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. Information on listening figures, global sales, popularity levels and audience responses to advertising campaigns, can all enable a to produce informed decisions about the impact of the digitization on the music business. This can be achieved through the usage of Business Intelligence assisted with machine learning.
Machine Learning is a branch of artificial intelligence, which provides computers the capacity to implement learning behaviour and change their behavioural pattern, when exposed to varying situations, without the usage of explicit instructions. Machine learning applications recognise patterns while they emerge, and adjust themselves in response, to improve their functionality.
The use of real-time data plays a significant role in effective Business Intelligence, which is often produced from all aspects of business activities, such as production levels, sales and customer feedback. The information 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 this information almost soon after events have occurred, implies that businesses can react immediately to changing situations, by identifying potential problems before they’ve a chance to develop. By to be able to regularly access these records, organisations are able to monitor activities closely, providing immediate input on changes such as 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, in addition to 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 answer them accordingly, for instance, by providing competitive services – streaming services like Spotify are now driving traffic away 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 really a useful method of gaining insight into these unofficial opinions, in addition to gauging which blogs and networks exert the most influence over readers. Data mined from social support systems is analysed using a machine learning based application, that will be trained to detect keywords, labelled as positive or negative michael blakey net worth. It’s necessary to make sure that the technology can adapt and evolve to changing patterns in language usage, while requiring the smallest amount of level of supervision and human intervention. The volume of data would make manual monitoring an impossible task, so machine learning is therefore ideally suited. The use of transfer learning, for instance, can enable a system been trained in one domain to be found in another untrained domain, allowing it to maintain if you find an overlap or change in the expression of positive and negative emotion.
After the available data is narrowed using machine learning based applications, music industry professionals may be supplied with information regarding artist popularity, consumer behaviour, fan interactions and opinions. This information may then be utilized to produce their marketing campaigns more targeted and efficient, helping in the discovery of emerging artists and trends, minimise damage from piracy and help to spot the influential “superfans” in various online communities.