We are Classical

Nowadays it’s pretty clear that to be successful, you need to incorporate data as part of your core business strategy, but getting the best out of your data is not always as simple as it sounds. You read about AI, machine learning, deep learning, big data, neural networks, etc., but what does this mean for a young startup?

In the quest to compile and organise the most complete catalogue of classical music, we’ve encountered quite a few interesting insights into how the composer landscape has evolved over the years. In this article, we’ll take a deep dive into some of the findings we’ve acquired by doing what we love second most: data analysis (our first is classical music of course 🤓).

One of the downsides of the under-representation of classical music in existing streaming services, is that we have very limited exposure to datasets consisting purely of classical music usage. We’re only able to access data on an aggregate level, and since classical music streaming represents about 1% of the streaming market, any meaningful insights we could’ve gained from the peculiarities of classical music lovers, are quickly pushed to oblivion by the more popular genres.