Insight From Analyzing Big Data is Playing a Major Role in the Music Industry

For years, we have had the technology that enables us to choose what songs we listen to and when: CD, iPods, iPhones etc. Yet, we still listen to the good old radio. Why? Possibly because it seems to have an element of surprise.

This may also be why we love using new music apps such as Spotify, Songza, and Pandora. They enable us to listen to new music that we don’t have downloaded on our phones or computers, and they enable their users to discover new music, that we otherwise wouldn’t.

I find myself consistently wondering how they do it.  Not even the NSA could possibly know all of my musical preferences, and have them so readily available for streaming on each of my devices. Sure, they are able to somehow track my preferences and make recommendations, but I always wonder how they do this. I can’t even keep track of my own preferences!

Even if they could do all of this for just me, how is it possible they could do the same for any number of my friends? What about for the people that love Hip-Hop and Country (an interesting combination of preferences, but these people certainly exist)? What do they do when a teenager that was listening to Blink-182, ages and now has different preferences. It is rather difficult for me to comprehend how they can address all of these obstacles.  But they do. And even better,  they can do this for the entire world.

So who exactly is “they?” Is it Big Brother and a team of minions?

Fortunately, not.

“They” are actually data analytics.

How Data Analytics Provide Solutions to Problems in the Music Industry:

There are at a minimum, two major questions here. First, how do music listeners get accurate recommendations despite their ever-evolving tastes? The second is: how do these companies make wise decisions about what songs to offer and which artists to sign? Because ultimately, they are operating a business, and need to keep up the revenue streams.

So, how exactly do the listeners get accurate recommendations? First, data must be collected from various social media sources. But this is a lot of data; too much to work with. Therefore, it must be anatomized in order to get all the relevant information (artist, title, duration, genre, and so on). It is then matched to individual preferences, and finally, a recommendation algorithm is applied. The beauty of all this is: the process continues even after all of these steps are completed. As the listener reviews the recommendations, the app continues to collect feedback from the listener (e.g., the thumbs up/down feature in Spotify). Therefore, recommendations can be refined to keep up with the listener’s ever-evolving tastes.

And how do companies determine which songs to offer, and artists to sign? Again, through big data analytics. There are some companies, that provide niche services for the music industry to help answer this question by providing insight they gather from analyzing data.  By scouring social media sites, they gather loads of data, every day, every hour and every minute. They are able to use this data to assist with ranking the top artists and songs.

For record companies, the issue has always been betting on the right artists and genres. Imagine if Decca Records had signed the Beatles. Several companies turned down signing Buddy Guy, thinking he would never be a hit; he became the Blues guitar legend. Before big data, these bets were merely guesses. The guesses were non-representative of listener tastes, and personal choices. On occasion, the bets/guesses paid off  (as in The Rolling Stones), but more often than not, they did not (most of the one-hit wonders).

While people will likely always be involved in this process, big data makes comparing artists and genres much more efficient. Utilizing big data tools therefore results in targeted nurturing, fewer promotional expenses, better music,  and of course, greater profit.

Big Data is Even Helping Music Artists:

Finally, with regards to the musical acts themselves, there are various ways to cut out the costly middlemen. Lady Gaga has used social media and data mining to successfully understand her listener’s tastes. She was able to steer her Twitter followers to one of her business venture’s websites (littlemonsters.com). Here, they uploaded their choices for merchandise design, and simultaneously boosted sales by roughly 30%.

For those artists, just starting out in the industry, big data analysis is especially helpful. Typically, when just getting started, new artists cannot afford large publicity expenses. Now, they have an alternative. Their managers can now look into big data insights to help them figure out which actions will yield a greater audience lift. In some cases, they can lift audience numbers by as much as 50%. Such intelligence can help artists and their managers focus on scheduling these pivotal events.

Music is vital to my day. It softens the frustration of sitting in rush hour traffic, it lifts our spirits when we are down, and even brings strangers together in harmony. I am grateful for big data, as it helps me discover what makes my heart sing.