Predicting retail business success using urban social data mining

Abstract

Predicting the footfall in a new brick-and-mortar shop (and thus, its prosperity), is a problem of strategic importance in business. Few previous attempts have been made to address this problem in the context of big data analytics in smart cities. These works propose the use of social network check-ins as a proxy for business popularity, concentrating however only on singular business types. Adding to the existing literature, we mine a large dataset of high temporal granularity check-in data for two medium-sized cities in Southern and Northern Europe, with the aim to predict the evolution of check-ins of new businesses of any type, from the moment that they appear in a social network. We propose and analyze the performance of three algorithms for the dynamic identification of suitable neighbouring businesses, whose data can be used to predict the evolution of a new business. Our SmartGrid algorithm reaches a performance of being able to accurately predict the evolution of 86% of new businesses. In this paper, extended from our original contribution at IEEE InteEnv'19, we further investigate the influence of neighbourhood venues in prediction accuracy, depending on their exhibited weekly data patterns.