A Multi-Data Mining Approach for Shelf Space Optimization - Considering Customer Behaviour
Sheng-Hsiang Huang, Chieh-yuan Tsai, Chih-Chung Lo
2014
Abstract
A well product-to-shelf assignment strategy can help customers easily find product items and dramatically increase the retailing store profit. Previous studies in this area usually applied the space elasticity to optimize product assortment and space allocation models. However, a well product-to-shelf assignment strategy should not only consider product assortment and space elasticity. Thus, this study develops a product-to-shelf assignment approach by considering both product association rules and traveling behaviour of consumer. Specifically, the first task of this research is to develop a method to discover traveling behaviour of consumer, which includes both product association rules and traveling behaviour of consumer, in the store. The second task is to construct and solve a product-to-shelf assignment model, based on the information provided in the first task. In this research, products are classified as major item, minor item and the others. Only minor will be reassigned. Experimental result shows our proposed method can reassign minor items to suitable shelves and increase cross-selling opportunity of major and minor items.
References
- Agrawal, R., Srikant R., 1994. Fast algorithms for mining association rules', Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, 487-499, Santiago, Chile.
- Chen, M.C., C.P. Lin, 2007. A data mining approach to product assortment and shelf space allocation. Expert Systems with Applications, 32 (4), 976-986.
- Kuhn, H.W., 1955. The Hungarian Method for the assignment problem. Naval Research Logistics Quarterly, 2, 83-97.
- Larson, J.S., Bradlow, E.T., Fader, P.S., 2005. An exploratory look at supermarket shopping paths. International Journal of Research in Marketing, 22 (4), 395-414.
- Levy, M. and Weitz, B.A., 1995. Retailing management, Irwin, Chicago.
- Liu, Y., Liao, W.K., Choudhary, A., 2005. A fast high utility itemsets mining algorithm. Proceedings of the 1st International Workshop on Utility-Based Data Mining, 90-99.
- Shie, B.E., Hsiao, H.F., Tseng, V.S., 2012. Efficient algorithms for discovering high utility user behavior patterns in mobile commerce environments. Knowledge Information Systems, 37 (2), 363-387.
- Yang, M.H., 1999. An efficient algorithm to allocate shelf space. European Journal of Operational Research, 131(1), 107-118.
- Yun, C.H. Chen, M.S., 2007. Mining mobile sequential patterns in a mobile commerce environment. IEEE Transactions on Systems, Man and Cybernetics-Part C, 30 (2), 278-295.
Paper Citation
in Harvard Style
Huang S., Tsai C. and Lo C. (2014). A Multi-Data Mining Approach for Shelf Space Optimization - Considering Customer Behaviour . In Proceedings of the 11th International Conference on e-Business - Volume 1: ICE-B, (ICETE 2014) ISBN 978-989-758-043-7, pages 89-95. DOI: 10.5220/0005037700890095
in Bibtex Style
@conference{ice-b14,
author={Sheng-Hsiang Huang and Chieh-yuan Tsai and Chih-Chung Lo},
title={A Multi-Data Mining Approach for Shelf Space Optimization - Considering Customer Behaviour},
booktitle={Proceedings of the 11th International Conference on e-Business - Volume 1: ICE-B, (ICETE 2014)},
year={2014},
pages={89-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005037700890095},
isbn={978-989-758-043-7},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 11th International Conference on e-Business - Volume 1: ICE-B, (ICETE 2014)
TI - A Multi-Data Mining Approach for Shelf Space Optimization - Considering Customer Behaviour
SN - 978-989-758-043-7
AU - Huang S.
AU - Tsai C.
AU - Lo C.
PY - 2014
SP - 89
EP - 95
DO - 10.5220/0005037700890095