Where is the Goldmine? Finding Promising Business Locations through Facebook Data Analytics
This addresses the challenge for entrepreneurs and businesses in making data-driven location decisions, though it is incremental as it applies existing methods to new social media data.
The paper tackles the problem of selecting optimal business locations by analyzing Facebook Pages data, including check-ins and business types, to predict location popularity, achieving accurate predictions primarily through features from neighboring businesses.
If you were to open your own cafe, would you not want to effortlessly identify the most suitable location to set up your shop? Choosing an optimal physical location is a critical decision for numerous businesses, as many factors contribute to the final choice of the location. In this paper, we seek to address the issue by investigating the use of publicly available Facebook Pages data---which include user check-ins, types of business, and business locations---to evaluate a user-selected physical location with respect to a type of business. Using a dataset of 20,877 food businesses in Singapore, we conduct analysis of several key factors including business categories, locations, and neighboring businesses. From these factors, we extract a set of relevant features and develop a robust predictive model to estimate the popularity of a business location. Our experiments have shown that the popularity of neighboring business contributes the key features to perform accurate prediction. We finally illustrate the practical usage of our proposed approach via an interactive web application system.