  Every generation throws a hero up the pop charts. For the current generation,
one of the most relevant pop charts is the Spotify Top 200. Spotify is the
world's largest music streaming service and the Top 200 is a daily list of the
platform's 200 most streamed songs. In this paper, we analyze a data set
collected from over 20 months of these rankings. Via exploratory data analysis,
we investigate the popularity, rarity, and longevity of songs on the Top 200
and we construct a stochastic process model for the daily streaming counts that
draws upon ideas from stochastic intensity point processes and marked point
processes. Using the parameters of this model as estimated from the Top 200
data, we apply a clustering algorithm to identify songs with similar features
and performance.
