Datalab

Audience Segmentation

How to get to know the digital visitor?

Developing a new tool, offer or exhibition requires to get to know your audience. But how can this be done better for the digital museum and can methods from the field of artificial intelligence help in this regard?

Although the field of visitor research has seen advances in theory and practice in recent years (an example in Germany being the Visitor Research Network), we still know very little about the audience of the digital museum space. To get closer to the digital visitors of Badisches Landesmuseum, we chose various starting points.

For one, together with Landesmuseum Württemberg, we held a digital conference in November 2021 , which brought together international best practice examples, including a contribution by John Stack on key figures of digital visits and a contribution by Alexander Britz on approaches to recording visitors with the help of artificial intelligence.

Adding to that, Johannes Bernhardt and Christian Gries summarised overarching models for collecting and analysing digital visitor data in an essay.

Our second starting point was a digital user survey, which was conducted in 2021 and evaluated in 2022 in cooperation with Kiel University of Applied Sciences and the ZEB | Centre for Evaluation and Visitor Research in 2022.

The user data was statistically evaluated until December 2021, and an AI-supported segmentation model was developed, which can be reused.

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The aim was to shed light on the topic of AI-supported audience segmentation from a research and development perspective in order to develop approaches that can serve as a basis for the further development of the xCurator tool and to support museums in getting to know their visitors better. Through the parallel development of key figures, the examination of long-term visitor tracking and the introduction of new tools, synergies were created that contributed to a better understanding our digital users.