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The evaluation of sonys track record as an innovative company

Messenger Fifteen years ago, Steve Jobs introduced the iPod.

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Since then, most music fans have understood this has radically changed how they listen to music. Less understood are the ways that raw information — accumulated via downloads, apps and online searches — is influencing not only what songs are marketed and sold, but which songs become hits. Decisions about how to market and sell music, to some extent, still hinge upon subjective assumptions about what sounds good to an executive, or which artists might be easier to market.

Increasingly, however, businesses are turning to big data and the analytics that can help turn this information into actions. Some estimate that today, humans generate more information in one minute than in every moment from the earliest historical record through 2000. Unsurprisingly, harnessing this data has shaped the music industry in radical new ways. When it was all about the charts In the 20th century, decisions about how to market and sell music were based upon assumptions about who would buy it or how they would hear it.

At times, purely subjective assumptions would guide major decisions.

  • When a user holds his phone toward a speaker playing a recording, he quickly learns what he is hearing;
  • Technological foresight is not enough Some would argue that technological know-how is a distinguishing feature of such firms.

Music charts have typically combined two pieces of information: Charts like the Billboard Hot 100 measure the exposure of a recording. In the 1920s through the 1950s, when record charts began to appear in Billboardthey were compiled from sales information provided by select shops where records were sold.

The number of times a recording played on the radio began to be incorporated into the charts in the 1950s. A 1962 cover from Billboard Music Week. For example, in the 1950s, artists started appearing on multiple charts presumed to be distinct. In the 1990s, chart information incorporated better data, with charts automatically being tracked via scans at record stores.

Once sales data began to be accumulated across all stores using Nielsen Soundscan, some larger assumptions about what people were listening to were challenged. Record charts are constantly evolving.

  • Star field in deep space from shutterstock;
  • Most companies employ some form of market segmentation;
  • Once sales data began to be accumulated across all stores using Nielsen Soundscan, some larger assumptions about what people were listening to were challenged;
  • No wonder so few saw the financial crisis coming.

Billboard magazine has the longest-running series of charts evaluating different genres and styles of music, and so it makes a good standard for comparison. Yet new technology has made this system a bit problematic.

The end of genre? Today, companies are trying to make decisions relying on as few assumptions as possible. Whereas in the past, the industry relied primarily on sales and how often a songs were played on the radio, they can now see what specific songs people are listening to, where they are hearing it and how they are consuming it.

On a daily basispeople generate 2.

The end of genre?

Obviously, not all of this data is useful to the music industry. But analytical software can utilize some of it to help the music industry understand the market.

The Musical Genomethe algorithm behind Pandora, sifts through 450 pieces of information about the sound of a recording.

For example, a song might feature the drums as being one of the loudest components of the sound, compared to other features of the recording. That measurement is a piece of data that can be incorporated into the larger model.

Pandora uses these data to help listeners find music that is similar in sound to what they have enjoyed in the past. This approach upends the 20th-century assumptions of genre.

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For example, a genre such as classic rock can become monolithic and exclusionary. With Pandora, the sound of a recording becomes much more influential.

  • Songwriters and distributors now know — more than ever — how people listen to music and which sounds they seem to prefer;
  • This key advantage is often critically important to develop high performance application systems for Automobile, Drone, Robotics of Industry automation, Human Interface, etc;
  • For example, a genre such as classic rock can become monolithic and exclusionary.

Meanwhile, Shazam began as an idea that turned sound into data. When a user holds his phone toward a speaker playing a recording, he quickly learns what he is hearing.

The music industry now can learn how many people, when they heard a particular song, wanted to know the name of the singer and artist. It gives real-time data that can shape decisions about how — and to whom — songs are marketed, using the preferences of the listeners. Companies like Music Intelligence Solutions, Inc.

The University of Antwerp in Belgium conducted a study on dance songs to create a model that had a 70 percent likelihood of predicting a hit. Even as new information becomes available, old models still help us organize that information.

In Facebook groups or on Twitter lists, some dedicated and like-minded fans are talking about the music they enjoy — and record companies want to listen. Streaming music services are increasingly focused upon how social media is intertwined with the listening experience. The Social 50 chart is derived from information gathered by the company Next Big Soundwhich is now owned by Pandora.

Songwriters and distributors now know — more than ever — how people listen to music and which sounds they seem to prefer. Or did it spread on these networks only because it possessed many of the traits of a successful record? Does taste even matter? But is your taste your own?