Beyond Demographics: Behavioural Segmentation and Spatial Analytics to Enhance Visitor Experience at The British Museum

TL;DR

This paper applies data science to analyze visitor behaviour at The British Museum, revealing key patterns, segmentation, and spatial navigation insights to improve visitor experience and museum planning.

Contribution

It introduces a novel data-driven approach combining audio guide logs and reviews to segment visitors and model spatial navigation in a museum context.

Findings

Identified four distinct visitor segments with different engagement levels.

High drop-off rates in tour usage, varying by language group.

Spatial flow influenced more by accessibility than thematic layout.

Abstract

This study explores visitor behaviour at The British Museum using data science methods applied to novel sources, including audio guide usage logs and TripAdvisor reviews. Analysing 42,000 visitor journeys and over 50,000 reviews, we identify key drivers of satisfaction, segment visitors by behavioural patterns, examine tour engagement, model spatial navigation, and investigate room popularity. Behavioural clustering uncovered four distinct visitor types: Committed Trekkers, Leisurely Explorers, Targeted Visitors, and Speedy Samplers, each characterised by different levels of engagement and movement. Tour usage analysis revealed high drop-off rates and variation in completion rates across different language groups. Spatial flow modelling revealed that accessibility and proximity, particularly aversion to stairs, shaped visitor paths more than thematic organisation. Room popularity was more strongly predicted by physical accessibility than curatorial content. We propose practical strategies for improving engagement and flow, offering a scalable framework for visitor-centred, data-informed museum planning.