The Taylor Swift Effect: Striking the Right Chord with Data
A lighthearted look at the importance of context in data-decision making. Post 1/3
A very poor ChatGPT rendering of Taylor + MJ + the Fab Four in performance!
Taylor Swift and Market Metrics
Taylor Swift is unarguably a worldwide phenomenon. Her just-concluded tour is estimated to have earned over $2 Billion.
She’s no less impactful in the US music market.
Last week’s article in the Wall Street Journal reporting on her market success was a masterclass in contextualizing metrics. Here’s the excerpt:
If Taylor Swift were her own genre, she’d be bigger than jazz.
The pop-music sensation accounts for nearly 2% of the U.S. music market, according to data-tracker Luminate—more than any other artist. Even in today’s splintered media landscape, where top acts can go unnoticed by swaths of the public, she’s a cultural phenomenon on par with Michael Jackson in the 1980s and the Beatles in the 1960s. [Source]
This is a great example of data reporting, and the key to it is contextualization.
How big is 2%? Well, it’s bigger than jazz. That’s maybe meaningful if you’re a jazz fan but for the rest of us?
What about bigger than MJ or the Beatles? Oh. That big. That’s big.
The importance of contextualization
One of the things about unusual numbers is that we don’t know how to contextualize them.
How do we orient ourselves around the number?
How do we evaluate the number? What’s in our common history, experience, or preparation that can guide our thinking?
How do we — or do we — act on the number?
In this case, it’s (mostly) for entertainment, so this last question perhaps is a moo point (sic):
Alert: Taylor Swift is bigger than jazz.
Orientation: > 2% of the market.
Evaluation: how does this compare to known benchmarks: bigger than MJ & the Fab Four!
Action: find a way to invest in her catalog.
But that model (Alert → Orient → Evaluate → Act) is critical to effective metrics reporting. If we don’t understand the number being reported, we run the risk of:
Ignoring it.
Over-reacting to it.
Taking the wrong action.
Stay tuned: when data-based decision-making fails
In my next article, I’ll write about precisely that failure of decision-making: the Great Bay Area Tsunami Warning of December 2024.