I have discontinued this service, thank you though for everyone who signed up! Learned a lot on bots, automation and design on this one. :-)

Hello! 👋

I had some spare time. So I built myself a little WhatsApp bot that sends me a daily update on the NL Spotify Top 200.

The problem I was solving for myself: I have a database with daily chart data going back to 2018, and I was still opening Spotify's charting website every time I wanted a quick read. Two issues with that:

One: the extra step of actually opening it is just enough friction to make you not bother.

Two: and this is the bigger one, Spotify's own chart page doesn't tell you all that much. You get a ranking. That's about it. No local market share, no label splits, no sense of what's trending versus what's fading.

So I built the thing I actually wanted. Every afternoon the NL charts update, my bot checks for new data every half hour from 4pm onwards and sends a WhatsApp the moment it drops: local share across the chart, label and local vs. international splits, biggest movers, new entries, trending tracks. No login, no tab switching: just the context that actually matters, already in my pocket.

The message itself contains a daily preview card — a visual snapshot of the key numbers at a glance. If you want to go deeper, tapping the image on mobile (or clicking the link on desktop) takes you to the full card: a more extensive breakdown with label shares, local shares, chart movers, new entries, YTD artist rankings and more.

As many of you know, I'm big on making data more accessible, and since it’s all just public data I've opened it up. You can leave your number at chartalert.plantbo.app and you're in.

It'll stay free for the foreseeable future. Full transparency: Each message costs me around $0.07 to deliver, in addition to some monthly subscription fees (approx. 15-20 bucks), so costs will grow with the number of sign-ups — but for now it's manageable and I'm happy to keep it that way.

The label classification is based on a mapping I maintain. I believe it's pretty solid, but label rosters shift constantly — I'll be keeping a closer eye on any noteworthy changes.

Knowing myself I’ll probably keep adding to this along the way!

Cheers!

Bo

Since my last update, I definitely haven’t been sitting still.
There’s been a long (and short) list of ideas I’ve been vibing with, some practical, some just fun to explore. At this point, if vibe-coding counts as a hobby… it’s probably one of mine 😅

Alongside a few new client projects and some personal experiments, I kept coming back to something older. A project that has already resurfaced twice before, and that felt ready for a proper re-imagining.

One of the clearest ways to see how fast technology is moving is to revisit your own old work.

This idea started as one of the very first things I shared on my blog (back when it was still on Medium). I tried bringing it back once more later on. And now, this week, I rebuilt it again, this time as an interactive tool that actually lets the idea breathe.

Initial Attempt on Visializing Genre Movements in NL

Seeing those three versions side by side is honestly wild.
Not just the technical differences, but how much more alive the concept feels each time.

Back then, I was already thrilled that the “movement of music genres” visualization ended up being used by a data-viz community in their magazine.
The funny part? Under the hood it was all stitched together with slightly painful BI tooling (hello Tableau…), plus a lot of extra design work layered on top.

The second attempt came somewhere in early 2024.
That version involved plenty of buggy Python generated by ChatGPT, a rough grid of Dutch chart data, and (again) quite a bit of Photoshop cleanup to make it presentable.

Second attempt on Visualising the Dutch Music Landscape

Recently, someone mentioned the design again and asked if I was planning a 2025 update. I wanted to… but it also felt like the tools we have now should let us push it much further:

• make it more visually refined, something closer to a gallery piece

• make it interactive and engaging

• give people choices in how they experience it

So I started wondering:

What if you could highlight your favorite artist?
Switch between color themes inspired by Dutch modernism or MoMA minimalism?
Toggle between a square poster and a wide landscape?

I gave it a shot… and I’m pretty happy with how it turned out.

Give it a spin and let me know what you think.

^{Quick disclaimer: even with all the different methods I’ve explored, genre labels still come down to song metadata, and genres are never fully objective anyway.}

^{For the ‘By Label’ mapping, probably the most complicated, I’ve applied quite a few rules to group releases into the major labels. Still, one might slip through now and then. That’s why I’ve added the option for you to overwrite the label directly on the canvas, if needed (use wisely & refresh :-)).}

Album Cover View

Frenna highlighted

Dutch Modernist theme | by genre

See you next time! 👋

“Effe Serieus”, hard to miss in the Dutch music space right now: Rijck. 😉

His release strategy, and the way he builds anticipation, has been genuinely refreshing. While scrolling through Reels, I kept seeing artists and industry voices point to his rollout as something that “needs to be studied.”

So I did exactly that. 🤷‍♀️

I’ll let the article speak for itself, but it’s a data essay on Rijck and, more broadly, on how the Dutch hip-hop scene is releasing music right now.

Let this also be a small reminder: now that AI makes data, design, and analysis more accessible at a low cost, the standard for reporting and insight should be higher than ever. There’s no excuse for insights to hide in dull spreadsheets or badly designed dashboards.

Hope you enjoy it, and feel free to share any thoughts or ideas.

^{You can read the article over here.}

Over the next couple of weeks, I’ll be vibe-coding a mock creator coin strategy, exploring how artists (or authors) could use it to engage their fanbase. So keep an eye out.

Have a good week, cheers! 👋

It’s been a while.

Lately, I’ve been drifting into what I’ll call my vibe-coding era.

I was planning to spend some time in Barcelona to learn something new. Due to last-minute curriculum changes, that fell through. Unfortunate, but it pushed me to explore how far you can take vibe coding in music and books instead. Something different, but just as exciting.

Going forward, I want to spend more time showing how easy it has become to gather data, form an idea, and turn that idea into something tangible you can ship, test, or share. Often in days. Sometimes in hours. We all know, in fast-moving industries like music and publishing, that speed matters.

Not long ago, the barriers were very real: budget, time, technical skills, access to developers or designers. Even the mental overhead of getting started. Those constraints shaped what got built, and what quietly stayed in notebooks.

Building something as simple as a Spotify API-connected tool meant clearing budgets, hiring external teams, and waiting weeks or months before seeing anything usable. The same applied to internal scraping projects or basic data visualisations, often blocked by headcount, tooling, or design limitations. The real cost wasn’t just money. It was momentum.

Today, most of those barriers are gone.

The tools are cheap (or free). Inspiration is everywhere. And with AI-assisted coding and design, you no longer need deep technical expertise to experiment. The real bottleneck now isn’t technology or budget, it’s critical and creative thinking. Knowing what’s worth exploring, what to question, and what story the data actually tells (and doesn’t).

To push myself in that direction, I’ll be sharing small vibe-coding projects where I deliberately don’t overthink or overbuild. The goal isn’t polish, it’s speed, curiosity, and learning. Build something, put it out there, see if it resonates. If not, move on. If it does, there’s always room to expand.

What excites me most is that the playing field in music, books, and creative industries has never been flatter. Access to data is no longer the differentiator. Creativity and clarity of thought are.

So, what does that look like in practice?

Book Trails – Where Non-Fiction Leads

A visual exploration of how nonfiction books connect through shared readership on Storytel. Built quickly using Lovable, with publicly available data, and focused on storytelling rather than dashboards. Inspired by The Pudding.

This project is about proving that engaging, editorial data experiences are no longer reserved for big teams, deep technical skills, or big budgets.

👉 Book Trails – Where Non-Fiction Leads

Spotify Charting Dashboard

A lightweight dashboard shell built on top of all Dutch Spotify charting data. Third-party tools charge serious fees for this, yet access to the underlying data has never been easier.

If you want to use analytics as a unique selling point toward artists or creators, it has to be genuinely good. The playing field is more equal than ever, and that’s exactly what fuels competition; pushing everyone to build better, sharper, more creative tools.

👉 Spotify Charting Dashboard

(This one is already tempting me to expand it further.)

Thanks for reading. Many ideas, so more very soon.

Bo

If any of this resonates and you’d like help thinking through digital or data strategy, feel free to reach out by replying to this e-mail.

I’ve had this one sitting in drafts for a while, but with Popronde in full swing, now feels like the right time to share it.

It’s often assumed that playing live immediately translates into more streams and followers. But the relationship between live shows and online growth is less straightforward than many think. That’s exactly why I wanted to look into the impact Popronde has had on its yearly selected artists.

What is Popronde?

For international readers: Popronde is a traveling music festival in the Netherlands that takes over dozens of cities every fall. Emerging Dutch artists get to perform in pop venues, bars, bookstores, cafés, and even galleries. The idea is to expose developing talent to a bigger audience, and it has worked in the past. Alumni include Froukje, Racoon, De Staat, Dotan, Chef’Special, John Coffey, Blaudzun, The Vices, EUT, Rondé, MEAU, Haevn, and more.

How I Measured Impact

I scraped the Popronde website (2019–2024 editions), matched artists to Spotify, Chartmetric, and YouTube IDs, and pulled the following metrics:

• Spotify: monthly listeners, followers, popularity score

• Instagram: followers, engagement

• YouTube: views, subscribers

The goal: track where artists stood at selection, how they developed during Popronde, and what happened in the 1-2 year(s) after.

Median growth within Popronde period vs. year after participation

Why Spotify Listeners Matter Most

Many selected artists start from very small numbers. That means percentage growth can look huge, even when absolute numbers remain modest. Of all metrics, Spotify listeners turned out to be the most consistent benchmark, because it exists for nearly every artist, unlike YouTube or Instagram, where many had no baseline at all. Please, make sure you have socials (or have them linked to Popronde’s website) before participating. 🤷‍♀️

What the Data Shows

To smooth out release spikes and playlist effects, I looked at median growth in Spotify monthly listeners (and IG/YT) across three moments:

• After participation (two-week average after the tour)

• One year later (two-month average)

• Two years later (two-month average, where available)

Here’s the pattern that emerges:

• 2020 (Covid year) → essentially no growth

• 2021 cohort → ~35–40% growth during Popronde, with the year after showing little additional lift

• 2022 cohort → ~40% growth during Popronde, followed by a strong ~60% increase the year after — the clearest case of Popronde creating longer-term momentum

• 2023 cohort → ~35–40% growth during Popronde, ~50% growth one year after

In other words: Popronde does help artists grow their audience, but the effect varies. For most cohorts, the real step change comes not during the tour itself, but in the year(s) that follow — provided artists keep releasing and building on the momentum.

Zooming In: Individual Artist Growth

The averages hide a wide spread. Some artists blow up, others barely move the needle. You can see the top 10 growers per year in the charts below, they tell the story better than any table.

Should Spotify Do More?

Here’s another angle: editorial support. If Popronde is supposed to be a launchpad, shouldn’t Spotify help amplify that?

Now, I’ll be honest, I’m not the biggest fan of New Music Friday. To me, it feels more like an industry-facing playlist than something regular listeners engage with. It blends every genre together, has a low threshold for inclusion, and rarely builds a long-term audience for emerging acts.

That said, it’s still one of the easiest entry points into Spotify editorial. So at the very least, you’d expect Popronde artists to get some visibility there.

Looking at New Music Friday Netherlands placements:

• 2019: 41% featured

• 2020: 35% featured

• 2021: 51% featured

• 2022: 46.5% featured

• 2023: 44.9% featured

• 2024: 39.2% featured

In other words: roughly half of Popronde artists don’t get playlisted at all.

If Popronde is already filtering and curating talent, then why not give these artists more dedicated editorial spotlight?

Conclusion

Popronde does move the needle, but not in a straightforward way. For most artists, the biggest growth in listeners happens after the tour, once they release new music and capitalise on the momentum.

It’s a reminder that live exposure and streaming growth don’t always run in sync — and that turning early buzz into lasting fanbases still takes strategy, time and not less important: $$$.

That's a wrap for this week! Let me know what you think by voting in the poll below. Catch you next time! 👋

For all the annoyed data-analysts out there that disagree with the discontinuation of the related artist endpoint in Spotify’s API - here’s a Python scraper that gets you the same results.

This one took way longer than I expected.

Me and ChatGPT were vibe-coding our way through Dutch hip-hop collaborations when we hit a wall. I was chasing a slick interactive edge chord diagram, but it just wouldn’t land the way I pictured it.

Eventually, I had to admit the insights were already there. The data was solid. Maybe it doesn’t have to be 110 percent pretty to be worth sharing. 😉

Rewinding for a sec.

Back in 2018, I wrote one of my first music blog posts on Medium about Dutch hip-hop. Wild to look back now, both at the tools I used and the way I thought about the scene. At the time, Dutch hip-hop was dominating Spotify in the Netherlands. That hasn’t changed completely, but the genre has evolved. It’s blending with pop, crossing into other spaces, and feeling more fragmented and fluid than before.

I wanted to reconnect. So I pulled together a dataset to sketch the shape of the scene today. Who’s releasing? Who’s collaborating? And how are those connections shaped by popularity?

🔍 Starting from the ground up

I began with a scraped list of Dutch hip-hop tracks released in 2024, pulled from a Glenn McDonald playlist aggregator. The goal was to avoid editorial bias, no playlist push, no algorithmic boost, just a raw view of who’s putting music out.

From there, I grabbed each artist’s discography from the past two years. For every main and featured artist, I assigned a Spotify popularity segment (where available):

🟡 Very Low
🟠 Low
🔵 Mid-Low
🟢 Mid
🔴 High
🟣 Very High

Then I looked at how often artists from one tier collaborated with others.

🔍 Who is working with who?

The chord diagram below shows how artists across different popularity tiers collaborate. Each arc represents a connection. The thicker the arc, the more collaborations between those groups.

Collaboration flows between popularity tiers in Dutch hip-hop. Each arc represents shared tracks between two segments. Thicker arcs mean more collaborations. Mid-tier artists connect widely, while top-tier artists mostly collaborate within their own tier.

Here’s what stood out:

🎯 Mid-tier artists = the scene’s backbone

They connect upward and downward, bridging the scene and driving the most collabs per artist.

💎 Top-tier artists are selective

Very High popularity artists mostly collab with peers — and barely touch the lower tiers. Only two collaborations crossed from “Very High” to “Very Low.”

🔁 Emerging artists stick together

Low and Very Low artists mostly collaborate horizontally. These tight webs often stay within their own echo chambers — local scenes, subgenres, maybe shared friend groups.

📈 Who leads and who features?

Next, I looked at the share of releases where each artist appeared as a featured act rather than the main artist. The horizontal axis shows the percentage of their releases where they were featured. The vertical axis shows the total number of songs they released across the two-year period.

per artist segment, the % of featured artist visualised vs. number of releases

Key patterns:

🟡 Very Low: Self-released and starting out

216 artists | Median 16.7% featured

The largest segment in the dataset. Most artists here are early in their careers, focused on releasing music under their own names. Feature rates are low, but not negligible — there’s a quiet network of peer-to-peer collaboration happening below the radar.

🟠 Low: Still solo-heavy, but more mixed

177 artists | Median 14.3% featured

Despite being relatively large, this group shows the lowest median feature rate. Most artists are still steering their own projects. That said, a few highly collaborative outliers lift the average — signs of artists testing collab strategies to grow reach.

🔵 Mid-Low: Quietly collaborative

152 artists | Median 17.8% featured

This group starts leaning more into collaboration. While still primarily solo-driven, feature rates tick up and the density of mid-volume collaborators grows. Artists here seem to be working their way out of the echo chamber — often leading, but not exclusively.

🟢 Mid: The engine of the scene

111 artists | Median 18.2% featured

With the second-highest population and consistently high release volume, this group is the backbone of Dutch hip-hop. While most artists lead their own tracks, even a ~18% feature rate across this group translates into a lot of collaboration. They’re the network builders.

🔴 High: Collaboration as strategy

105 artists | Median 25.0% featured

This is where strategy really splits. Some artists maintain full solo control, while others float above 40–50% feature rates. With a higher feature % and sizable cluster, this group contributes to a large share of total scene collaborations.

🟣 Very High: Selective, but not isolated

35 artists | Median 22.7% featured

Despite being the smallest segment, artists here collaborate more than expected. With a median feature rate over 22%, the top tier isn’t just commanding attention — they’re also showing up on others’ tracks. These collaborations are fewer in number, but likely higher in visibility and cross-audience impact.

🎥 The one that almost broke me

There was one last visual I really wanted to include. A full tangled web of every artist and every connection across popularity tiers. Zoomable, bundled, slick.

And honestly?
It looked really cool.

Here’s a preview:

Interactive edge bundling of connections between rappers (in & out coming)

But I got stuck. Not because the insight was missing. It was there. But I kept tweaking little things. Node order, edge curves, label clutter, all of it. A classic perfectionism spiral.

The truth is, the first two visuals already told the story. But I was too deep in the weeds, chasing a perfect version of something that was already working.

Lesson learned again.
Sometimes done is better than perfect. Especially when the insight is already right in front of you.

✌️ One last thing...

If I had more time (or, you know, label budget), I’d dig into how collaborations actually impact streams, do featured spots lead to measurable bumps? Maybe next time.

🔜 Next up: Popprijs trajectories

I pulled all artists from the Popprijs (a major music talent award in the Netherlands) over the past few years to see what happens after they’re in the spotlight.

Specifically, I’m tracking changes in:

• 🎧 Spotify monthly listeners

• 📸 Instagram following

• 📺 YouTube views

The big question:
Does Popprijs actually move the needle?

👨‍💻 A little code detour for the nerds

Getting back into the swing of things, I was mildly (read: deeply) annoyed to find out that Spotify has been quietly sunsetting some of its API endpoints — including the one for “Fans also like.”

Not great.
I’ve always believed that if you're building public-facing features on the backs of artists and creators, those data points should be open by default.

That said... there’s always a workaround. 😏

So, for those of you who want to pull “Fans also like” data anyway — here’s a little scraper that still gets the job done.

That's a wrap for this week! Let me know what you think by voting in the poll below. Catch you next time! 👋

Before we dive in, I just want to give a quick shoutout to anyone who's recently joined this newsletter or has been sticking around for a while (even through the quieter months). 😄

And to anyone who’s wondering what exactly I’m trying to do here, I think this meme pretty much sums it up:

Honestly though, with AI, it’s even more feasible than ever.

Why isn’t anyone talking about Audius?
Seriously, we love to throw around terms like direct-to-fan, superfans (ugh, that word again), and fairer deals for artists. Yet somehow, Audius barely gets any love.

So here’s why I think it deserves a bit more love:

• It’s helping to decentralize the music industry.

• Fans can support artists beyond just pressing play.

• It could be a goldmine for discovering forward-thinking artists who are tech-savvy and open to new models.

• And here’s a big one: platform governance. Unlike the popular DSPs where decisions just... happen, Audius lets token holders vote on changes. The power dynamics are different — and that’s kind of the whole point.

• They are giving the artist, fan, and music industry way more valuable data points

The more I poked around Audius, the more I started thinking: if I were doing A&R at an indie label, how could I use this data? Maybe to:

1. Get a better sense of who these so-called “superfans” actually are.

2. Spot artists in niche genres gaining traction and experimenting with new ways to build their careers.

Let’s dig in.

I pulled together a batch of 147 electronic artists who’ve made a mark on Audius — in other words, these are artists who’ve already shown up on the platform’s charts. You could expand this pool, but right now, we’re just trying to prove a concept. After all, 147 artists x (potentially) 100 supporters x (potentially) 100 other artists they support... that’s already a pretty solid discovery pool to explore.

To start, I wanted to answer two basic questions:

1. Who’s been getting the most $AUDIO support overall?

2. Who’s attracting the most distinct supporters (a.k.a., how many different people are backing them)?

Go interactive over here

Next up, I took those 147 electronic artists, grabbed up to 100 of their supporters each, and checked out which other top 100 artists (if they reached 100) those fans are also backing.

I’ve always loved this kind of overlap—it’s one of the clearest signs that if someone’s into Artist X, they’re probably into Artist Y too. And on Audius, that signal is even stronger because we're not just talking about streams or follows — these fans are actually putting $AUDIO behind the artists they care about.

Now imagine if you could track down specific supporters who are always early adopters, or even spot niche subgroups that keep showing up in different artist circles. That could be huge for music discovery.

Fans clustered by who they support — one dot per supporter, grouped by taste. Get interactive over here (takes a second to load).

The calculation behind this visualization measures the similarity between supporters by comparing the artists they each back, with closer supporters sharing more common artist preferences. This allows us to identify distinct subgroups of supporters who share similar tastes, and in the future, we could explore how early these groups are in supporting new or emerging artists.

Hold tight, we’re almost there—just one last thing to explore!

I also wanted to see if a network graph could give us more insights by visualizing two tiers of support. Here’s the breakdown:

🧬 1. Seed: Your 147 Electronic Artists
These are your “root nodes,” the starting point of the network.

🙌 2. Supporters who backed them
Only the supporters who backed these artists with ≥1 support. These become the “bridge nodes” that connect the artists.

🔁 3. The artists the supporters from the seed artists also backed
Now we get to see which artists share fan bases with the original group and what new scenes or sounds these fans are exploring.

This gives us a clearer picture of which artists are connected through their shared supporters and could help us spot emerging trends, crucial supporters or discover new artists.

artists with a lower # of connections were filtered out

• Black represents the seed artists (the starting point of the analysis)

• Green shows the first-tier supporters, those fans backing the seed artists

• Red represents the artists supported by the first-tier supporters, revealing further artist connections

I could’ve spent more time refining this, as a data visualization fan 😬, but the goal here is really to ship ideas and theories.

When certain supporters and artists appear together in the plot, it suggests that these fans are backing multiple artists within the same cluster, hinting at shared musical tastes or overlapping fan bases.

I realize Audius has a smaller user base compared to the bigger DSPs, but the way it's set up—decentralized and with so much public data that other platforms don’t have—opens up some really cool ways to discover new talent.

That's a wrap for this week! Let me know what you think by voting in the poll below. Catch you next time! 👋

I’ve been wanting to share more of these little scripts I’ve written—mainly because I’m all about making data (especially from public sources or content creators) easier to understand. Audius does a pretty solid job of this, but let’s be real… some of the other DSPs could definitely learn a thing or two. 😅

I know this might be more up the alley of the data nerds out there, but I also know this newsletter has a bit of a mixed crowd. So here’s a Python script that grabs genre chart data from Audius over different time periods. Enjoy!

Every few months, I like to check in on what’s going on in the Dutch Spotify charts — mostly just to keep myself sharp, since I’m not really deep in that world day-to-day anymore. So I dust off my script, run some fresh data, and revisit a few of the stats I’ve written about in earlier newsletters.

Lately, I’ve been feeling that itch again to dive into some bigger research-y stuff around the music industry. It took me a damn long time to get back into that mindset, so I figured it’d be a good idea to get myself back up to speed on what’s been happening. And while I was at it, I thought I’d quickly share a little update on the Dutch charts with you.

The age of songs on the chart

I’ve updated the graph below with data from the last quarter of 2024 and the first three months of 2025 (March is based on the average so far).

the average age of songs on the Dutch Spotify Top 200, per year & month

So far in 2025, things are looking pretty similar to early 2024 when it comes to how recent the music on the charts is. Tracks released in the same year are holding steady, while songs from between one and two years ago are up by about 5% compared to the first three months of 2024. Older repertoire (3+ years old) is down around 5% compared to the first quarter in 2024.

If we rewind a bit: when comparing 2024 to 2023, there was a small 2.1% bump in music that was + three years old, and a 1% increase in same-year releases.

Local share of Dutch music on the charts

If you remember from the last update, I called out the IFPI for their conclusions — slight understatement there. Looking only at the top 10 songs and claiming that local music dominates? Yeah, that doesn’t really add up.

Obviously, I’m working with data from just the Top 200, but even within that, you can clearly see differences depending on where you look: the Top 50, spots 51–100, and the bottom half.

The local share of Dutch music per chart category & year

That said, the first two months of 2025 (and the start of March, even though it’s not on the graph yet) are showing a higher share of local music on the charts compared to the same period in 2024.

Across the full Top 200, 33.2% of the chart so far in 2025 is made up of local music — that’s a +3.85% jump compared to the same period in 2024.

The biggest growth is seen in the 101–150 segment (+6.7%), followed by 151–200 (+3.7%), then the Top 50 (+2.9%), and a smaller bump in the 51-100 segment (+2.9%).

And just because... why not? 😉
Here’s a weekly breakdown of the local share across the full Top 200.

Share of local music on the Dutch Spotify Top 200 charts, per week

That’s it for this week — short but (hopefully) sweet.

If you loved it, hated it, or just skimmed for the graphs — drop a vote in the poll below.

Since we're working with genres and metadata, it's not going to be 100% foolproof, but we get pretty close. We're checking if a song is tagged with a Dutch genre, and if there's no genre metadata, we look at whether the ISRC starts with the country's ISO code, followed by a label check.

If you haven’t watched Doechii’s Tiny Desk performance yet… seriously, what are you waiting for? It’s a must-see. And if the title of this newsletter didn’t already give it away—I’m a fan of her Alligator Bites Don’t Heal album. It’s been on repeat.

I won’t pretend I was an early fan, but it’s undeniable—Doechii is having a lot of momentum. With her recent feature on ExtraL and the release of Anxiety, she’s reaching new audiences fast.

Doechii’s monthly listeners on Spotify

That got me thinking—so, naturally, I went down a data rabbit hole. I analyzed over 13,000 Doechii-related YouTube videos uploaded since January 1st to see what patterns would emerge. My goal? To answer two key questions:

1️⃣ What kinds of tags are people using on these videos? Are there recurring themes, and how are creators optimizing for SEO?
2️⃣ Can I connect the dots between commenters across multiple videos? Which uploads have the strongest links based on shared audiences?

Just a quick reminder—this newsletter is all about pushing the limits of publicly available data. Could I do more with full access to streaming and social insights? Absolutely. Would it change my approach? Probably.

The beauty of this method is that it isn’t just for Doechii. These deep dives can be applied to any artist, market, or genre.

Let’s get into it. 🚀

Clusters in Doechii’s YouTube Hashtag Network

Every artist leaves a digital footprint, and on YouTube, that footprint is shaped by hashtags and keywords. Analyzing these tags reveals how an artist is being talked about, what trends are forming, and where audience engagement is strongest.

tags used on Doechii-related YouTube uploads between Jan 1st and March 10th

For this edition, I analyzed hashtag data from over 13,000 YouTube videos mentioning Doechii, all uploaded between January 1st and last week. By tracking which tags appear together, I built a network graph—a map of keyword connections—revealing how different themes cluster around her name.

Here’s what stood out 👇

1️⃣ “Anxiety” – The Breakout Song Cluster

Doechii’s latest track, Anxiety, has quickly built its own keyword ecosystem. The song is linked to hashtags like:

👉 doechii anxiety, anxiety lyrics, anxiety reaction, somebody’s watching me, my anxiety, keep on trying me, sleepy hallow anxiety.

2️⃣ Doechii’s Core Music & Performance Cluster

These tags highlight which songs and performances are gaining traction:

👉 doechii, doechii denial is a river, doechii live, tiny desk, genius open mic, doechii tour.

This signals that fans are actively searching for her live performances.

3️⃣ Hip-Hop & Beat-Making Community

Doechii is making an impact among producers, with many creating beats in her style:

👉 doechii type beat, kendrick lamar type beat, jid type beat, boom bap, trap beat, freestyle beat.

This signals her influence in hip-hop production circles—an indicator that her sound is inspiring a creative wave.

4️⃣ Reaction & Commentary Cluster

Strong reaction video engagement often signals virality. These tags show that YouTube creators are jumping on her content for engagement:

👉 reaction video, voice coach reacts, doechii reaction, first reaction, tryna silence me and my anxiety.

5️⃣ TikTok & Viral Trends Cluster

TikTok remains a major driver of music discovery. If these tags cluster around a song, it’s a strong sign that it’s spreading beyond YouTube:

👉 fyp, tiktok dance, trending, viral, challenge song.

6️⃣ Doechii & Other Artists

These tags show how listeners categorize her—who they associate her with

👉 kendrick lamar, sza, nicki minaj, tyler the creator, doechii x jid type beat, doechii x doja cat type beat.

If certain names keep showing up, it suggests a shared fanbase and potential crossover appeal.

7️⃣ Doechii & K-Pop Crossovers

Doechii’s collaboration with BLACKPINK’s Jennie is making waves, and the hashtag data reflects that crossover:

👉 jennie, blackpink, kpop, jennie doechii reaction, jennie rap, kpop reaction.

With K-pop’s massive global reach, this could be a strategic expansion into new fanbases.

How Are Fans Connecting Videos?

Beyond analyzing hashtags, I also looked at YouTube comments to see which videos share the strongest audience overlap. By tracking users who commented on multiple videos, I wanted to uncover whether certain uploads are more interconnected than others.

To keep the visualization clean, I focused on the videos with the strongest relationships—but there’s more beneath the surface.

What YouTube uploads have a relationship with each other based on user comments?

Based on the clustering results and an analysis of the video tags, the main content type groups are:

1. Dance & Challenge Videos – Titles with "Who Is The Best?" and references to dance trends, challenges, and short-form content.

2. Fashion & Performance Clips – Videos related to runway shows, stylish appearances, and artistic expressions (e.g., Doechii at Schiaparelli PFW).

3. Music Videos & Live Performances – Official music videos and live music performances, including concert recordings.

4. Behind-the-Scenes & Reaction Content – Includes reactions, interviews, and behind-the-scenes footage related to artists.

5. Freestyle & Creative Sessions – Unstructured performances, such as "Swamp Session" or freestyle clips.

I appreciate you taking the time to read this—I know data isn’t always the most exciting thing to look at, but I do my best to make it as engaging as possible. If you enjoyed (or did not) this deep dive, let me know by voting in the poll below!

Until next time—where I’ll be breaking down Audius and how it tackles some of the biggest superfan concerns in the industry. 👋

Spotify might have started as a music streaming platform, but these days, it’s much more than that. Podcasts are claiming a bigger slice of listeners' time, showing up prominently in recommendations and the app’s interface. And with the late 2024 launch of audiobooks in the Netherlands, spoken-word content is making an even stronger push. Ultimately, it all competes for attention with Spotify’s original star—music.

Spotify’s daily podcast chart ranks the top 100 most-listened-to shows in the Netherlands, but there’s no way to look back at past rankings—only the current day is available. About a year ago, I wrote a script to scrape the charting data daily (let’s keep that between us, though let’s be honest they should open this up 😉).

Interestingly, while Spotify only shows the top 100, the HTML sneakily hides rankings to 200. A little Easter egg for data nerds like me.

Now, after a full year of tracking Dutch podcast charts, I figured—why keep this to myself? Let’s dig into the numbers and see what’s going on.

average chart ranking in the top 50 vs. the number of days spent in the top 50

I dove into every podcast that cracked Spotify’s top 50 charts in 2024 to see which shows held their ground the best. The big hitters—those spending the most days on the charts and racking up the best (i.e., lowest) average rankings—include: Aaf en Lies lossen het wel weer op, Maarten van Rossem, BROERS, Moordzaken, Geuze & Gorgels, Napleiten, and NRC Vandaag.

Ideally, a top-performing podcast hangs out on the chart for a long time while keeping its ranking as close to #1 as possible.

Top 30 podcasts based on top 50 inclusion and average ranking in the top 50

I was also curious to see which shows hold a steady high ranking in the top 50 versus those that move around more in the charts. Some podcasts manage to stick near the top without much fluctuation, while others have a bit more movement but still perform well overall.

The most consistent high-rankers? Maarten van Rossem, Aaf en Lies, BROERS, Geuze & Gorgels, and Moordzaken. These shows maintain a strong position with little variation, proving they’ve built a rock-solid listener base.

Others see more ups and downs, but that’s not necessarily a bad thing—some shows might spike due to viral moments, guest appearances, or trending topics before settling back into their usual range.

weekly average ranking for the top 20 shows

Division in genres

Every time I dive into this data, I’m blown away by how many shows have no genre tagged on Spotify. Seriously, it’s such a simple way to optimize metadata for SEO, yet so many skip it.

Even major players like NRC Vandaag don’t have category tags, which is wild. If your podcast ranks without metadata optimization, imagine what it could do with it.

Number of shows with given (first) genre tag in either top 50, 100 or >100

Regarding podcast charts, Culture dominates as the top genre, with the most shows appearing, followed by Personal Stories and True Crime. The rankings shift slightly depending on whether we’re looking at the top 50, top 100, or beyond—but that makes sense. Niche shows often perform well but don’t always break into the highest tiers, while the biggest ones hold down the top spots.

To get a better look at how different genres perform, I’ve mapped them on a scatter plot, comparing their average ranking with the number of days spent on the chart. Some genres consistently rank high, while others stick around longer despite fluctuating placements.

average rank vs. average days on the chart - per main genre and chart category

Release frequency

Success in podcasting isn’t one-size-fits-all. Maybe your goal is to build a tight-knit community rather than chase the top of the charts. But one thing stands out when analyzing all the shows that made it onto the Dutch Spotify podcast charts: Consistency is key!

The most frequently charting podcasts tend to release on a regular schedule, whether that’s daily news updates, weekly deep dives, or anything in between. The data shows that staying present keeps listeners engaged—and keeps your podcast visible.

average shows release per year / genre (2024) | charting shows only

Below is the release schedule from January 2023 onwards for the top 20 shows that spent the most time in Spotify’s top 50.

You can see the different strategies at play—some podcasts drop episodes daily, while others stick to a more spaced-out schedule. Regardless of frequency, the common thread is consistency. The shows that dominate the charts tend to maintain a reliable release rhythm, keeping listeners engaged week after week.

episode release frequency for top 20 shows

There’s a lot more that could be uncovered with this data—but hey, maybe I’ll dive back into it sometime. 😉

If you have any hypotheses that you’d like me to test with data (there has to be some publicly available data out there) feel free to shoot me any ideas!

Until the next one! 👋

I’ll admit, I haven’t been keeping a close eye on the Spotify charts lately. It’s not like I’ve been slacking off—just caught up analyzing other stuff. So, I figured it’s about time for a quick refresher on where the Dutch Spotify Top 200 stands. And hey, why not share what I found with you too?

There’s been a lot of buzz online about the IFPI report and the whole (g)localisation trend. To be honest, it’s not exactly a new concept. What surprised me was how the IFPI, which is supposed to represent the global recording industry, based their conclusions about the dominance of local music on just the top 10 charting songs. You’d think they’d dig a bit deeper and analyze a bigger pool. After all, the long tail matters a lot in the bigger picture.

As much as I’d love to dive into the full landscape of Dutch music streaming, it’s just me flying solo here, so I’ve got to work with what’s available online. That means scraping the daily Spotify Top 200 to spot any trends for 2024. Hey, at least it's better than just looking at a top 10. ;-)

As usual, I tend to focus on a specific region or country, but feel free to try this out for any market you’re interested in!

Shining a Spotlight on Dutch Music’s Presence in the Charts

The average daily Dutch share on the charts, per month

I was pretty surprised to see a shift in the first half of 2024 compared to the last five years. As you can see it takes 6 months to catch up with the monthly average from 2023.

I've also broken it down by week to spot any significant spikes or drops in the share of Dutch music.

AVG weekly Dutch share on the Top 200 daily charts, per year

I’d love to dig into a bigger dataset, but even within the top 200, you can clearly see a difference between the top 50, 51-100, 101-150, and 151-200. It just proves my earlier point... a top 10 simply doesn’t cut it.

AVG monthly Dutch share on the Top 200 daily charts, per year and chart category

I also wanted to check if there’s any difference in the age of the songs during those first six months, and the visualization below makes it pretty clear.

Share of songs 1 year, 2-3 years or older on the daily Spotify charts

During the first five months, the share of songs older than three years was higher than in the previous five years.

Here are a few key takeaways:

• Shift in Dutch music trends: The first half of 2024 showed a notable change compared to the last five years, with Dutch music taking six months to catch up with the 2023 monthly average.

• Age of songs matters: The share of older songs (3+ years) increased in the first five months of 2024, bucking the trend of recent years.

• Top 200 reveals more: Even within the Top 200, there’s a clear distinction between different segments (Top 50, 51-100, etc.), further emphasizing that just looking at a Top 10 isn’t enough to understand the bigger picture.

What’s your theory on why these changes are happening?

Since we're working with genres and metadata, it's not going to be 100% foolproof, but we get pretty close. We're checking if a song is tagged with a Dutch genre, and if there's no genre metadata, we look at whether the ISRC starts with the country's ISO code, followed by a label check.

Greetings from my new (and slightly chaotic) corner of the world! It's been a hot minute since my last newsletter landed in your inbox, and for that, I owe you both an explanation and an apology. The whirlwind of settling into a fresh apartment, coupled with the allure of the latest web3 ventures in the hidden corners of X, and the usual hustle of day-to-day work, have kept me from diving into the research questions. I have a sneaking suspicion you haven't had a moment to miss me—am I right? 😉

Alright, let’s keep it real for a sec—Spotify (& any other DSP for that matter…) really needs to step up its game when it comes to helping artists connect with their fans. I mean, you put in the work, the endless hours, and the passion to build your tribe on their platform, and what do you get? A big ol' roadblock when it’s time to actually chat with your followers.

Picture this: you've been grinding on Spotify, building your listener base for ages, but when push comes to shove, there’s no straightforward way to reach out to them. This is why it's crucial to consider establishing your own customer data platform, enabling you to take the reins in connecting with your audience.

Currently, the closest thing to reaching out? Featuring an artist pick on your profile. But let’s be clear, it’s a shout into the void rather than a chat with your fans, because it’s not even aimed at just your followers. As for the artist pick, you’ve got a few choices on what to showcase:

• 📍 an album

• 📍 a song

• 📍 a playlist

• 📍 a link to merch

• 📍 a link to touring tickets

• 📍 a link to an upcoming release / countdown

Although many of these options have their own dedicated space on the artist page, they can be challenging for fans or listeners to locate.

Aware of this, my curiosity was piqued regarding how the top artists are leveraging this particular feature. Therefore, I examined the Spotify charts in The Netherlands, identifying all artists who had appeared in the Top 200 chart at any time, including those featured in any song on the chart (2024 so far). This process revealed 383 unique artists, encompassing both local and international talent, reflecting the chart's diverse nature.

For this cohort, I conducted a scrape of their artist pages to determine whether they 1) featured an artist pick at all and 2) if so, what type of content they chose for their artist pick on their profile.

overview of artist pick choice across 383 unique charting artists in NL

About 23.2% of the artists didn't bother with an artist pick at all. The majority (29.3%) leaned towards highlighting a playlist, with 23% pinning an album and another 21.4% showcasing a product with 2 or fewer songs. Surprisingly, almost no one in the group pinned links to their merch. Only Taylor Swift had an 'upcoming album' as her featured pin.

Y/N artist pick per charting artist categorised per ERA (determined by first release)

I wanted to check if more established artists use the artist pick feature to showcase playlists or albums, giving their catalog a little boost. Even though there are fewer artists on the charts from before the 2000s, it was good to see that many of them actually do highlight their music this way. Many of them direct listeners to playlists, a logical choice given their larger collection of music to share.

Y/N artist pick per monthly listener category

It seems like artists with fewer monthly listeners are the ones who often skip pinning something to their profile. The more monthly listeners an artist has, the more likely they are to have their artist pick in place.

While it's key to aim for direct connections with our fans, and we're all hoping Spotify rolls out more features to help with that, making use of the artist pick is a simple step every artist should consider. It's a no-lose move to showcase your work on your profile!

Remember, it's smart to guide listeners towards places where they might stick around longer, like a playlist instead of just one song. A common strategy is for artists to highlight their latest track but cleverly position it at the top of an artist-curated playlist with their artist pick.

👉 Just so you know, all the insights I've shared so far come from publicly available data. While there's a ton of questions out there that public data can answer, I'm reaching out through this newsletter to see if anyone's interested in a deeper dive. Whether it's about your music, label, book, or podcast, I'm open to collaboration.

🤷‍♀️ What do you get out of this? Some solid data analysis assistance, my take on things, plus the exposure from my newsletter, which is well-received in the music and publishing (books/podcasting) sectors.

🤗 And for me? I get the thrill of working with firsthand data, showcasing some real-life examples, which I find super exciting!

If you're keen to team up, just hit reply to this email. If not, no worries—keep an eye out for my next update. I've been tinkering with a YouTube shorts scraper (Shorts are being slept on…!), so I'll have some findings to share soon.

Cheers! 👋

I got myself a new house! Woohoo, something I've been working towards for a while now. You might be wondering why I'm bringing this up when we're supposed to be talking about content strategy... Well, you're absolutely right! But I wanted to take you on this journey with me, where I used Spotify chart data to jazz up the walls of my new place.

Anyone who knows me even a little knows I'm super into data storytelling, data art, and finding new ways to get people excited about data analysis. I've tackled similar projects in the past, like the ones over here, but this time, I wanted to set a fresh challenge for myself.

Could I use Spotify chart data in a way that looks awesome, reflects my passion, and doubles as decoration for my new pad? 🧑‍🎨

Alright, let's dive into the beginning!

To kick things off, I began by scraping the weekly Spotify charts in the Netherlands for the year 2023. Just so you know, you can actually do this for any country around the world, by the way.

From the top 100 tracks each week, I pulled out the following details:

• The song's name and the artists behind it

• Where it stood on the chart

• And I also snagged their corresponding single or album covers.

This led me to a clean dataset that I could use for the poster print.

This is where the creative juices started flowing, and I grabbed my trusty drawing board. I began by laying out each week in 2023 in a single horizontal row. Then, for every rank position on the chart, ranging from 1 to 100, I created a corresponding column. The end result? A grid that had the week's end date (Thursday) along the Y-axis and the chart ranking along the X-axis.

From that point, the real challenge was plotting 10,400 images. 🤯 This posed a few interesting challenges:

1. Space Management: How do you cram 10,400 images onto a poster file and still make them look good?

2. Image Quality: Achieving high-quality images with a high DPI (dots per inch) demands large and detailed image files. This leads to a massive design file.

3. Efficiency: The process of plotting all these images takes a substantial amount of time, around 30 minutes, using a program I wrote.

Handling these challenges was quite an adventure but you’ll be able to see the results below. 🧑‍🎨👩‍💻

Hello again!

It has been some time since my last newsletter. I've undergone a rebranding effort to place a greater emphasis on content and digital strategy supported by data, rather than have data take the forefront. The key insight I've had is that I'm doing this purely for enjoyment and to satisfy my curiosity, sharing this with whoever subscribes is just a nice bonus. 😌 This is the primary reason why I won't be committing to weekly newsletters but instead will share more spontaneous thoughts as they occur to me.

While my passion for the music industry remains strong, I'll also be dedicating some time to analyzing the publishing industry, which encompasses (audio) books and podcasting. If this doesn't interest you, please participate in the poll below. If you choose not to take action, you will continue to receive all content.

LISTEN TO MY ALBUM NOW!!!

Today, I'm excited to dive into why the old-school way of selling your music on short-form content platforms doesn't cut it anymore. It might sound like common sense, but I still see artists doing it all the time.

Now, let's talk about an artist who gets it – Tate Mcrae. She's the ultimate short-form content queen, effortlessly weaving TikTok into her album promotions. Her latest album, 'THINK LATER,' is a prime example of this seamless integration.

Tate Mcrae on Tiktok

have a look at her TikTok profile over here 👉

www.tiktok.com/@tatemcrae

So, where do you start?

To dig into this, I had to lay my hands on all of Tate's videos and check out how they were doing. While I couldn't access her account's analytics directly, I could scrape all her content and find out the play count, like count, share count, comment count, and save count. These stats provide a pretty good idea of;

1) Which content managed to reach a larger audience on the platform?

2) Which content generated more engagement, regardless of how many people it reached?

I managed to collect a total of 790 videos that were posted between 2019 and 2023. Given how TikTok constantly evolves and considering Tate's remarkable growth, I thought it best to analyze these years separately to ensure a fair comparison.

Posting rhythm

I first looked into the timing of when content is being posted and how frequently this is done.

posting frequency of Tate Mcrae in 2023

As you can observe, Tate increased her posting frequency approximately 18 weeks before launching her second album. In the weeks leading up to week 31, she typically averaged between 1 to 4 posts per week. However, starting from week 31 onwards, there is a noticeable uptick in posting frequency, with occasional peaks of up to 16 posts per week coinciding with new music releases.

This strategy underscores the significance of consistently engaging with the TikTok algorithm and rekindling the interest of your follower base in anticipation of a music release.

Engagement on TikTok posts

To create a comprehensive engagement metric that considers comments, likes, saves, and shares, I calculated the mean of each of these metrics per year. This approach takes into account the evolving TikTok algorithm and Tate's expanding reach over time. Next, I combined these four engagement values into a single metric and scaled (normalized) it to a range between 0 and 1.

It's easier to just hit the like button on a video, whereas sharing, commenting, or saving a post requires more effort. 👀

That's why I'm comparing these higher-effort forms of engagement to likes in my visualization below.

showing the engagement and like ratio on all videos posted in 2022 and 2023

“OUT (OR BUY) NOW” just doesn’t cut it anymore…

Today, my goal is to demonstrate, backed by data, that posting content with a strong sales call-to-action (CTA) may not necessarily result in high engagement.

The graph below illustrates that posts with higher placement in the upper right corner generally received greater engagement.

engagement and like ratio on videos posted in 2023

What I’ve observed is:

1. Duets or stitches typically receive below-average engagement.

2. Videos with a clear sales Call to Action (CTA) also tend to perform below average. For example, "woke up to being on the cover of Today's Top Hits" falls into the lower left corner, yet it had one of the highest play counts among all videos in 2023.

3. The worst-performing posts, when looking at engagement, were ads with Amazon….

4. Sales CTA related to touring seem to have a higher engagement than sales CTAs related to music.

5. REACH ≠ ENGAGEMENT. Some of the posts with the most plays ended up in the lower left quadrant. Think about what your goals are, engagement might be more valuable than reach…

So what did work?

Videos that occupy the upper right corner tend to be either;

1. Unveiling or teasing snippets of new music.

2. These videos prominently showcase Tate in the foreground as they reintroduce music in a way that resonates with the audience. While the music is at the forefront, there's no explicit sales call-to-action (CTA) included. Instead, these videos are skillfully edited, incorporating lyrics that connect with the audience and capture a specific mood or sentiment.

Curious to hear your opinion on what does and doesn’t work on TikTok, and how you’re measuring engagement. Feel free to let me know by replying or hitting me up on X.

👩‍🏫 I also enjoyed reading these articles lately:

👉The first collaboration between 2 music models - 3LAU AI x GrimesAI: https://bit.ly/48L5VA0

👉Anticipating the future of music and media: https://bit.ly/47DO7Wa 

👉Luminate year-end music report: https://bit.ly/3tUlKW4

👋 Hey folks,

I'm delighted to share another data tidbit with you, right before I embark on a plane to the Caribbean. No complaints from me! 🍹

Today, we're delving into some intriguing numbers that shed light on how different genders are shaping the music landscape, both as artists and Spotify listeners across various genres.

Now, when I mention "gender", I'm referring to two (limited) data points:

1. How listeners identify their gender when using Spotify (limited to she/he in the dataset I have).

2. The gender classification of artists, which can be she, he, or a mix of genders.

Let's dive right in and explore what's happening in the realm of music and gender!

Let's kick things off with this data:

In general, Spotify comprises 44.7% female listeners, yet only 22.9% of the streams come from female or mixed-gender artists.

💁‍♀️ Female listeners stream 32% of their music from female or mixed-gender artists.

🙋‍♂️ Male listeners stream 18.9% from female or mixed-gender artists.

💿 When listeners personally select their music (without playlists or algorithms), they stream 23.2% from female artists, which is a slight improvement of 0.3% compared to the overall listening pattern.

You may be curious about how I obtained access to this data. As previously mentioned in my updates, it's common for people to underestimate the sheer volume of available music data. One of the sources I frequently utilize is Every Noise, a platform continuously enriched with comprehensive data by Glenn McDonald, a data alchemist 🧙‍♂️ at Spotify. Today, I'll leverage some of this data.

The percentage of female listeners per genre

The percentage of female listeners within various top global Spotify genres, measured and ranked by streaming volume, is something I aimed to visualize below.

visualizes the % of female listeners for the top global genres on Spotify

The visualization above illustrates the genres that typically garner a higher female audience compared to those favored by men. It's not surprising to observe that genres such as trap, hip-hop, and rap tend to have a predominantly male following. Also, as expected, K-Pop stands out with a remarkable 70% or more female listeners.

My curiosity also led me to explore whether there are variations in audience gender distribution among European countries and their respective local hip-hop scenes.

visualizes the % of female listeners for European Hip-Hop

I found it intriguing that the Nordic countries have a larger proportion of female listeners, with Finland 🇫🇮 approaching a nearly equal 50/50 split.

Female listeners are one thing, but what about female artists?

Great question! That's why I delved into the percentage of female or mixed-gender artists within each genre and visualized:

1. On average, what proportion of female or mixed-gender artists men are listening to within a specific genre?

2. On average, what proportion of female or mixed-gender artists women are listening to within a particular genre?

what proportion of female or mixed-gender artists men/women are listening to within a specific genre

I conducted a similar analysis for European Hip-Hop, and the results revealed a significantly lower percentage of female artists compared to female listeners, as we discussed earlier.

We also see that women tend to listen to a higher proportion of female or mixed-gender artists in the Hip-Hop genre compared to men.

what proportion of female or mixed-gender artists men/women are listening to within European Hip-Hop

One thing is unmistakable: There's a distinction between women listening to a particular genre and Spotify users listening to female artists.

With this awareness, it becomes evident that we need greater representation of female artists across numerous genres and must explore additional avenues to ensure that the talented women we do have receive the visibility and recognition they deserve.

That’s a wrap, folks! Thanks once again for tuning in, and I hope this update sparks your curiosity and prompts more questions. Feel free to join the discussion with me on X at @plan_bo.

Kindly note that this data undergoes daily updates, and there may be slight day-to-day variations.

Hey there! 👋

Let's Talk Summer Anthems!

It's been a while, but I'm gearing up to pick up the pace in September as I'll have more time on my hands. Luckily, I've got a bunch of data-driven hypotheses to explore and discuss with all of you.

I'd like to extend a welcome to all the new subscribers. My motivation for delving into data insights within the music industry is due to the widespread underestimation of valuable information at our disposal.

By harnessing this data, fostering curiosity, and employing critical thinking, you can attain a notable advantage over typical music industry executives – a primary objective of the Data Bites newsletter.

Most of these updates have an international scope, but you can use the same method for markets you’re interested in if we do focus on a specific one.

If you're new here, you might have missed some of my earlier newsletters, accessible right here.

Now, let's jump right into the Summer topic... 🏊‍♀️

Recently, I stumbled upon an Instagram Reel video where people were asked about their 2023 Summer anthems. This got me thinking: Could I use data exploration to figure this out, visualize the outcomes, and then share them here with you all? Plus, I'm planning a late Summer vacation at the end of September, so these insights could help me put together my own Summer playlist. 😌

In previous updates, we chatted about the significance of local consumption trends. With Summer hits, this factor comes into play even more. While there are global or internationally loved Summer hits, there are also songs that become associated with Summer in specific markets. For this update, my focus will be on The Netherlands. However, this approach can apply to any market or playlist keywords, for that matter.

Exploring Spotify's Platform to Uncover Summer Playlists

To jumpstart this investigation, I tapped into Spotify's API to hunt down users who are crafting their Summer playlists.

As a starting point, I queried Spotify's API to locate users with active playlists that feature the keyword 'zomer,' which translates to Summer in Dutch. This initial step led me to discover a total of 1,007 user-generated playlists.

With this playlist collection in hand, I delved into the contents of these playlists to decipher the musical choices people have been including in their personalized Summer selections. This approach granted me valuable insight into the trending Summer songs of the year, offering a proactive glimpse into the tracks that individuals themselves have chosen to add to their playlists.

The dataset encompassed a grand total of 69,000 songs.

Summer 2023's Hottest Adds

First, I went through all the songs that were added to Summer playlists in 2023. This left me with 38,161 songs out of the initial dataset.

Then, I did some math to figure out what percentage of these playlists each song appeared in. Basically, the higher the percentage, the more it showed up on people's Summer playlists.

Not surprisingly, Peggy Gou takes the lead in this aspect. Her song "(It Goes Like) Nanana" made its way into an impressive 31% of all the Summer playlists, a level of success that truly merits its own newsletter update...🤔

Top 10 added songs to Summer playlists in 2023

After Peggy Gou, some of the other hot favorites were "Relax My Eyes," "Pornstar Martini," "Zin In De Zomer Man," and "Sprinter."

I've added a total of 60 songs to this playlist. These additions are categorized into batches of the top ten for each release year, spanning from 2018 to 2023. The ordering within each batch is determined by the number of times a song was added, with the most frequently added song being labeled as #1, and so forth up to #10. The batches are then arranged in a descending manner from #1 to #10, while the years are positioned from 2023 down to 2018.

Determining the release year of songs being added

Something I was even more interested in: When folks put together their Summer playlists, how many of those songs are actually from recent years?

I took a look at the percentages for each release year of all the additions made in 2023, out of the total.

percentage out of all songs added by release year

Among the 38,161 tracks that were added in 2023, 56% were released in the past five years. The remaining songs added in 2023 had been released prior to this timeframe. This observation underscores the enduring importance of the 'catalogue' of older music and reinforces my stance against rigidly categorizing it.

Seasonality, such as the Summer period, presents a prime opportunity for people to rediscover music.

The balance between local and international songs added

Next, I examined the artists of those 38K+ songs, resulting in 13554 distinct artists. For each artist, I gathered their genres and checked for presence in Spotify's designated Dutch genres. If found, I marked them as 'local'; otherwise, they were considered 'international'. There was a minor fraction of artists who didn't have any assigned genres.

split between local and international music per release year

Regarding songs released within the past five years and included in the selection of Summer playlists, an average of 53% were of Dutch origin. Among all the songs added and released in 2023, 55% constituted locally released tracks.

Enhance your Dutch Summer playlist for the upcoming year with these keywords

Be sure to begin early, ensuring your timely indexing in search results... 😉

most used words in Summer playlist titles

That’s all folks! Thanks again for tuning in and I hope this update sparks your curiosity and prompts further questions. Feel free to engage in further discussion with me on X at @plan_bo.

Hey there,

In this update, we delve into the fascinating world of TikTok and YouTube.

As people use TikTok, many of them also turn to YouTube for two main reasons:

1. Catchy Tunes: Enjoying those addictive tunes and finding the songs they stumbled upon while scrolling through TikTok.

2. Nostalgic Compilations: YouTube is a treasure trove of TikTok videos thoughtfully compiled into delightful compilations, letting users relive their favorite moments repeatedly.

Understanding the connection between these platforms can empower content creators and marketers to leverage TikTok's viral music trends and optimize content discovery. 📈

In this data-bites 🍘 update, I've gathered insights by analyzing the top 500 TikTok music-related videos on YouTube, as if I was in the United States. This search led to the discovery of 2,146 unique tags being used for these popular videos.

Keep in mind that each market is unique, so results may differ in other regions. 🌎

Now, let's explore the clusters associated with TikTok music on YouTube:

Use of hashtags when looking for TikTok music content on YouTube - download high-resolution image over here

1. TikTok Viral/Songs: This cluster includes tags like "tiktok viral," "tiktok songs," and "best tiktok songs." People often search for popular TikTok music, resulting in the frequent use of these tags together.

2. Audios/Sped Up: Sped-up songs have become a popular trend, leading to a cluster focused on such content. These tags are often combined with audio-related keywords, emphasizing edited audio on TikTok.

3. Chill: This cluster revolves around chill, morning, and acoustic music.

4. Mix & Mashup: This cluster features mashups of specific TikTok trends or compilations of the best sounds of a particular year, like "best sounds of 2022."

5. Geographical: The search query reveals regional clusters based on the US YouTube client, but similar analyses could be performed for other regions (and will probably do so…;-)).

6. Genre: The most prominent genre within the network is EDM/dance music, we’re also seeing sub-genres like phonk and drift music that have gained significant popularity in recent years.

💡 Utilizing this information can be advantageous for content creators and marketers:

By analyzing these clusters, you can identify content creation opportunities and optimize your YouTube upload strategy. Focusing on specific clusters or exploring different ones can help tailor your content to your target audience's interests, increasing engagement and success on YouTube. Don't forget to sync your YouTube and TikTok strategies to achieve maximum impact.

If you're curious and prefer more detailed visualizations of the individual tags, I have them available upon request.

Recommended TikTok music videos

Additionally, I explored YouTube's recommendations for TikTok music-related videos, resulting in a dataset of 2450 unique videos. These recommended videos showcased a more cohesive structure compared to our previous video tagging approach.

visualization of recommended videos when searching for TikTok Music on YouTube - download high-res version of image

Clusters of related videos mainly revolve around themes like TikTok Mashups, Sped Ups & Audio Edits, TikTok Music Videos, Artist and Song-Related Music Videos, Remixes/Party/Dance, and Lyrics. The analysis also revealed intriguing geographical clusters from regions like India, Indonesia, and Vietnam.

I hope this update sparks your curiosity and prompts further questions. Feel free to engage in further discussion with me on Twitter at @plan_bo.

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Let me start off by thanking everyone who has subscribed so far. 🤗

The reason why I started writing about data insights in the music industry is that many underestimate the vast amount of valuable information available for the taking. With the rise of AI tools, gathering and harnessing this data has become increasingly effortless, and this will become even easier. By levering this information, nurturing curiosity, and applying critical thinking, you can gain a significant edge over the average music industry executive. It’s my goal to showcase what data is out there for grabs and make data more accessible.

It is important to recognize and consider that every market is unique, characterized by its distinct local elements and consumption patterns. In a music landscape that is fragmented, it is crucial to understand that a "one-size-fits-all" approach cannot be applied worldwide.

As you may be aware, Spotify curates country-specific playlists featuring the best-performing hits in most countries. While the branding may differ from country to country, artists' ultimate aspiration is to secure a spot on these influential playlists. Considered a milestone in the journey of a hit song, these playlists serve as a culmination point, marking a significant achievement for artists.

As a side note, it's important to acknowledge that the playlist ecosystem extends far beyond this particular subset of playlists. However, for the purpose of our discussion today, we'll narrow our focus to these popular and influential Hot Hits playlists.

1. using data to determine the overlap percentage across hot hits curation

In order to visualize this I gathered curation data across 32 hits playlists to see what the overlap percentage is across any possible relation.

overlap percentage between Hot Hits playlists (tracks added >= 2022)

This highlights the evident diversity in curation across various hits playlists, with clear distinctions between regions and countries. Unsurprisingly, there is a higher percentage of overlap among playlists in Latin America, while Nordic markets also exhibit a substantial overlap. Additionally, the overlap percentage between Malaysia and Singapore exceeds 60%, showcasing the interconnectedness between these neighboring regions.

2. Get a better understanding of how quickly markets are catching up

In the realm of global playlists, there is one standout: Today's Top Hits. I found myself intrigued by the question of how many songs featured on this playlist also make their way onto country-specific hit lists. Moreover, I pondered whether these songs are swiftly embraced or if some countries lag behind in catching onto the international trends.

tracks added >= 2022

It becomes evident that when songs are featured on both Today's Top Hits and added to LatAm hits playlists, they often receive earlier inclusion in the latter. This observation aligns with the fact that the overlap predominantly consists of Latin American repertoire, which typically originates in the region before gradually expanding to other territories.

In addition, it is worth noting that the countries with the highest one-way overlap, where songs from Today's Top Hits are added to their respective country-specific playlists, include Canada, UK, and Australia. Moreover, a similar trend can be observed in the Nordic countries, where they are often grouped together in terms of playlist inclusion.

3. Get a better understanding of the individual Hot Hits playlists

In the previous visualizations, our focus was primarily on comparing the playlists among themselves. However, there are instances when we may desire to zoom in on a particular market and gain a deeper understanding of its unique trends and curation.

In order to gain deeper insights, another effective method is to create a plot that encompasses all the tracks added to a specific playlist between 2022 and the present. This plot can provide valuable information about the number of days it took for a song to be included in the playlist after its release. Additionally, it can highlight the duration for which a song remains on the playlist. By making a clear distinction between songs originating from within the country and those from abroad, we can further enhance our understanding of the playlist's composition and dynamics.

Hits Du Moment (France’s #1 hits playlist on Spotify)

Hits Du Moment (France’s #1 hits playlist on Spotify) - zoomed in

The above graphs show the curation of Hits Du Moment, France’s number-one hits playlist. Here we can tell that the median amount of days for songs to be added is 49 days and songs tend to stay on for 124 days. 62% of the tracks were coming from local artists.

In Italy, they curate their playlist by quickly adding songs but keeping them on for fewer days (108 days), with 61% of the added songs originating from Italy, maintaining an equivalent local-to-international ratio as France.

Hot Hits Italy (Italy’s #1 hits playlist on Spotify)

Hot Hits Italy (Italy’s #1 hits playlist on Spotify) - zoomed in

I hope this information has sparked your curiosity and prompted further questions. I would love to hear your thoughts on the topic. Feel free to reach out to me on Twitter at @plan_bo to engage in further discussion.

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This raises an important question: How often should we release music to keep up with today’s short attention spans?

Gone are the days of patiently waiting for years between album releases. We’re in the era of instant gratification, where listeners crave fresh tunes like never before. With the rise of streaming platforms and the constant buzz of social media, finding the right balance between quality and quantity has become an exciting challenge.

So, I got curious and decided to go on a bit of a research adventure. I first isolated a group of artists that hit the Dutch Spotify chart in either 2022 or 2023 and had at least 10 releases between 2020 and now. It’s a group of nearly 500 artists (both local and international).

Through meticulous data collection and analysis, I sought to uncover any patterns.

International artists appeared to have a tendency to release new music every 5–6 weeks. On the other hand, the release patterns for local music exhibited greater variability, with a prominent peak at around 8 weeks.

Building upon the initial analysis of release frequency, I delved deeper into the data, seeking to unravel differences across various music genres such as pop, hip-hop, EDM, and others.

As I dug into the data, an exciting discovery unfolded. It became clear that each genre — pop, hip hop, EDM, and others — had its own distinct approach when it came to release frequency. Artists within these musical realms employed diverse strategies, carefully planning their release timings to captivate audiences and make an impact. This revelation provided valuable insights into how artists in different genres navigate the ever-changing landscape of release frequencies to engage and enthrall their listeners.

To gain a deeper understanding of the relationship between release frequency and chart success, I focused on the top 30 artists in both the international and local music scene in The Netherlands. Analyzing their presence on the charts and considering the average weeks between their releases, I visualized the number of songs they had on the chart against their release frequency.

Upon examining the data, a distinct disparity became evident in the average number of weeks between releases for local hip-hop and pop artists. The analysis showcased a noticeable difference between these genres, highlighting how their respective release strategies diverge in terms of timing and frequency.

It is evident that each artist possesses their own unique vision and release strategy. The analysis showcased a range of approaches, demonstrating that there is no one-size-fits-all formula for release frequency in the music industry. The image below illustrates the diversity among artists, highlighting that every individual follows their own path when it comes to determining their release strategy.

While the data provides valuable insights into general trends and patterns, it raises an intriguing question: How do you determine your release strategies? Does your strategy align with these data-driven findings, or do you carve your own path based on your creative vision and intuition?

Exploring the decision-making processes of artists in relation to release strategies opens up a fascinating avenue for further exploration, revealing the delicate interplay between data-driven insights and individual artistic choices.