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The use of social network and digital music technologies generate a large amount of data exploitable by machine learning, and by looking at possible patterns and developments on this information, tools might help music industry experts to achieve insight into the performance of the profession. Information on listening figures, global sales, popularity levels and audience responses to advertising campaigns, can all enable the industry to produce informed decisions concerning the impact of the digitization around the music business. This can be achieved through the use of Business Intelligence assisted with machine learning.
Machine Learning is really a branch of artificial intelligence, giving computers the ability to implement learning behaviour and modify their behavioural pattern, when confronted with varying situations, without using explicit instructions. Machine learning applications recognise patterns because they emerge, and adjust themselves in response, to improve their functionality. Jason Aldean Just Gettin Started
The use of real-time data plays a huge role in effective Business Intelligence, which can 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, instantly. Having access to this information right away after events have occurred, means that businesses can react immediately to changing situations, by identifying potential issues before they have a chance to develop. By being in a position to regularly access this info, organisations are able to monitor activities closely, providing immediate input on changes for example stock levels, sales figures and promotional activities, letting them make informed decisions and respond promptly.
Using Business Intelligence to watch P2P file sharing provides a detailed insight into both volume and geographical distribution of illegal downloading, along with giving the music industry with a few vital insight into the particular listening habits from the music audience. By analysing patterns in data on downloads, music professionals can identify recurring trends and respond accordingly, for example, through providing competitive services - streaming services like Spotify are now driving traffic away from P2P filesharing, towards more monetizable routes.
Social networking sites can provide invaluable insight to the music industry, by giving direct input on fans' feedback and opinions. Automated sentiment analysis is a useful method of gaining understanding of these unofficial opinions, in addition to gauging which blogs and networks exert probably the most influence over readers. Data mined from social networks is analysed utilizing a machine learning based application, that is trained to detect keywords, labelled as negative or positive. 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 level of data would make manual monitoring a hopeless task, so machine learning thus remains ideally suited. The usage of transfer learning, for example, can enable something trained in one domain to use in another untrained domain, allowing it to keep up when there is an overlap or alternation in the expression of negative and positive emotion.
After the available data is narrowed using machine learning based applications, music business professionals can be supplied with information regarding artist popularity, consumer behaviour, fan interactions and opinions. This info can then be used to make their marketing campaigns more targeted and efficient, helping inside the discovery of emerging artists and trends, minimise damage from piracy which help to identify the influential "superfans" in several online communities.