Using location-based social networks as a novel source of marketing data: the case of shopping malls
In this paper we demonstrate how publicly available data from location-based social networks can be used to model the popularity of different places. Our empirical analysis is based on a sample of 112 shopping malls located in Saint- Petersburg, the second largest city in Russia. Regression models that explain various measures of shopping malls' popularity using the characteristics of a place are built. It appeared that the number of visitors, check-ins, tips and even the frequency of visit can be largely explained by the basic features of a shopping mall, while successful modeling of a place's user rating requires more detailed data. Combining the data on the features of places, text reviews and popularity indicators from social networks is a promising approach for building sales, traffic, satisfaction and loyalty projection models, which are especially useful in business planning.