Geo-Spotting: Mining Online Location-based Services for Optimal Retail Store Placement

TL;DR

This study leverages location-based social network data to predict retail store popularity and optimize store placement using machine learning, highlighting the importance of diverse geographic and mobility features.

Contribution

It introduces a novel approach combining geographic and user mobility features from social network data to predict retail success, improving placement strategies.

Findings

Combined features enhance prediction accuracy.

Geographic and mobility features are predictive across different retail chains.

Multiple factors influence retail store popularity.

Abstract

The problem of identifying the optimal location for a new retail store has been the focus of past research, especially in the field of land economy, due to its importance in the success of a business. Traditional approaches to the problem have factored in demographics, revenue and aggregated human flow statistics from nearby or remote areas. However, the acquisition of relevant data is usually expensive. With the growth of location-based social networks, fine grained data describing user mobility and popularity of places has recently become attainable. In this paper we study the predictive power of various machine learning features on the popularity of retail stores in the city through the use of a dataset collected from Foursquare in New York. The features we mine are based on two general signals: geographic, where features are formulated according to the types and density of nearby places, and user mobility, which includes transitions between venues or the incoming flow of mobile users from distant areas. Our evaluation suggests that the best performing features are common across the three different commercial chains considered in the analysis, although variations may exist too, as explained by heterogeneities in the way retail facilities attract users. We also show that performance improves significantly when combining multiple features in supervised learning algorithms, suggesting that the retail success of a business may depend on multiple factors.