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November 12 2014

Business Intelligence in the Music Industry

The use of social networking and digital music technologies produce a large amount of data exploitable by machine learning, and also by looking at possible patterns and developments in this information, tools can help music industry experts to gain insight into the performance of the marketplace. Information on listening figures, global sales, popularity levels and audience responses to promotional initiatives, can all enable the industry to produce informed decisions about the impact of the digitization about the music business. This can be achieved by using Business Intelligence assisted with machine learning. Jason Aldean Just Getting Started

Machine Learning is really a branch of artificial intelligence, giving computers the ability to implement learning behaviour and change their behavioural pattern, when exposed to varying situations, without having to use explicit instructions. Machine learning applications recognise patterns since they emerge, and adjust themselves in reaction, to improve their functionality. Jason Aldean Just Gettin Started

Using real-time data plays a crucial role in effective Business Intelligence, which may be derived from all aspects of business activities, such as production levels, sales and comments from customers. The data can be given to business analysts by way of a dashboard, a visual interface which pulls data from different information-gathering applications, live. Having access to this information almost immediately after events have occurred, implies that businesses can react immediately to changing situations, by identifying potential issues before they have a possiblity to develop. By being able to regularly access this information, organisations are able to monitor activities closely, providing immediate input on changes including stock levels, sales figures and promotional activities, permitting them to make informed decisions and respond promptly.

Using Business Intelligence to monitor P2P file sharing can offer a detailed insight into both volume and geographical distribution of illegal downloading, in addition to giving the music industry with some vital insight into the actual listening habits with the music audience. By analysing patterns in data on downloads, music professionals can identify recurring trends and respond to them accordingly, for example, by providing competitive services - streaming services like Spotify have become driving traffic away from P2P filesharing, towards more monetizable routes.

Social networks can provide invaluable insight for the music industry, by giving direct input on fans' feedback and opinions. Automated sentiment analysis is really a useful method of gaining comprehension of these unofficial opinions, as well as gauging which blogs and networks exert probably the most influence over readers. Data mined from internet sites is analysed using a machine learning based application, which is trained to detect keywords, labelled as good or bad. It is necessary to ensure that the technology can adapt and evolve to changing patterns in language usage, while requiring the least amount of supervision and human intervention. The total number of data would make manual monitoring a hopeless task, so machine learning is therefore ideally suited. The use of transfer learning, for instance, can enable a system trained in one domain to be used in another untrained domain, letting it to keep up when there is an overlap or change in the expression of good and bad emotion.

After the available details are narrowed using machine learning based applications, music business professionals can be given information regarding artist popularity, consumer behaviour, fan interactions and opinions. These details can then be used to make their marketing campaigns more targeted and efficient, helping in the discovery of emerging artists and trends, minimise damage from piracy and help to identify the influential "superfans" in a variety of online communities.
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