Musicians today measure success by virality or streaming numbers. Ideally, of course, you’d have both, but the truth is that only a small proportion of songs “make it” on both counts. What can we learn by using our Tag API to analyze viral hits, billboard hits, and the rare songs that manage to do both?
To answer this question, we looked at 104 weeks of 2 separate charts over the last 2 years (July 2019 to July 2021): the Spotify Weekly Top 200, and the Spotify Weekly Viral 50.
- From a total of 20800 songs (200 songs a week for 104 weeks) in the raw Spotify Weekly Top 200 (Top 200) dataset, we removed duplicates and ended up with a dataset of 1806 unique songs.
- From a total of 5200 songs (50 songs a week for 104 weeks) in the raw Spotify Weekly Viral 50 (Viral 50) dataset, we removed duplicates and ended up with a dataset of 1529 unique songs
The first insight that emerged from this initial culling of the raw dataset was that the Viral 50 dataset was much more varied than the Top 200 dataset, with 29.4% Viral songs being unique, versus just 8.68% of Top 200 songs (almost 4 times as many unique songs by proportion).
This suggests that the Top 200 charts are much more competitive than the Viral 50, with fewer unique songs being featured overall, and more songs dominating the charts week after week.
Before analyzing the datasets to determine meaningful differences between Top 200 and Viral 50 songs, we wanted to ensure that they did not just contain the same songs as each other.
There were 3335 songs in total. 468 songs were found in both sets, leaving us with 2399 songs that only appeared in either the Viral 50 or Top 200 sets. With approximately 72% of the whole dataset appearing only in one set or the other, we considered the two datasets sufficiently differentiated to provide meaningful insight when compared.
So let’s get into the results:
Key Differences In Genre
In Viral 50, Hip Hop, Trap, Latin, and Reggaeton less present, while Pop, Electronic, and Folk were better represented. The differences are at most 3+%, and are as such fairly subtle.
Less Power, More Romance
Viral 50 songs were less powerful and quirky, and they were more romantic and relaxed. Interestingly, the Energetic and Happy Moods were almost exactly equally represented in both sets.
Less Middle Ground In Energy
We found that Viral 50 songs tended to be less likely to be assigned Medium energy, with a marked increase in High Energy and and general increase across all other Energy levels.
Some Things Never Change
Mood Valence and Key were virtually identical between the Viral 50 and Top 200 sets, suggesting a fairly universal distribution of preferences in the case of these tags.
Viral 50 songs feature slightly more female vocals than Top 200 songs
Female vocals in the Viral 50 set saw an almost 3% bump compared to the Top 200 set.
Viral and Top 200 Tempos Are Quite Different – Could They Explain A Trend?
Viral 50 songs show a decreased preference for 90-99 BPM, and an increased preference for 120-129 BPM. Their adjacent tempos 80-89 BPM and 130-139 BPM are also similarly decreased and increased, respectively.
More research is needed to confirm this, but this may be due to a preference for more upbeat/danceable music by creators in TikTok, leading to songs in those tempos being more likely to go Viral.
Analysis of Top 200 preferences on a week-by-week basis over 104 weeks indicates a similar trend over time. This, in addition to significant increase in TikTok use over the past 2 years may suggest that Viral music could be having an influence on Top 200 preferences over time.
What About Songs That Make It To Both Charts?
We also analyzed the 468 songs to make it to both charts. Certain striking features included a greater than average preference for positive Mood Valence, as well as a pronounced preference for the 90-99 BPM tempo range.
Conclusion – Mostly Subtle Differences
Most of the differences that emerged between the two sets were fairly nuanced. More significant findings are likely to be found in comparisons between Viral/Top 200 sets in individual countries, versus the entire Global set.
However, considering that Key and Mood Valence are virtually identical between the two sets, it would probably be advisable for labels and songwriters to put out tracks that are Positive and in a Minor Key.
Overall, tagging and analyzing successful datasets such as these remain a useful way to take the pulse of a competitive and fast-moving music marketplace.
I am a composer and run a small distributed music, voice, and audio production team comprised of experts in 3 countries. I have a deep interest in startups, business, venture capital, networking, and learning about new ideas. I love meeting founders, angels, and people in VC and private equity to talk shop and connect people that need each other. I've Served clients and brands including Ubisoft, Garena (SEA), IGG Games, Hogarth Worldwide, We Are Social, Moving Bits, Playstudios, Ferrero, Samsung, GSK, Pernod Ricard, Pan Pacific Hotels etc. I’ve also worked with Wang Leehom, Joanna Dong, Derrick Hoh, Luke Slott, and a number of other artists. Founded and ran a professional orchestra in Boston for 5 years.