Comparative Analysis of Store Clustering Techniques in the Retail Industry

Kanika Agarwal, Prateek Jain, Mamta Rajnayak

2019

Abstract

Many offline retailers in European Markets are currently exploring different store designs to address local demands and to gain a competitive edge. There has been a significant demand in this industry to use analytics as a key pillar to take store-centric informed strategic decisions. The main objective of this case study is to propose a robust store clustering mechanism which will help the business to understand their stores better and frame store-centric marketing strategies with an aim to maximize their revenues. This paper evaluates four advance analytics-based clustering techniques namely: Hierarchical clustering, Self Organizing Maps, Gaussian Mixture Matrix, and Fuzzy C-means These techniques are used for clustering offline stores of a global retailer across four European markets. The results from these four techniques are compared and presented in this paper.

Paper Citation

in Harvard Style

Agarwal K., Jain P. and Rajnayak M. (2019). Comparative Analysis of Store Clustering Techniques in the Retail Industry.In Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-377-3, pages 65-73. DOI: 10.5220/0007917500650073

in Bibtex Style

@conference{data19,
author={Kanika Agarwal and Prateek Jain and Mamta Rajnayak},
title={Comparative Analysis of Store Clustering Techniques in the Retail Industry},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2019},
pages={65-73},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007917500650073},
isbn={978-989-758-377-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Comparative Analysis of Store Clustering Techniques in the Retail Industry
SN - 978-989-758-377-3
AU - Agarwal K.
AU - Jain P.
AU - Rajnayak M.
PY - 2019
SP - 65
EP - 73
DO - 10.5220/0007917500650073