The Role of Big Data Analytics in Understanding Music Trends
Unlock audience insights and boost your music success with big data analytics.

How can we predict the future? By studying the past. There is nothing new under the sun, the saying goes. In essence, there’s no such thing as a new trend. Every trend we see today has already happened in some way before. The implication is that there is a pattern of recurring trends that we can study and predict the next trends before they start. So to predict the future, we must look into the past. But how can we study the past accurately? Data.

The entire world generates data. Every decision you make and every piece of information you give away is recorded as data. What song do you play the most? What artist do you play the most? What genres do you play at a given time of the day? Everything you do online is recorded as data that can be used to predict the future. How can we analyze all this information? Big data analytics.

In this article, I’ll explore how big data analytics can be used to understand and predict music trends. How can the past possibly tell us what the future holds in music? Let’s find out.

Big Data Analytics

Big data analytics is exactly what it sounds like, data analytics done on a large scale. When you carry a massive amount of data, probably covering many years, big data analytics helps you find the insights you’re looking for. Big data analytics helps you to identify trends on a large scale.

For example, only jazz, rock, and classical music have had above 10% of sales as physical albums. This implies that apart from these three, the entire world has moved from physical sales to online streaming. Record labels can then focus more on online streaming and less on physical albums. That’s big data analytics in action.

On the flip side, one could zoom in on one user. Let’s call our user Chad. What genres does Chad enjoy listening to? How often does Chad listen to music? Does his choice of music have anything to do with the time of the day? These are all questions that will help us better understand Chad and how to curate recommended songs for him. That is not what big data analytics is about.

Understanding Trends Using Data

As indie artists, the big question remains. Should you completely ignore what the trends are and just make your music as you see fit or is it better to fit your sound with what’s more popular at the moment for more commercial success? If your answer is to pay some attention to what the popular trends are, then you must harness the power of data in understanding music trends.

When you can take music data from the last few decades and explore it properly, nothing will come as a surprise to you. For example, some people might be surprised at the fact that old music styles are being reintroduced in modern songs. If you’ve been studying the data, however, you would’ve seen it coming. You would already have been surfing past music styles to figure out which one you’d like to resurrect.

The key to unlocking the future lies in the past.

Applications of Data Analytics

Song Recommendations

I previously mentioned how song recommendations are not an application of big data analytics, but hear me out. Zooming in on Chad, our user from before, is just one side of the coin. Let’s assume that Chad loves RnB. The normal thing would be to keep recommending RnB songs to him, but it doesn’t stop there. The system would go back to its massive database of users to find everyone who loves RnB like Chad.

The next step is to find out what other genres those guys enjoy. So if the data says that most RnB lovers also enjoy hip-hop, Chad will eventually start getting some hip-hop songs in his lineup of songs. That’s an application of big data analytics.

A & R

Record labels have many departments, but the A & R department is arguably the most important. Once a record label stops bringing in the best talent on offer, its steady decline has begun. The best way to sign the next big artist is to predict which of the many talented artists has what the fans want.

Are the fans tired of commercial music and in need of a Billie Eilish or NF? Are the fans tired of sad music and in need of some love with a John Legend? The scouting department must be able to predict what the people will want and find an artist who meets all those criteria that they can work on. All that prediction relies on big data analytics.

Marketing

Using big data, you can make your marketing strategies as efficient as possible. Imagine offering free tickets to a hip-hop concert in a place where older people are more. How many people do you reckon would appreciate the offer? If you go to a college campus, however, you’re more likely to get a better reception. Big data analytics will help guide you to offer the right deals to the right people, thus making more sales.

Tour Planning

As an artist, what’s the best feeling when you’re performing on stage? In my opinion, it’s when the crowd knows the lyrics to all the songs you perform. I recently saw a clip from a Lewis Capaldi concert where his voice failed him and the crowd continued singing. The chills don’t get any colder than that.

So you want to tour in places where your music is appreciated. Which countries or states listen to you the most? Where do the people who buy your merch come from? Big data analytics will help you find the best locations to tour.

Final Thoughts

Big data analytics is a powerful tool that can set you apart from the competition in a way you’ve never experienced before. If you can understand and predict music trends, nothing is stopping you from being the next big thing.

Connor Price saw the pandemic and recognized an opportunity to grow his following using skits on Instagram. He saw the trend before anyone else. You can too.