Alison Lamb: Smart Data - Labels, Artists, Brands & Fans
27 Feb 2015
Ahead of MusicTank’s upcoming March 10th event ‘Moneyballing Music: Big Data, Consumers And A&R’ this blog looks to explore the varying roles that big data plays in the music industry. Big data often refers to ‘the very large and highly complex data sets thrown off by global online consumer activity, particularly arising from social media activity such as on Twitter, Facebook, YouTube or from page-views of sits such as Wikipedia’ (1).
In these days of tight budgets, lowering sales and remuneration for labels and artists, big data is becoming and will continue to be, ever more crucial and relied upon. The music business has already taken to working with big data in order to make sense of the pace, size and unpredictability that is today’s industry to become as succinct, effective, and successful as possible.
With the recent announcement that streams are being taken into account in the album chart from the beginning of March (2), big data is becoming a fundamental aspect for the future of the music industry.
Big data and analytics are crucial in the streaming world from a service point of view; similarly it is also crucial in the development of music recommendations and discovery – a weakness that all platforms are still facing.
From an artist and label point of view, big data from streaming is invaluable, as it provides a wide-ranging amount of information on fan demographics – from age, gender and location to isolated information on catalogue – what tracks fans are listening to the most to what they are skipping!
The upcoming MusicTank event is derived from Prithwijit Mukerji’s [Jeet’s] MA thesis entitled Moneyballing Music. As Jeet argues in his thesis, big data has the “power to more efficiently and effectively help record labels in spotting trends and understanding what, how, where and when changes are happening” (3).
Big data has become very competitive for streaming services within the past couple of years. Music analytics company, Next Big Sound, was recently voted the Number 1 music company by Fast Company, due to its ability of having predicted future success of artists such as Iggy Azalea and Macklemore & Ryan Lewis (4). Due to success such as this, the company was purchased by Spotify in 2013 in order to power their artist portal (5). The creation of the portal gave artists and manager’s access to data from streams, such as fan demographics and also payment calculations, something only previously available to labels.
Last year, Spotify also purchased The Echo Nest, a service that helps to understand relationships between songs and artists, as well as the habits and preferences of streaming users’ (6). At the time of acquisition, The Echo Nest said that it built the company to “fix how people were discovering music” (7), something that is becoming more and more of a bone of contention for streaming services. Along with the Next Big Sound acquisition, Spotify are in an extremely strong position, with the tools at their disposal to be able to move both the platform and the streaming market forwards in 2015.
Following in their footsteps, Apple announced at the beginning of 2015 that it had bought analytics service Musicmetric, a service which provides labels with the ability to track sales, streaming and social data. As Music Ally argued, Musicmetric’s analytics dashboard could slot neatly into Apple’s soon-to-be-launched streaming service (6). This was a crucial step for Apple to make, in order for them to, hopefully, launch a competitive streaming service.
In the case of streaming, big data is highlighting trends and shifts in fan ‘likes’ more dynamically than the old fashion way of having to directly ask fans their preferences. Although, there is also an argument that big data could have a strong role in the creation of hits – through data-driven Billboard charts derived from Twitter and Next Big Sound, there’s an argument that hits are more forcefully being created, rather than acting as prediction tools.
In the basic sense, big data has the ability to analyse the potential value of new products. Although a crude connection when talking about creativity, this is where big data has an undeniable role in informing the A&R process. In his thesis, Jeet explored how big data can be used to enhance and inform A&R decisions by considering existing consumer tastes. In all, big data in A&R can support how best and whether labels should invest in artists, to ultimately minimize risk – a crucial consideration for all types of label.
Early in 2014, it was announced that Lyor Cohen’s label, 300, had formed a partnership with Twitter. The deal allows 300 full access to Twitter’s music data, including information not available publicly, for example location tags highlighting where specific tweets were sent from (8). In exchange, 300 signs artists for recorded music and publishing plus, it was reported, support Twitter with organising data and develop software that could be used by other artists and labels.
In a similar deal, Warner partnered with Shazam. As a new way of A&R-ing, Warner gained access to data from Shazam about what songs people are listening to and get to identify break-through artists. An undeniably mutually beneficial partnership, it allows Shazam to provide Warner with a way of cutting through the noise and finding artists/ songs that have continual streams over a period of time.
Proof-positive in the power and concept of digital’s ‘long-tail’, Warner’s ability to sign artists to a Shazam-branded channel enabling artists to then loop back to Shazam, and through interpreting big data, to then be able to get their music marketed to those who have previously tagged their songs makes a powerful label- brand-artist proposition. In exchange, the artist is then be able to loop back to Shazam and through big data information, get their music marketed to people who had previously tagged their songs (9).
These examples show the value that big data has in the A&R process; it drills down on specifics of what consumers are listening to, interested in and searching for. As argued in Jeet’s thesis, data collected via these platforms is a lot more instant as the information reflects what is happening in the here and now (3). It makes a strong case for label signings, but requires labels to be able to work in a similar, almost immediate, pace with release strategies.
At the beginning of the year, Universal announced the creation of the Global Music Data Alliance, a partnership between them and Havas Media Group (10), The idea behind the initiative being that demographical big data can be used to connect brands with the most relevant bands who would be most appropriate to be involved in ads and other promotional campaigns.
In this case, big data about fan age, gender and geographical location, collected via social networks and streaming services will reassure brands that they are making sound investments and more likely to see positive responses and return. The launch of an alliance such as this echoes Jeet’s statement in his thesis, about how – crucially – big data has the ability to ‘understand shifts in consumer tastes more dynamically without having to directly ask the audience about their preferences’ (3).
Although still set to launch, an initiative such as this will likely be invaluable for both Universal and brands because, as Alex White CEO of Next Big Sound states, it should “lead us to a place where brands will feel more comfortable allocating more and more of their budget towards music” (11). This confidence would be undisputable in creating long lasting (and profitable) relationships between particular brands and bands, with data from streaming services having the potential to play a crucial role here. There’s also an opportunity for streaming services to use the data that they acquire to work with brands, which could lead to increased revenue for artists from their music being streamed.
The next step for the music industry is to see how the use of big data can be pushed forwards to tap into fandom. Big data has the ability to drill down into different levels of fans and work to capture attentions and get into their heads in the most effective ways as possible. Paul Crick’s recent article for the Telegraph (12), considered how sport is comparatively a lot further advanced in successfully making use of big data in terms of being able to identify and monitor the differing levels of fan in order to appeal to, and convert their fandom as much as possible.
For example, gig ticket sales data is second to none in informing just how much of a fan a band might have – from those hard-core artist fans who will buy tickets on pre-sale (via a mailing list sign-up) to those casual music fans who might buy an artist’s ticket a day or two before the event.
Fandom is a whole other blog post, but the power of the fan can make a huge difference to campaigns and ultimately, to an artist’s career – and the music industry has got a long way to go to fully make use of big data to move the industry forwards.
The possibilities are endless, especially from a financial point of view. As Crick alludes to, if a fan has bought a ticket for a concert, they should be automatically then targeted with travel and accommodation offers. In turn here, big data would be of use two-fold – targeting the most relevant fans, with the most appropriate brands to create appealing deals, which would lead to further spend.