The 90% myth: What the Oliver Wyman study hid in the footnotes

We’ve all heard the stat: “90% of first-time orchestra attendees don’t return the next season.”

That shocking figure from Oliver Wyman's 2007 study has haunted the arts sector for nearly two decades. It’s a mic-drop moment that launched countless churn reports, loyalty initiatives, and retention conversations. But there’s something in that study that most people missed.

Buried in a quiet footnote on page six is this line:

“One or two year ‘dippers’ are estimated to be 9% of the audience.”

Nine percent. Nearly 1 in 10 people labeled as "churned" actually did return—just not on the one-year cycle the model expected.

They didn’t ghost us. They paused. They came back when it made sense for them.

That single line, tucked in the footnotes, could have changed everything.

Because what we’ve been calling churn might actually be something else entirely.

Built for Institutions, Not Real People

Why did Oliver Wyman lean so hard into a one-year return metric? Simple. Because orchestras are built around a season-based model:

• Programming is planned annually

• Subscriptions renew yearly

• Budgets are tied to fiscal years

So the study asked, “Do new audience members return next season?”

That’s the question institutions care about. The report wasn’t designed to understand customers—it was designed to diagnose a loyalty problem inside the subscription model.

Which is why the “dipper” insight barely makes it out of the margins. It suggest a more fluid, human pattern of engagement—one that didn’t fit the dominant narrative.

A Misunderstanding with Real Consequences

That 9% figure isn’t a throwaway. It’s a lifeline. Because if almost 10% of those so-called lost patrons did return eventually, then maybe the problem isn’t that audiences are disloyal.

Maybe the problem is that we’re measuring loyalty on the wrong timeline. As Colleen Dilenschneider’s research tells us, the typical visitation cycle—average time between visits—is approximately 28-34 months. That’s two to three years!

We’re calling people lost when they’re just… not ready.

Harvard Business Review says "in this new era of digital-based competition and customer control, people are increasingly buying because of a brand’s relevance to their needs.”

If your model is built on annual loyalty, it's built on a misunderstanding of how real people behave. The needs that arts experiences fulfill are episodic. Not habitual. And not perfectly cyclical.

What we’ve long called churn is something else entirely: A natural ebb and flow based on people’s changing needs, priorities, and lives.

Even outside the arts, loyalty has unraveled. Consumers dip in and out based on need. And brands that meet episodic needs rather than daily needs—like the arts do—are building models around relevance, not habit.

Are we ignoring potential audiences simply because they’re being… normal?

These People Came Back Despite the Model

And here’s the kicker: that 9% newcomer return rate happened in spite of the system. What if episodic engagement had been baked into the model? Imagine a system that said:

• "We’re still here when you’re ready."

• “Here’s a one-time engagement opportunity that aligns with your needs.”

• “Some people subscribe and donate. Others don’t. And that’s ok.”

That’s what building for reality looks like. It means assuming discontinuity, planning for re-entry at any time, and normalizing gaps.

It means moment-centered entry points for non-traditional patrons. Instead of "Here’s our season" it’s need based events like:

• Digital detox night

• Escape-the-chaos matinee

• Connect with other lonely retirees at our upcoming chamber concert

It means tracking signals, not just attendance. When you ground your messaging in real-life needs, you learn more about how to engage lapsed patrons. Clicks matter. Email opens matter. Likes matter.

And it means reinvitation over judgment. Let go of shame-based messaging. Build trust over time. Not months. Years.

Bottom Line

The 9% return rate didn’t happen because the model supported episodic loyalty.

It happened in spite of a model that wasn’t built for it.

That’s the real mic-drop: if that many people came back under friction, how many more might return if you actually welcomed them?

And that tiny footnote tells us episodic behavior was already happening in the early 2000s—before smartphones, before streaming, before the attention economy went into overdrive.

If that was true in 2007, imagine how much more true it is today—in a world of endless options, constant distraction, and fractured attention.

Churn isn’t failure. It’s a signal. A reality to design for—not against.

And when we stop trying to fix it, and start building with it? We get something stronger than loyalty: Relevance.