More than a decade a back, Steve Jobs launched iPod and we all saw music differently. It brought a radical change in the music industry and the way in which the audience perceived music.
The aspect which is less understood is the ability of this industry to generate huge amounts of raw data through apps, online searches, and downloads. This data can be used to change the way music is produced, advertised, and created for the new, emerging markets.
The decisions related to which music to market and how to do it still depends upon the assumptions made by leading industry players. This guesswork may often go wrong or constrict the wide-level capability of one artist and music type including all the other things. Utilizing analytics can empower the industry to harness the power of data and make the music transition from one phase to another without much hassle.
Spotify’s Publishing Analytics
Take Spotify, for example. In 2018, the company launched the beta version of its publishing analytics. This tool can provide daily analytics to publishing companies such as data regarding the songwriters, their success rate since the starting, etc. The program would give performance statistics which would allow songwriters to evaluate their playlists.
The major objective of this application is to reduce the gap between the marketing and sales of the companies. It helps Spotify evaluate the genres that are generating more revenue and ways of minimizing licensing costs of music albums. On top of everything, this tool allows identification of the songs that are likely to hit the top charts in one year. The subjective assumption can be completely eliminated from the industry.
Additional Benefits Of Big Data In Music Industry
As already discussed, big data has the power to predict the songs that will become popular in the near future. Shazam is an organization that checks the songs being played by users and utilizes this knowledge to predict successful songs. The prediction is a result of multiple recognitions along with ratings to the songs.
The researchers analyzed various top 10 songs from past albums or playlists to find a pattern. This pattern was later used to predict future top 10 songs. Luckily, this big data tool achieved nearly accurate results.
Conclusion
The motive behind using big data in the music industry is to understand the music industry and impart the highest level of customer experience. Big data, most certainly, makes this possible. It would be a waste of resources to not fully use the data collected through various sources by the music industry.
In many cases, music giants have observed that they have been advertising some type of music to a specific audience without knowing that they can advertise and market this playlist, album, or song to a much wider audience. But, without data analysis, there was no way of knowing how wide the audience base can be.
With the capability of using algorithms to predict data patterns, analyze music classification and evaluate data related to artists, the music industry can gain many opportunities. The truth is that guesswork is not required anymore. When we have the ability to predict future patterns through effective machines and big data capabilities, then why even take chances of wrong predictions.