The music industry is slowly pacing up its steps with the new rhythm orchestrated by artificial intelligence (AI). There is a magnanimous improvement provided by artificial intelligence in business insights, strategies and fine-tuning the way the music plays. In the music industry, emerging AI enabled tools are helping to revamp the way the audience perceives music content. One of the most effective marketing tools industry professional can utilise is consumer data which will deliver valuable insights through machine learning. The music industry is expected to become a $70 billion market by 2020, which can be bolstered by AI which shift conventional practices to more sustainable digital spheres.
The following are the four machine learning metrics that music industry professionals use:
Audience Engagement metrics
Engagement data offers insights into how the audience respond to new music genres, trends, artists and songs. This can deliver a number of collections, change in the followers and the number of plays per player, all calibrated by the number of saves or collection that include a specific song. Music industry professional can use actionable engagement data to attract visibility for their signed artist, thereby penetrating to more fans. Music labels can target audience and track patterns to make improved business decisions.
YouTube and Recommendation Engines
YouTube and recommendation engine improve matches between listeners and artists. Music industry professionals are already clutching the artificial intelligence technology that allows YouTube and recommendation engine to promote artist through raw engagement data from streaming platforms in the form of rate collections and even segmented by ZIP code.
Google Brain is the AI division which helps to improve its recommendation capabilities. The technique is called unsupervised learning, allows for more detailed, significant insights to the viewers. The technology identifies if varying the video length for specific platforms can help manage watch times.
The music industry can utilize the tool to target advertising length based on different platforms. Hundreds of micro changes were made in YouTube to increase the time spent by the viewer by 70 percent. This deep learning technology will accelerate the music industry forward, as companies learn how to advertise and market to high potential arenas based on streaming data.
Music streaming services uses filtered data to separate listeners into paying and non-paying subscribers. Mostly streaming services focus to create filtered data which helps them to market the songs and turn non-paying subscribers to paying ones. The insights from data are used to push the customer’s growth, driving fans to explore more artists. Industry players can use filters to better design their outreach strategies and content to drive competition. Data filtering advancements in other sectors may help the music industry advance the analysis of its own data.
Automated Marketing Tools
Artificially intelligent algorithms can help music professional to assess their competitors by examining the social media analytics. Targeting the music streaming habits of listeners help the experts to identify the trending professionals who are guaranteed to invest money.
Consider an example where marketers can break down the demographics, age and typical search patterns of Coldplay. By targeting the typical buying capacity of each artist’s listening demographic, they can correlate when, how and who to market the songs.
AI’s data-driven insights can enhance the music industry by connecting more with the audience. A sudden change from the conventional approach to digitalisation, artificial intelligence can power the production, search, delivery, and profitability in the digital music value chain.