I’ve spent three decades watching venue bookers make decisions. Some of them are brilliant at it — they’ve got an instinct for what will work in their room, built from years of watching crowds and reading the market. Others are terrible, booking acts based on personal taste, label relationships, or whatever their mate in the industry told them was “about to blow up.”

The difference between a venue that consistently fills its room and one that struggles isn’t luck. It’s information. And increasingly, the venues getting it right are the ones using actual data to make booking decisions rather than relying on gut feel alone.

The Old Way Still Dominates (And That’s a Problem)

Let me describe how most mid-level Australian venues still book acts, because I think people outside the industry would find it shocking.

A promoter or agent calls the booker. They pitch an act — maybe share some streaming numbers, mention a few supports they’ve done, name-drop a festival or two. The booker checks their calendar, thinks about whether the act “fits” their venue, maybe looks at some Spotify numbers, and makes a call. If the act has played the venue before, they might check how many tickets sold last time. If they haven’t, it’s mostly guesswork informed by experience.

This process has barely changed in 30 years. And while experienced bookers develop genuine intuition over time, the failure rate is still high. I’ve seen rooms that average 30-40% unsold capacity across their annual programming. That’s money left on the table every single night. It’s also bad for artists, who play to half-empty rooms that should’ve been full if the right act had been programmed.

What Data-Driven Booking Looks Like

The venues doing this well aren’t replacing human judgement with algorithms. They’re feeding their bookers better information so those human judgements are sharper.

The Forum in Melbourne is one venue I’ve watched evolve its approach over the past few years. Their programming team now uses a combination of streaming data, social media analytics, ticket pre-sale performance from other markets, and their own historical data to assess acts before committing. They track which genres perform best on which nights of the week, which ticket price points their audience tolerates, and which promotional channels drive the most conversions.

The result? Their fill rate has improved noticeably. They’re taking fewer gambles on acts that look good on paper but don’t have the local audience to fill an 1,800-cap room. And when they do take a punt on an emerging act, they’re doing it with data that suggests the audience is there.

The Enmore Theatre in Sydney has always been well-programmed, but their team has gotten smarter about using pre-sale data from touring acts’ other Australian dates to gauge Sydney demand. If an act is selling well in Brisbane and Melbourne but the agent is quoting Sydney numbers that seem optimistic, the data gives the booker ammunition to negotiate terms that reflect realistic expectations rather than inflated projections.

The Tivoli in Brisbane has been experimenting with audience survey data — not just post-show satisfaction surveys, but proactive research asking their mailing list what they want to see. This sounds obvious, but almost no venues do it systematically. The Tivoli’s programming team uses this demand data alongside traditional booking channels, and it’s surfaced acts they wouldn’t have considered otherwise — artists with strong local followings that don’t show up in national streaming charts.

The Data Sources That Matter

Not all data is equally useful for booking decisions. Here’s what I’ve seen actually move the needle.

Spotify and Apple Music listener geography. Streaming platforms now provide reasonably granular data on where an artist’s listeners are located. If a band has 500,000 monthly listeners but only 3,000 of them are in Sydney, that tells you something very different from a band with 50,000 monthly listeners and 8,000 in Sydney. The second act will probably sell more tickets at a Sydney show, despite having a fraction of the global numbers.

Social media engagement rates (not just follower counts). Follower counts are meaningless for predicting ticket sales. What matters is engagement — comments, shares, saves — and particularly engagement from people in the venue’s city. An act with 20,000 Instagram followers and 8% engagement is a better booking bet than one with 200,000 followers and 0.3% engagement. The first has a dedicated community. The second has an audience that scrolls past.

Historical venue data. This is the most valuable and most underused data source. Every venue has years of ticket sales data sitting in their ticketing system. Which genres sell best? Which days of the week? What’s the optimal ticket price for their audience? How far in advance do their punters buy? How much do walk-ups contribute? The venues that actually analyse this data make dramatically better decisions than those who just vaguely remember how last year went.

Touring performance data. How an act performs in other Australian cities on the same tour is highly predictive of how they’ll perform in yours. AI consultants in Melbourne have been building models that use early-market ticket sales to predict performance in later tour markets, giving bookers and promoters better visibility into likely outcomes before they commit.

Pre-sale and early sales velocity. The speed at which tickets sell in the first 48 hours after going on sale is strongly predictive of final sales outcomes. Venues that track this can make early decisions about marketing spend, capacity adjustments, and support act choices while there’s still time to influence the outcome.

The Resistance

Not everyone’s on board, and I understand some of the pushback.

The most common objection is that data-driven booking will lead to safe, homogeneous programming — venues only booking acts that the numbers say will sell, ignoring emerging artists, experimental music, and genre diversity. It’s a legitimate concern. If you only book what the data says will work, you’ll never discover the next act that doesn’t fit any existing pattern.

My counter: good data-driven booking should make venues more willing to take risks, not less. If your bread-and-butter programming is consistently filling the room because you’re making informed choices, you’ve got the financial headroom to take a punt on an unknown act on a Tuesday night. It’s the venues that are barely breaking even on every show that can’t afford risks.

Another objection comes from agents and promoters who benefit from information asymmetry. When a booker doesn’t have good data, the agent controls the narrative — they can present streaming numbers selectively, exaggerate comparison acts’ performance, and generally sell harder than the facts warrant. Data-literate bookers are harder to bullshit, and some agents don’t love that.

Good. The industry needs less bullshit.

What’s Missing

The biggest gap I see is that most venues are still doing this analysis manually and sporadically. A booker might check Spotify numbers for a specific act they’re considering, but they’re not systematically scanning the landscape for acts that their data suggests would work in their room.

The tools exist — Chartmetric, Soundcharts, Bandsintown for Artists, various social listening platforms — but they’re designed for labels and managers, not venue bookers. There’s a real opportunity for someone to build a venue-focused analytics platform that combines streaming data, social data, historical venue data, and touring data into a single dashboard that answers the question every booker asks: “Will this act sell tickets in my room?”

A few companies are working on this, and I think whoever cracks it will change how mid-level venues operate. The information is all there. It just needs to be assembled in a way that’s useful to the people making nightly programming decisions.

The Bottom Line

I’m not arguing that data should replace the booker. The best bookers bring something that no algorithm captures — an understanding of their room’s culture, a sense of timing, a nose for what their specific audience will respond to. Data doesn’t tell you that your crowd has been craving a good funk night, or that the local scene is ready for a heavier band than you’d normally book.

But data can stop you from making expensive mistakes. It can confirm your instincts or challenge your assumptions. It can give you the confidence to say yes to an act you believe in and the evidence to say no to one that doesn’t add up.

In an industry where margins are tight and every empty seat costs money, booking smarter isn’t optional anymore. It’s survival.