The foundation for the Audience Monitor was already laid before DIP was established, and during the pandemic, it gained momentum. Joep: “There were two parties working on similar trajectories: Publiek.nl, an initiative of SPOT Groningen in collaboration with an agency, and Podiumkunst.info. In both cases, it was about sharing data and insights, but with a different focus. Podiumkunst.info mainly focused on working more efficiently and streamlining work processes, while Publiek.nl primarily aimed to generate data and insights based on ticket sales. The main challenge for both initiatives was getting enough parties on board. The industry associations found the two initiatives interesting but wanted to merge them with the major advantage that members would immediately become users of the new platform. This would also make it more attractive for other organizations to join.”
Unique ID
In 2019, the decision was made to build a new platform that combined both initiatives. Due to various circumstances, including the pandemic, it did not immediately take off. “When I started in 2022, the renewed platform was not yet there,” Joep explains. “After that, we managed to accelerate it. We started building the Podiumkunst.info section. It already had a large database behind it, and a unique identifier was linked to each production. That concept has been further developed: not only does the production get a unique code, but also the performances underneath it. Together, this forms the DIP ID, and based on that, we can recognize specific performances in all those different databases and then link sales and audience information to them. Thanks to an additional contribution from the Innovation Labs, we were able to further accelerate the process, and by the end of 2023, the Audience Monitor went live with 54 theaters.”
The Entire Professional Field
With the anonymized data in the DIP ID, it is possible to share data with and between theaters. “Moreover,” Joep continues, “for the first time, producers can now gain insight into who those ticket buyers are and how they behave. Previously, that data was only available to theaters. Currently, 126 theaters are connected to the Audience Monitor, and more are joining every month. By the end of the year, we hope to have 175. Then the majority of the professional field will be connected.” In the meantime, the tool is being further developed.”
Pilot Linking Cultural Target Group Model
The Audience Monitor is never finished; there will always be opportunities to further develop the model. “In collaboration with Cultuurmarketing, we have a focus group to map out what they use, which graphs are useful, and so on. We work on that feedback. Furthermore, we not only want to connect more theaters but also groups that are still missing, such as festivals or concert halls. We are in talks with Oerol and Theaterfestival Boulevard. Additionally, we want to explore what the sector can extract from that large database and what is still needed to make good analyses. We also want to continue with the predictive model. In the future, we aim to enrich productions with, for example, text, images, and player information. So, there is still a world to win, but the most important step has been taken.”
Collaborating Within Europe
With the Audience Monitor, DIP is one of the frontrunners in Europe. “We always look at the Audience Agency in the UK. They do interesting things, and we are in regular contact. But they don’t have such a unique identifier. On the other hand, I find the analyses they make there very interesting. So perhaps we can collaborate in the future. The European Audience Data Alliance is also very valuable. We can learn from each other and potentially use each other’s technical solutions. That would help everyone enormously. And if you choose uniform formats, you can also compare at the European level.”
Predictive Model: Audience Monitor X Predictive AI
Joep came into contact with Edsart Udo de Haes during the application period for the Innovation Labs. “Edsart was working on a predictive model and needed a large amount of data to feed this model. That’s when he approached DIP. We decided to make it a joint project and experiment together. With this model, Edsart aims to achieve better programming in the cultural sector. He himself is trained as a classical guitarist and noticed that many artists are staying at home. Even artists with a large online following sometimes rarely perform on stage. How do you ensure these artists get on programmers’ radar? This predictive model can help explore a different kind of offering and connect with artists who may still be unknown to the artistic staff. The model is based not only on sales data but also on online sources such as Spotify or YouTube.
The self-learning model has been developed as a programming tool for both theaters and producers. For example, as a theater, you can ask: I am theater X, and in the main hall, I want to program cabaret on March 12. What offerings would achieve – for instance – a certain revenue, the highest attendance, or attract a new audience? Producers can, for example, input that they are touring with players XYZ and ask where they should perform. Some producers fear that theaters with such a tool will only program performances with the highest revenue, thereby ignoring social and societal objectives. I’m not too worried about that. In Germany, programming is already done with the help of AI, and the opposite is happening. Because risks can be better assessed, there is more room for experimentation.
We have now reached a proof of concept; it is not yet part of the Audience Monitor or DIP. But the model has a lot of potential and is currently 85% accurate. The model needs further development, and adjustments to the database are also required. If cabaret artist X is on stage, that’s very clear. If you program a theater group, it’s not immediately clear who is on stage. But we now know it works. Now we need to figure out how to take it further!”
Also, listen to the podcast series Data, AI, and the Cultural Sector (opens in new tab)
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