We're at least two generations into the world of big data, where data points are generated by the millions and uses for them are multiplying exponentially, all the time. Data can be a powerful tool for understanding what's happening around us and making educated guesses about what's going to happen next. But it's only one type of information, and it will never completely unseat human intelligence and intuition as a likewise valuable tool for evaluating context. This is especially true when it comes to realms that are non-scientific, such as culture. Culture—meaning all the creative and decidedly human things we generate and exchange—is unpredictable and irrational, and that's a large part of what makes it so interesting.
Every day, people around the world are listening to music using dozens of platforms. And that generates big sets of data that can provide some level of insight into what's trending and what is meaningful. With something as subjective and amorphous as music, though, the cultural knowledge and intuition of humans is essential to making sense of the data. The key is to connect the quantitative (the data) with the qualitative (the human insights that contextualize it). Using those two analytical perspectives in tandem, it's possible to make sense of a mountain of information—combining, sorting, and analyzing it to discover where tastes, trends, and creativity are headed.
We're calling this music intelligence. The term refers to collecting and analyzing music consumption data and looking for patterns and also diversions from patterns, and then interpreting that information using human knowledge and learned intuition. This approach creates countless opportunities for companies that work in or partner with cultural enterprises of all kinds, including music.
Distinguishing a blip from a pattern
Take the quirk from late 2022 where Lady Gaga's "Bloody Mary" (off 2011's Born This Way) saw an abrupt spike in traffic. What was going on? The wildly popular Netflix series Wednesday had featured a very memorable scene where the titular character performed a dance right up there with Napoleon Dynamite's most indelible sequence in terms of wonderful weirdness. TikTok noticed. TikTok could not resist the temptation to meme it to infinity. But instead of setting the memes to The Cramps' "Goo Goo Muck," which Wednesday danced to in the episode, the world of TikTok landed on "Bloody Mary." The platform is known for being an incubator where ideas get melted down, stirred together, and spat out as something new. But it takes human understanding to follow the data up the chain, find its apex, and contextualize a phenomenon so particular to its moment.
Viral trends on social media, like that one, often drive surges in catalog listening habits, and music curation projects need to examine those trends in order to understand what is happening and why. Then they can use that information to create experiences that are relevant and compelling to listeners. They can also assist owners of vast swaths of user-generated music in identifying the value in their portfolios in ways that are meaningful and even predictive. And the marketing departments of streaming platforms need data to identify and engage with highly relevant, on-brand emerging talent.
Doing this work effectively requires designing systems that strategically intertwine human expertise with the data, each providing checks and balances on the other. The first step is analysis of the data points, including their sources. For example, an artist or track surging on TikTok is an entirely different phenomenon than one surging on a traditional DSP. The music on TikTok is often not the centerpiece of the content, and while a spike on that platform can sometimes lead to lasting success, it’s frequently ephemeral. To get a sense of what direction a trend is headed, that signal needs to be analyzed alongside ones from platforms where music is the focus.
With understanding of the significance of relevant data signals in place, it's possible to construct a simple algorithm that establishes baseline criteria around artist performance across multiple platforms and then weights those signals appropriately. This algorithm can sift a pool of artist candidates to see which of them are likely gaining serious traction, versus enjoying a viral flash. Literally millions of artists (and AI bots) are looking for their big break at any moment, but only a fraction have the skills and timing to earn it.
Human intelligence re-enters the process at this point. Metrics measuring engagement (the number of people listening) and velocity (how quickly that number is growing) are invaluable, but in isolation they can be misleading. That's where highly specialized music experts spanning genres, scenes, and territories lend the big-picture context that's crucial to identifying what's actually happening. This team can include taste makers, DJs, writers, and people who are themselves musicians, past or present. They can discern the difference between an emerging act being signed to a buzzy label and a sound or genre entering the actual zeitgeist, making it more likely for adjacent artists to gain a broader audience. With the list of relevant emerging talent now sifted again, the remaining pool can still be large—as many as 1,000 artists.
To further winnow it down, data and human intelligence need to operate in tandem again. An algorithm that looks at the variance in the performance metrics between the remaining artists can produce a simple weighted score that accounts for those signals. The above visualization is an example of a Third Bridge Creative tool that presents a score to allow a subject matter expert to quickly orient around priority artists. This score enables the expert to provide the final—and crucial—layer: actually listening to the artists and evaluating their music and brand. This is perhaps the most important step, because regardless of what the data indicates, an artist is not going to be popular if their sound isn’t compelling.
Though the process laid out above is oriented around identifying emerging artists, music intelligence isn’t a single product or service. It's flexible and modular, a highly customizable approach to strategic content development and decision-making. The insights can identify trends in catalog music or help streaming platforms prioritize new releases. Marketing teams can also use the information to identify trends within the music world so they can make key alignments.
In the current world of music consumption, where more than 7 billion tracks are streamed every day, it's impossible to keep track of what's going on without the benefit of data. But to use that data effectively and figure out how to anticipate which 7 billion tracks will be queued up tomorrow and also next month, human intelligence is equally essential, and this is where the music intelligence approach produces results that either method can't achieve alone.