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Geospatial Analytics in Retail Site Selection and Sales Prediction
Choo-Yee Ting1, Chiung Ching Ho1, Hui Jia Yee1
11 Faculty of Computing and Informatics, Multimedia University-Cyberjaya Campus , Cyberjaya, Malaysia .
Big Data
|March 24, 2018
Summary
Selecting optimal retail sites involves analyzing geography, demographics, and environment. This study uses feature selection and similarity-based methods for accurate retail sales prediction, improving business performance.
Area of Science:
- Retail analytics
- Geographic Information Systems (GIS)
- Predictive modeling
Background:
- Retail site selection is influenced by geographic, demographic, trade area, and environmental factors impacting performance.
- Identifying optimal features and predicting sales for new locations presents challenges, especially with time-varying data.
Purpose of the Study:
- To propose a non-intervening approach for retail site selection and sales prediction.
- To address the challenges of determining important features and estimating sales performance for new retail locations.
Main Methods:
- Employed feature selection algorithms to identify crucial retail site characteristics.
- Utilized similarity-based methods for predicting retail sales performance.
- Data from 96 Malaysian telecommunication branches, including location, population, property type, education, and sales, were aggregated and analyzed.
Main Results:
- Optimal retail performance is achieved by integrating specific location features with surrounding trade area characteristics.
- Similarity-based methods effectively predict retail sales, offering a viable solution for performance estimation.
Conclusions:
- The study demonstrates the efficacy of combining feature selection and similarity-based prediction for retail analytics.
- Findings highlight the importance of a holistic approach considering both site-specific and trade area factors for successful retail strategy.
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