SiteSense: Optimizing Restaurant Site Selection Using a Comprehensive Data-Driven Framework

Anvitha Swaroop, Kritika Prasad, Sonia Khetarpaul

Abstract: Restaurant site selection is a critical decision-making process that impacts the success and longevity of dining establishments. In Bangalore, where diverse culinary preferences and economic disparities coexist, selecting optimal locations for new restaurants poses a significant challenge. Traditional methods of location selection rely on population density and foot traffic, often resulting in an oversaturation of restaurants in certain areas and a lack of options in underrepresented neighbourhoods. In this, an approach has been proposed that combines sentiment analysis of customer reviews with geographic and socio-economic data to inform restaurant site selection. Here sentiment analysis is leveraged to identify areas with high customer satisfaction and frequent visits while incorporating socio-economic data to align restaurant placement with local demand and purchasing power. By utilising datasets from platforms like Zomato and government sources, a comprehensive framework has been developed for optimising restaurant locations across Bangalore. This approach has led to a 39% improvement in identifying underserved neighborhoods and a 57% increase in restaurant success rate by matching location with customer preferences and economic conditions.

External IDs:doi:10.1007/978-3-032-15134-6_12