In recent years, the music industry has seen significant transformations. The most common way to consume music is through streaming, and albums have given way to singles.
These streaming platforms, such as Spotify or Amazon Music, have large audiences and continue to grow year after year; their importance is such that in 2015, they outperformed hardware in terms of revenue for the first time in history, coinciding with the first year in two decades that the entire music industry grew in comparison to the previous year.
Big data provides the music industry with a leg up on what people are listening to, and it doesn't only tell them what they're listening to; it can also tell them where, when, and how many times they've listened to a song or a genre.
Using advanced big data analytics tools to monitor international music patterns and preferences, big data is also allowing the music industry to predict what the next music "trend" or "greatest hit" will be. There are many advantages of Big Data.
The music industry has been heavily commercialized, as much as underground communities, genre gatekeepers, music reviewers, and art fans would like it to change. The goal for commercial musicians is to create music that will appeal to a large audience while also making a profit. Artists can produce songs that are more likely to appeal to a specific audience when data is handled intelligently.
Benefits of Big Data in the Music Industry:
Big Data can help artists organize and plan their tours:
Many musicians need to tour in order to have a long-term career. New fans can be won over if an artist performs an intense live set and has a captivating presence on stage. Many booking agents use this strategy to seek opportunities for their young bands to open for larger, more established performers as supporting acts.
It's not easy to figure out when and where a band should tour. This is where big data can assist in drawing more informed decisions.
Big data can also aid technologies such as the Internet of Things (IoT), which refers to internet-connected devices. Among the numerous popular IoT gadgets, many people are familiar with Alexa, Amazon's virtual assistant. Speaking of Alexa, you could already have one at home.
Some social media-based technologies, for example, can tell you when and what people are discussing about a band, a performance, or a song. Analyzing that feedback could help determine which markets, locations, or demographics are the most profitable, as well as where the most fans exist.
Also Read | How IoT is used in Music Industry
The Revenue Model is Transforming:
The music industry's entire income model has changed in the recent decade. The music business has yet to precisely establish royalty rates for streaming music, despite the fact that streaming sites like Spotify have aided in the control of online piracy.
Big data has the potential to change this by allowing corporations and artists to collaborate more efficiently using high-speed data processing, such as the Hadoop cluster. Streaming site data provides firms with a wealth of information on the types and genres that their target market enjoys right now.
Despite a long-running discussion about "selling out," it appears that a data-driven method may be the last resort for getting musicians paid.
Keeping track of musical preferences:
Organizations in the business can monitor the heartbeat of the industry by using a real-time database of global music trends. Consumer tastes change all the time, therefore artists must constantly reinvent themselves to keep up with the latest trends.
Artists can customize their new records to match changing customer tastes, and record companies and promoters can adjust marketing efforts to schedule events and promotional activities around the current consumer needs, boosting their outcomes.
Big data analytics is transforming the way businesses engage with their target populations, and the music industry is set to follow suit in the near future.
The music industry may learn how to grow its digital marketing sector by utilizing social media platforms and new ad technology. They can make use of this area to promote new forms of collaborative marketing with larger brands. Red Bull, Urban Outfitters, and Nike are among those who have already signed on.
This means that record labels and musicians may follow the revenue-sharing model pioneered by social media platforms like Instagram.
Instagram's use as a marketing tool has skyrocketed in recent years. It is currently recognized as one of the largest interaction platforms. Brands are able to raise awareness, and artists are able to share their work through well-chosen advertising.
The music industry is following suit, and big data is playing an important part in the transition. It may not be long before businesses finance entire albums or even music videos. (Source)
Spotify, an industry leader in Online Music Streaming Services employs a combination of the following three recommendation algorithms:
Spotify's NLP model searches hundreds of articles, forums, blog entries, and discussions about an album or song on the internet after scanning the metadata (artist name, song title, etc.). The algorithm looks at how people describe the song and compares it to other songs that have been discussed in a similar way.
Spotify's machine learning algorithm regularly evaluates your actions toward certain tracks to determine what type of music you now enjoy.
For example, the algorithm considers the songs you've played repeatedly, added to your playlist, and so on. Then Spotify analyses your music tastes to those of other users, discovers individuals who have similar tastes, and suggests songs to you.
Convolutional Neural Networks (CNNs):
Spotify analyses raw audio data such as the song's BPM, musical key, volume, and other factors using a CNN-based model.
Spotify then searches for songs that have similar parameters and suggests them to you. This method has shown to be quite helpful in locating high-quality music that has yet to be discovered by the general public.
Also Read | How Spotify uses Machine Learning Models
What Impact Does Big Data Have on Music?
Big data can reveal information about a listener's motive — why do they listen to a certain artist? This allows the business to quickly notice trends and provide precise information about a certain segment of the population's musical DNA.
It also gives the music industry greater fan interaction tactics. Relationships between innovative brands and artists make it feasible. This could alleviate a lot of the problems that the music industry is having with music distribution right now. Most crucially, the use of a big data catalogue for streaming music may be able to solve the issue of artist pay. Many are asking for musicians to be paid based on the number of plays they receive, similar to pay-per-click commercials. (Reference)
The music industry is well aware that many people will not pay for music. Big data advancements may usher in a huge revolution, relieving consumers of the burden of fixing the music industry's antiquated business mechanisms.
After all, songs are only data. Over time, a massive "store" might be built from this massive accumulation of data. Artists can choose to share data earnings, giving them more control over the direction of their work.
Also Read | Machine Learning for Music Genere Classification
The music industry benefits from big data. As you can see, we're about to embark on a new and exciting era. To gain insights from Big Data, you don't need to be an expert in technology or computing. Big data isn't only for the privileged few.
It's time for you, as a business owner, to start using big data to better understand music customers. Big data is definitely worth investigating. Just keep in mind that, as consumer privacy concerns grow and legislation mandates how to handle acquired data, music business professionals who rely on it may face new hurdles in the future.