Measurement fallacies in music and fandom
Why music is more “unmeasurable” than most industries
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Let’s dive into today’s topic:
Measurement fallacies in music and fandom
Metrics can predict behaviour. But in music, they primarily outline the context.
Why it matters
Music is unusually vulnerable to measurement fallacies, the logical errors that happen when we confuse what’s easy to measure with what’s actually meaningful.
In tech or retail, data often mirrors reality. Clicks mean attention. Sales mean revenue. But in music, the link between numbers and behaviour is weaker.
Music consumption is mostly passive. It can be done while focusing our attention on other tasks. Even fans may stream without noticing, follow without caring, and watch without engaging.
Artists and industry execs mistake these signals for genuine connection, leading to inflated confidence, poor decisions, and wasted energy.
How it works
In other industries, metrics can predict behaviour. But in music, they mainly describe context:
Streaming doesn’t predict ticket sales. Passive listening dominates modern platforms. Fans discover songs through algorithmic playlists, social snippets, or ambient settings like cafés and gyms. It’s listening without intent, while attending a show is highly intentional.
Social media following doesn’t predict current interest. A follow is a snapshot of the past, not a signal of present interest. It freezes a moment of enthusiasm, but tastes shift. Following measures who once cared, not who still do.
Social media following doesn’t predict ticket sales. Most followers don’t see all artists’ posts, don’t live nearby, and don’t plan to take action. They follow for entertainment, not participation.
Fanbase demography doesn’t predict market value. Age, gender, or income fail to explain emotional value. A student might skip lunch but buy a €70 hoodie to feel part of something. Besides, it’s not only a competition for the budget. Fans also take time, energy, social proof and other variables into consideration when evaluating the money they spend on music.
Engagement doesn’t predict fandom. Likes, comments, and shares often signal curiosity, humour, or outrage instead of emotional investment. The post that “performs” best might attract the least loyal audience.
Playlist adds don’t predict audience growth. Getting onto a big playlist can inflate streaming numbers, but may not translate into a lasting connection. Fans of the playlist aren’t fans of the artist. They intend to listen to, for example, coffeehouse music, and may not care about which artist is playing.
Yes but…
Data still matters. It helps artists test ideas, understand patterns, and communicate with partners. The danger lies in overconfidence: When artists treat numbers as absolute truth rather than clues, they begin optimising for visibility instead of loyalty. That’s how careers can lose depth while chasing scale.
Take action now
Artists can reframe how they measure success. Look for repeat behaviours, not spikes, and focus on actionable metrics.
Your thoughts
Further reading
All eyes, no ears: New MIDiA data shows why virality is not building fandom (MIDiA)
How to leverage user intent (The Fanbase Builder)
Finding the metrics that matter (The Fanbase Builder)
Market value is the music industry’s hidden currency (The Fanbase Builder)
Why ‘Total Likes’ is an excellent TikTok metric (The Fanbase Builder)
How artists can find the one metric that matters (The Fanbase Builder)
This really resonates with what I've seen artists struggle with - the disconnect between vanity metrics and actual fan engagement. Your point about playlist adds being particularly misleading is spot on. I've noticed that artists who get algorithmically placed often see their streaming numers spike without any corresponding growth in email lists, merch sales, or concert attendance. The passive vs. intentional listening distinction is crucial - being background music while someone works out is fundamentally different from someone seeking out your music deliberately. I think the shift you're recommending toward repeat behaviors rather than spikes is essential, but it requires a complete reframing of how we measure success in the streaming age. The challenge is that labels and investors still speak the language of these vanity metrics, creating real pressure to optimize for the wrong things.