Citation

Ting, Choo Yee and Ho, Chiung Ching and Yee, Hui Jia and Matsah, Wan Razali (2018) Geospatial Analytics in Retail Site Selection and Sales Prediction. Big Data, 6 (1). pp. 42-52. ISSN 2167-6461

Full text not available from this repository.

Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Sales forecasting, retail site selection, retail trade area analysis, sales prediction
Subjects:	H Social Sciences > HF Commerce > HF5001-6182 Business > HF5410-5417.5 Marketing. Distribution of products
Divisions:	Institute for Postgraduate Studies (IPS)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	10 Nov 2020 14:25
Last Modified:	10 Nov 2020 14:25
URII:	http://shdl.mmu.edu.my/id/eprint/7294

Downloads

Edit (login required)