variables used. Practical implications include that Spotify can further develop its database and calculations of the variables for in the future their databases will play an important role in new value creation.
Item Type:Essay (Bachelor)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:02 science and culture in general
Programme:International Business Administration BSc (50952)
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