AI Insights
We analyzed 1587 songs that made it to the USA Spotify Viral 50 charts during 2019 and 2020 to find out which Moods were most popular and correlated with commercial success.
A simple method of using comparison datasets to determine the market fit and overall potential of a piece of music, with a combination of objective and contextual data. It’s so simple that you don’t even need to be a data scientist to do it.
For a time, guitar gods roamed the earth. Steve Vai, Yngwie Malmsteen, Paul Gilbert – the idols of many a sweaty teenager, who no doubt drove the sales of guitars through the roof as the entire world dreamed of being able to play like them.
Let’s take a fun look at some songs graced by these deities, and quantify their impact with our Tag Demo.
Many independent songwriters and music producers toil day and night to create music that they hope will make their way into the mainstream. But depending on your chosen genre, you might be facing a great deal of competition for a limited number of spots on the global charts.
In this article, we tagged and compared genre distributions between two sets to identify ‘overrepresented’ and ‘underrepresented’ genres.
WHAT IS HIT POTENTIAL? Our AI-powered Hit Potential Algorithm ‘listens’ to tracks and returns a…
Following up on our previous study on Viral music in SEA, we repeated our experiment for robustness – this time with 15.6k songs.
We further put our Hit Potential AI to the test – this time with a larger sample size of 14k tracks.
2020 was an intense year for the USA. Protests, a hotly contested election, and the…
There are many tempos for songwriters and producers to choose from. Is there a way to know which ones are most likely to lead to a commercially successful work? We studied a total of ~6000 songs and uncovered some striking tempo choice trends.
We used our AI to analyze 5,200 viral songs from 104 weekly charts in the USA from 2019-2020 to see if we could discern useful patterns in Genre with actionable takeaways for artists and labels.