Digital music revolution – easiest way to listen to music

Pavel Prikhodko, Ph.D. Machine Learning

Every day millions of Americans download music over the Internet. With more than 70 authorized digital music services in the country, it is easier than ever to download and listen to music anywhere. In 2014, 56.34 million people downloaded music, and over 115 million of people used streaming services. In 2015 the numbers increased to 65.63 million downloaders and almost 130 million streaming users. The forecast suggests that in 2016 the total number of Americans who download music will be around 65.44 million, while the number of streaming users is expected to grow to 140.48 million people.

In 2017 the number of downloaders will be about 64.24 million, and streaming services will be used by 149.45 million people. According to the forecast, the number of downloading users would grow from 63.9 million in 2018 to 63.94 in 2019. Streaming users will increase to 162.99 million people in 2019 from 156.82 million people in 2018.

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Let’s break down American digital music users by gender and ages. In 2015, the number of males aged 16 to 24, was 20.03 million compared to 21.41 million female users at the same age. Among the 25-34 age group, 23.01 million of men and 21.78 women used digital music. The number of middle-aged (35-44) female users was 14.43 million, while there were 15.91 million middle-aged male users. In 2015, 10.17 million men and 9.36 million women aged 45-54 used digital music. In last place were people aged 55 and older: 5.6 million women and 7.08 million men.

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If we talk about salaries, the number of low-income digital music users in the United States was about 49.5 million. There were 76.1 million medium-income digital users. The number of high income users (defined as the top third of the personal income allocation) was 23.1 million last year.

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About Pavel Prikhodko

Pavel Prikhodko, Ph.D. Machine Learning

Pavel has worked for many years as a researcher and developer on a wide range of applications (varying from mechanics and manufacturing to social data, finance and advertising), building predictive systems and trying to find stories that data can tell.

In his free time, he enjoys being with his family.

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