The use of social networking and digital music technologies generate a large amount of data exploitable by machine learning, and by taking a look at possible patterns and developments in these records, tools can help music industry experts to achieve insight into the performance of the industry. Home elevators listening figures, global sales, popularity levels and audience responses to advertising campaigns, can all enable a to create informed decisions in regards to the impact of the digitization on the music business. This can be achieved through the use of Business Intelligence assisted with machine learning.
Machine Learning is a branch of artificial intelligence, which gives computers the capacity to implement learning behaviour and change their behavioural pattern, when exposed to varying situations, without the use of explicit instructions. Machine learning applications recognise patterns because they emerge, and adjust themselves in response, to enhance their functionality.
The use of real-time data plays an essential role in effective Business Intelligence, which can be derived from all facets of business activities, such as for example production levels, sales and customer feedback. The info may be presented to business analysts via a dashboard, a visual interface which draws data from different information-gathering applications, in real time. Having access to the information almost soon after events have occurred, implies that businesses can react immediately to changing situations, by identifying potential problems before they have a chance to develop. By to be able to regularly access these records, organisations can monitor activities closely, providing immediate input on changes such as for example stock levels, sales figures and promotional activities, allowing them to make informed decisions and respond promptly.
Using Business Intelligence to monitor P2P file sharing can provide a detailed insight into both the amount and geographical distribution of illegal downloading, as well as giving the music industry with some vital insight into the actual listening habits of the music audience. By analysing patterns in data on downloads, music professionals can identify recurring trends and react to them accordingly, for example, by giving competitive services – streaming services like Spotify are now driving traffic from P2P filesharing, towards more monetizable routes.
Social networks can provide invaluable insight to the music industry, by giving direct input on fans’feedback and opinions. Automated sentiment analysis is a useful way of gaining insight into these unofficial opinions, as well as gauging which blogs and networks exert the most influence over readers. Data mined from social support systems is analysed employing a machine learning based application, which is trained to detect keywords, labelled as positive or negative. It is necessary to ensure that the technology can adapt and evolve to changing patterns in language usage, while requiring minimal quantity of supervision and human intervention michael blakey net worth. The amount of data will make manual monitoring an impossible task, so machine learning is therefore ideally suited. The use of transfer learning, for example, can enable something been trained in one domain to be used in another untrained domain, allowing it to maintain if you find an overlap or change in the expression of positive and negative emotion.
Following the available data is narrowed using machine learning based applications, music industry professionals may be given information regarding artist popularity, consumer behaviour, fan interactions and opinions. These details will then be properly used to create their marketing campaigns more targeted and efficient, helping in the discovery of emerging artists and trends, minimise damage from piracy and help to recognize the influential “superfans” in a variety of online communities.