Recommending a retail business given a particular location of interest is nontrivial. Such a recommendation process requires careful study of demographics, trade area characteristics, sales performance, traffic, and environmental features. It is not only human effort taxing but often introduces inconsistency due to subjectivity in expert opinions. The process becomes more challenging when no sales data can be used to make a recommendation. As an attempt to overcome the challenges, this study used the machine learning approach that utilizes similarity measures to perform the recommendation. However, two challenges required careful attention when using the machine learning approach: (1) how to prepare a feature set that can commonly represent different types of retail business and (2) which similarity measure approach produces optimal recommendation accuracy? The data sets used in this study consist of points of interest, population, property, job type, and education level. Empirical studies were conducted to investigate (1) the overall accuracy of proposed similarity measure approaches to the retail business recommendation, and (2) whether the proposed approaches have a bias toward certain retail categories. In summary, the findings suggested that the proposed similarity-based techniques elicited an accuracy of above 70% and demonstrated higher accuracy when the recommendation was made within a set of similar retail businesses.
Studies have shown that certain features from geography, demography, trade area, and environment can play a vital role in retail site selection, largely due to the impact they asserted on retail performance. Although the relevant features could be elicited by domain experts, determining the optimal feature set can be intractable and labor-intensive exercise. The challenges center around (1) how to determine features that are important to a particular retail business and (2) how to estimate retail sales performance given a new location? The challenges become apparent when the features vary across time. In this light, this study proposed a nonintervening approach by employing feature selection algorithms and subsequently sales prediction through similarity-based methods. The results of prediction were validated by domain experts. In this study, data sets from different sources were transformed and aggregated before an analytics data set that is ready for analysis purpose could be obtained. The data sets included data about feature location, population count, property type, education status, and monthly sales from 96 branches of a telecommunication company in Malaysia. The finding suggested that (1) optimal retail performance can only be achieved through fulfillment of specific location features together with the surrounding trade area characteristics and (2) similarity-based method can provide solution to retail sales prediction.