Knowledge is power. Francis Bacon knew this 400 years ago. In business-to-consumer businesses, where knowledge of the customer base is a key asset, his dictum seems as prescient as ever.
Social media networks such as Facebook, Twitter, YouTube, and Instagram move massive amounts of data. Facebook alone has 1.35 billions monthly active users1, and is a goldmine for information on nearly any demographic in any corner of the developed world: user interests, activities, and listening and viewing habits are all mined in real time and leveraged for advertising sales.
Yet using and turning data into monetary gain may still be an underdeveloped craft in the music industry. Only a handful of players in the industry have figured out how to do this successfully and efficiently. Any understanding of the problem, naturally, has to begin with the data providers themselves.
Facebook and Twitter
Facebook, the largest player in social media, provides data in at least two important ways. On the one hand, it has the broadest view of general trends and interests. For example, it knows who is the most talked about artist in Miami or what current events are being followed in Cape Town, South Africa. But the devil is in the details, and it is the metrics on users’ engagement levels by age, gender, geographic location, and time of day that are really invaluable for advertisers. Such metrics point to who exactly is interacting with a Facebook embedded web page and in what manner. It is this particularity of Facebook’s data that allows a band to target different people with the same campaign and discover who is the better get: the 18-25 year old females in Philadelphia who have a higher engagement rate that any other demographic or the 25-35 year old females in the suburbs surrounding Philadelphia that may really purchase the music. For artists, this is bankable knowledge.
Twitter, of course, is the other social media platform with an invaluable data feed. It has recently announced, for example, a partnership with IBM: the blue chip company will mine Twitter using a new multi-variable pattern-dependent data model that addresses qualitative questions such as what is it that customers like best about an advertiser’s product and why is it that Brazil is its new trending market. Previously, this could only be guessed or inferred from sales data. Now, Twitter’s chatter can provide a rich tapestry of new information. And because people like to talk much more about music than, say, stovetop cleaning solutions, there will be better intelligence about where to tour, the best pairing of supporting talent, the right branding partnerships, and the geography of possible fan reactions to a band’s release. This is good for the music business and, by implication, for artists.
When looking for data on music and listening trends, labels and artists should go straight to the platform that consumers are using to listen to music. While iTunes and Amazon will never reveal customer analytics to labels, Spotify, which is partly owned by the labels, is much more obliging. Recently, Spotify teamed up with Next Big Sound to provide listener analytics for artists and their managers.2 Through Next Big Sound, artists can see their most popular songs, the age, gender, and location of their listeners, and how this correlates with data from other sources. If an artist can discover what kind of listeners are repeat listeners and not just casual fans, it narrows the target group for purposes of monetization.
Indeed, on a broader scale Spotify is a needed resource to understand and take advantage of daily trends in the industry. Two examples of possible lost opportunities, one by Aerosmith and the other by Spice Girls, should suffice to make the point.
Aerosmith’s song “I Don’t Want to Miss a Thing” spiked exceptionally in Spotify on November 13. This was at the same time that the robotic lander Phiale touched down on Comet 67P. It may have been a coincidence, but the storyline of the disaster movie Armageddon, where Aerosmith’s song was featured prominently, involved another comet hitting Earth. It is likely that Aerosmith fans and film audiences remembered the song in the movie. But a silence in the band’s social media assets suggests that no attention was given to this phenomena and that, ultimately, there was lost opportunity for the group to promote itself. In fact, Spotify data shows that every time a comet-related event happens, roughly twice a year in previous years, Aerosmith listening increases.3
As for Spice Girls, listening peaks in Spotify every Thursday. This, in all likelihood, is because the group is the most popular Throwback Thursday artist in the US. If listeners are more nostalgic about Spice Girls on Thursday than any other weekday, the group should know. There seems to be little evidence that traffic is higher at their website on that date, again suggesting a missed marketing opportunity.
In the above context, Taylor Swift’s recent action with Spotify may seem puzzling. Swift removed all her music from the streaming service because Spotify refused to make an exception of releasing her music worldwide but not in the US. Swift, a megastar, was bartering with the fact that streams in the US would have cannibalized album sales there. In effect, she traded listener data, and stream sales, for album sales (abroad, Swift is not as popular, which is why she had fewer misgivings about releasing streams). In the event, Swift became the top-selling US artist in 2014. The cost to Swift, though, is measurable. She was able to get nearly 1.3 million units in album sales in the first week, but she distanced herself from her fanbase in Spotify, including the 19 million customer playlists she commanded there and the analytics they provided. As the service allows artists to post tour schedules and merchandise on its artist page, arguably 19 million people were one click away from spending as much as $100 each on concert tickets; Taylor instead went for 1.3 million people spending $10 each on her album.
Taylor Swift does well anyway, but for younger bands understanding data is much more important. As an independent artist, there is often not a big enough budget to market to a mass audience, and even if there were there would be a risk of the music falling on deaf ears. For emerging talent, identifying their first audience type is invaluable. It certainly helps touring. If a band from Nashville sees that they have several repeat listeners in Milwaukee, they could plan a tour stop there. Previously, such bands never would have known about fans outside their immediate circle of influence: the equivalent analytics back then came from Soundscan and Broadcast Data Systems, which were purchased mostly by the major labels to serve their distinguished talent rosters, not unsigned performers (indie label generally felt that such data systems were too expensive for them).
Google Analytics and Smart URLs
Besides social media and Spotify, there are other ways for artists to understand their customers today.
Google Analytics provides copious data for free on web traffic. Artists can track time spent by fans on individual pages, their point of geographical origin, possible buyer conversion rates (people who are close to purchasing music but don’t commit), and bounce rates away from a page. Google Analytics is made to measure for a band selling their music and merchandise on their own e-commerce enabled website.
Smart urls can also be used to discover exactly who is clicking on links that an artist puts out and how fans are accessing these links. Smarturl.it provides information on geographic location, referring domains, and devices used as part of its free service.
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In the end, data is knowledge. And if knowledge is power, a claim that Francis Bacon could already make centuries ago, modern songwriting and performing musicians should be glad with the tools they have today. After all, fans of the same feather flock together, so identifying a neighborhood of supporters is surely more rewarding than blank-targeting an anonymous mass.
By Dan Servantes
2. Moody, Chris. “Changing the way business decisions are made”. October 29, 2014. Twitter Blog. https://blog.twitter.com/ibm
3. Williamson, Mark. “Spotify & Next Big Sound Artist Analytics”. November 2013. Spotify Artists. http://www.spotifyartists.com/spotify-next-big-sound-artist-analytics/