Last.fm is a music listening and tracking tool that peaked in popularity during the latter part of the 00s, competing with other music radio services like Pandora before Spotify and Apple Music came into the market. It’s still active today, but its radio services have been discontinued, and instead, the company has focused more on the music discovery and tracking services in recent years. Last.fm uses something called “scrobbles” (or “scrobbling”) – basically their word for “tracks played” to track what you’ve listened to, connect with other users, and discover new artists. The idea of “scrobbing”/showing users their long-term listening history is something that Spotify really hasn’t pounced on yet on the front-end side of things, which leaves connecting Spotify to Last.fm as the best way for those who are curious to see their entire musical listening history.
I recently highlighted a very cool tool made by a Spotify developer that uses Echo Nest data to dig into specific stats about the songs in a playlist, which got me inspired to look into more music visualization tools available online. I was pleased to find that Last.fm and its accompanying API, allowed developers the ability to dig into a users scrobbles for as long as you have had a Last.fm account. I actively used Last.fm during most of college (2010-13 give or take), but only realized that you could connect it with Spotify in late 2016, so for the purpose of highlighting these tools, I’ll be focusing on my musical listening data from only 2017.
By far the coolest Last.fm integrated tool I found, this tool visualizes your listening history by artist, album, or specific track and places it into a colored wave graph. Here’s an example of what you can create:
This is pretty much what I was hoping to find – a visualization tool to show my musical listening patterns. It highlights my simultaneous obsession with Kesha’s new album Rainbow and the Rolling Stones’ Sticky Fingers over the summer, how I listened to the Beatles pretty consistently for a long time before switching to the Stones, the extended listening period and subsequent fade out of Paramore, Gorillaz, and MGMT when they each released new material, as well as some more minor phases (mainly bottom of the chart – T. Rex, Taylor Swift, Melody’s Echo Chamber, Noel Gallagher). There are some issues with this chart: I didn’t realistically listen to the Beatles non-stop from February to May (that’s a result of listening to the Beatles a whole lot and it being stretched out by the tool for visual purposes, I believe). I’m hoping that with more time, I can update this chart and start to really dig into my musical cycles or patterns (like, how often do I really listen to the Beatles? What time of year? etc). More to come here, but still, amazingly cool program.
A tool that measures how “mainstream” your top scrobbled artists are.
This program uses the number of times you’ve scrobbled an artist in a specified time period as a weight and the number of listeners on Last.fm who listen to that artist to determine that artist’s “% mainstream” (where Coldplay is 100% mainstream). Apparently I’m “borderline mainstream,” and my girlfriend is firmly “cool.” This seems like more of a measure of how obscure the artists you listen to are. There also may be some bias here against (or for depending how you look at it?) older bands who may have more listeners because they have had more time to garner those listeners.
If you’re a nerd and want to do heavier analysis on your listening history, this tool also gives you the ability to export your entire Last.FM listening history to Excel, which is an amazing and super useful feature.
A mapping tool that shows which countries you’ve scrobbled artists from based off of Last.fm tags (this doesn’t give you the option of using a time frame, so this data goes back to my college days).
No real surprise here – most of the bands I listen to are from predominantly English speaking countries, namely the US, UK, Australia, and Canada. A little surprised that I haven’t scrobbled anything from China or India, or pretty much any of Africa in all these years. Goal for 2018 – get those countries highlighted on this map.
A simple, pie and bar chart driven tool that gives you statistics about how many unique tracks you’ve played in a certain timeframe. It can also give you information about how often you listen to your top artists, albums, or tracks versus the rest of the field in a certain time frame.
There’s a lot of potential with this tool, and while it gives some interesting insights, it barely scratches the surface of what it could realistically do. There’s also a lot missing from a visual standpoint that some of the other tools on this list have nearly perfected.