Pandora and Spotify sparked a music revolution of sorts when they began convincing consumers that they did not need to own their music to enjoy it. Mobile analytics firm Flurry's CEO Simon Khalaf noted in a talk he gave at Source 14 that MP3 purchases were declining while streamed consumption was exploding.
As music streaming services explode, it's now a land grab to replace consumers' MP3 music collections, much of which was purchased from iTunes with streamed music subscriptions. It's also a land grab for the time that consumers spend listening to radio, and the radio advertising dollars spent to reach them.
Given the choice, consumers in general prefer free internet services. Spotify recently announced that 25% of its 40 million active users elected to pay $10 per month for its ad-free streamed music service. The other three quarters of Spotify's active users prefer free, ad-sponsored music. According to eMarketer, radio advertising is a $16 billion opportunity for internet disruption.
Moving to streamed music services can help radio advertisers improve ROI. Streamed music services on smartphones can expose these consumers to advertising during lucrative drive-time commuting periods. Streamed music gives advertisers transparency into where their ad dollars are going. Radio advertisers measure the effectiveness and return on investment of their ads based on surveys conducted by companies like Arbitron/Nielsen, Donovan Data Systems, and Interactive Media Systems, which only reach a small fraction of the listening audience and interpolate the number of people that listened to a radio broadcast. Streamed music has all the accountability characteristics of internet advertising. Rather than wait for a historical survey, streamed music services immediately know exactly how many people listened and, to varying degrees, some information about the people who listened based on user registration data and social networks. Songza and Spotify offer consumers the option to sign in with social networks, such as Facebook or Google Plus, providing a treasure trove of personal information about listeners that is very useful to advertisers in finding a much more accurate ROI on advertising.
It's also an opportunity for the music streaming companies to use predictive technology to determine consumer music preferences so lower-cost licensed music tracks can be substituted for more expensive tracks by the most popular performers. In his book, The Power of Habit, author Charles Duhigg reported that for more than a decade companies such as Polymorphic HMI have used artificial intelligence and statistics to predict the popularity of songs. Duhigg explained that using software named Hit Song Science, Polymorphic HMI was able to not just predict a song's success, but to recommend where in the radio lineup the song should be played to maximize its popularity. For example, the song "Hey Ya!" by the group Outcast was predicted to be successful based on the experience of music producers. But "Hey Ya!" didn't find an audience. Undaunted by the song's failure and confident in its future, producers asked Polymorphic HMI to analyze it. Polymorphic confirmed with its computer analysis that the song should succeed, but it was often played in the wrong sequence, conflicting with songs played earlier in the lineup. Playing "Hey Ya!" in the positions in the radio lineup recommended by Polymorphic HMI made the song a success.
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