This paper examines the strategic application of Business Intelligence (BI) methodologies to the management of urban Third Places—semi-public sociocultural venues that bridge domestic and occupational environments. Although these spaces play a critical role in fostering social interaction and community resilience, urban governance lacks empirical models for translating visitor behavior into actionable planning insights. To address this gap, it was conducted a case study of Sevcable Port, a large Third Place in Saint Petersburg, Russia. A mixed-methods approach was employed, combining stratified temporal sampling and survey data (N = 283). The analysis included descriptive statistics, k-means clustering, chi-square tests, and regression modeling implemented in Python (Statsmodels). Four distinct visitor archetypes were identified, revealing variations in demographic profiles, spatial behaviors, and spending patterns. Chi-square tests demonstrated significant associations between age, gender, visit frequency, and behavioral variables. While regression analysis showed a weak but suggestive link between stay duration and spending, the explanatory power of the models was limited. The findings provide practical recommendations for programming, infrastructure planning, and inclusive marketing strategies. By framing Third Places as data-rich environments, this study highlights the potential of BI to inform data-driven urban governance and contribute to the development of more adaptive, inclusive, and resilient civic spaces.