Legitimacy in live entertainment starts with your own data

This year the Reeperbahn Festival in Hamburg is putting a question to the entire industry: "Forever Legit?" Coming from a business that finds it easier to talk about growth than about itself, that is a notable move.

We took it into ticketing, because that is where the question refuses to stay abstract. Acting legitimately means having answers. Who is my audience. How did my prices come about. What did any of this do. Whether an organizer has those answers is settled by the systems they work with, not by their intentions.

That is where live entertainment gets interesting, because expectations have climbed. Fans want to be recognized. They expect an organizer to know they have already been three times. They do not compare your checkout to other organizers, they compare it to everything else they do online. And they decide later than ever. A show you could read eight weeks out is now sometimes a show you read eight days out.

The questions most organizers cannot answer

On the other side are organizers who want to know exactly this and cannot get to it. How many guests came for the first time. How many come back. Which price point worked and which one was only a discount. Which channel brings buyers who buy a second time.

The information exists. It sits in pieces: some in ticketing, some in the email tool, some in the CRM, some in the head of the person who has been there for twelve years. At larger operations it multiplies. Run four venues, three event brands and a tour across five markets, and your systems do not hold one audience. They hold a dozen separate records where the same person turns up again and again.

A sold-out night proves that tickets moved. It says very little about the relationship with the audience.

First legit, then AI

The industry's standard answer right now is AI. Forecasts, price recommendations, audience segments on demand. Most AI projects in ticketing do not run aground on the model. They run aground on what sits underneath it. The same fan exists three times under three addresses. Whether someone came back is in no field, because the field was never created. Put a model on top of that and you do not get insight, you get a well formatted guess.

You notice it first in the marketing budget. If you cannot say which channel brings buyers who come back a second time, you spend on instinct and call it experience. In a market where the cost per show keeps climbing and the buying decision keeps arriving later, that gets expensive.

Where to start

Moritz Knabe, CEO and founder of BYB Agency and BYB Labs, watched the industry's last technology shift up close:

"Technology is moving faster than organizations can decide. Keeping up means radically shortening the distance between an idea and putting it to work. In practice, that means building small use cases, learning from them and scaling from there, instead of spending two years polishing a grand strategy."

That is the workable path, and it starts smaller than most people expect. It starts with one question an organizer wants answered about their audience, and with the work it takes to make the answer hold up.

One question is enough for a start. What share of the crowd is here for the first time? That does not call for a two-year roadmap. It calls for a checkout that ends with the organizer instead of a third party, and one place where orders, scans and contacts come together. Once that number holds up, the next one gets easier. How many of those first-timers come back. After that: through which channel.

This is how an ordinary ticket purchase becomes a customer profile that shows who buys early, who reacts to price and who returns. From there on, AI in ticketing gets interesting, because it has something real to work with.

An organizer who knows their audience guesses less, can explain their pricing and holds the relationship with their fans directly. That is the practical version of legit.