3 Ways to Analyze Your Apple Music Listening Habits

Although late to the party, Apple Music users can now analyze their data and listening history to uncover fun insights and re-listen to music that they previously had on repeat.

It’s very useful to look over your past listening habits to create great playlists, revisit old songs, and to simply ‘nerd out’!

Luckily, several third party web apps offer more in-depth analysis and show what a great programmer can accomplish without funding!

From visual representation to auto-generated playlists, let’s look at some ways to harness the treasures in your music habits:

1. Apple Replay

apple-replay

Go to Apple Replay

Apple Music now offers the Replay feature that will create an auto-generated playlist that allows you to dip back into your listening habits in any given year.

To access it, use your Apple ID to log in to the Apple web player and press on the “Get Replay Mix” button on this page.

It will analyze your listening habits and compile a playlist of your most frequently played songs in the previous year, or any other year that you have been using their service.

It also shows the Top Artists of any given year and interesting stats like how many artists and albums you heard in that year.

The recap or yearly roundup playlist can be found in the “Listen Now” section is listed in the section called “Replay: Top Songs By Year”.

It is a compilation of your most listened to songs. You can access it from the Music app on any iOS or Mac device

There is one obvious caveat. These data insights – and the ensuing nostalgia – is only available to registered Apple Music users.

Apple joined the ‘dip-back’ part two years ago. However, that has not stopped developers and users from finding workarounds. We’ll discuss some of them in detail.

2. Pat Murray’s Apple Music Analyzer

Screenshot of music.samthegeek.net

Go to website: Apple Music Analyzer

Before apple joined the ‘dip-back party’, Pat Murray, an iOS developer had made an app that gives you a breakdown of your listening habits. His web app analysis is based on your Apple music data that can be downloaded from their server.

The process that follows is fairly simple. Log on to the website and load your data into the app. The analyzer shows you the most played song each year with the number of plays (and the hours it amounts to).

It also displays the total hours, plays, songs, and artists. Other cool stats include the most played artists, how many songs failed to load, and the count of viewed lyrics.

The most frequently played songs or artists are presented in descending order with the number of times or plays per artist. The “reasons why a song finished’ is another cool feature of the web app.

Lastly, there is a graphic interpretation of playing time by date and by the hour of the day/week.

Don’t worry about your privacy and data with regards to Murray’s app. None of it leaves your computer and you can read the source of the app here.

Moreover, the web app even works offline all computations and present users with their data.

3. Apple Music Dashboard

Screenshot of www.acoullandreau.com

Go to website: Apple Music Dashboard

Alexina Coullandreau created the Apple Music Dashboard project that is one of the most fun ways to visually analyze your habits.

It is a free service for Apple Music users and all the graphs can be downloaded.

The core process is similar to the one mentioned above but the results are distinctly visual. To get started, load your Apple Music data here.

It will analyze it and show you interactive visual graphs that are displayed in three main modes: Calendar view, Favorites, and Listening Patterns.

They primarily focus on data related to genre, artists, and song titles with a plethora of modifications that can be applied using the filters.

The graphs show more info as you hover over the data points. You can also press on the slices to filter out the section of data.

Some examples are the ability to analyze what day of the week you are most active or which hour(s) of the day you listen to music the most.

You can also check how often you skip tracks or which year saw the most activity.

From a day-by-day to the overall analysis, their Plotly library visualizations are a treat to decipher and have some great features.

Just as with Murray’s app, your data is completely safe. The processing is performed locally to ensure that no data risks are involved.

Why is it worth analyzing your music listening habits?

Creating great playlists

Studies show that as we grow older, we settle into ‘favorites’ and become less inclined to exploring new music. Your listening habits are ‘you-centric’ and go well beyond the general information feeding the trending circuits on streaming platforms.

In simple words, they tell a story, of which YOU are the protagonist. And, minus the phase when you had kids and overdosed on the OST from Frozen, you are bound to find some pearls in the data.

Pearls that you can string into a playlist that you know is suited to your taste for sure.

Revisiting old songs you used to love

It is always a pleasure to re-discover a ‘lost band’ or ‘era favorite’. We don’t always stop liking them, often we just drift away in the waves of overexposure that we are subject to.

At other times, we just binge listen to a track and saturate the joy of it. Returning to it after a break is always a pleasant experience.

Secondly, there is a strong emotional response associated with our favorite songs. Just like a fond memory from childhood, a track from the 90s can completely change your state of mind.

Returning to old favorites may also help you distress. After all, true comfort lies in what we deem familiar.

Curiosity and ‘Nerding Out’

These ‘music analyzing features’ on every platform play into the needs of inquisitive people. The whole exercise only takes a few minutes, but it is a fascinating way to data-dive and identify patterns in the way you use a service.

Besides the insights, it is a fascinating way to understand your life and lead to hours of entertainment.

In the near future, it will be a thought-provoking moment to review 10 years of music habits and tracking your movement from Beastie Boys to Norah Jones (or any other deviant course you chart).

Conversely, for better or worse, you might realize that you have been listening to Dire Straits and CCR, forever, which is a good thing.

Conclusion

Data science is transforming our listening experience and the music industry in general.

Analytics, more often than, serve the marketing department of the companies more than the general users. They are behind the trajectories in trend forecasting and music recommendations.

Eventually, platforms began to provide data-dive opportunities to users. It surfaced as a chance to dip back into your listening habits with services such as Music Year in Review (YouTube), Scrobbling (Last.fm), and Wrapped (Spotify).

As listeners, we are only encouraged to lean forward to keep up with a never-ending train of upcoming releases and the latest trends. Yet, every once in a while, it feels great to take a step back and revisit old favorites through these web apps.

The only drawback, if any, is that you cannot edit the existing analysis by adding new data points. In simple words, you have to run it again – from scratch – when you want a new version. Although, given the nature of it, you won’t need to do that very often.

I’ve rounded up services that provide a great way to deconstruct your past and discover your listening habits using your Apple Music data. That said, I hope this post will take you on a long, free, and fun adventure.

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