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Authors: Kanika Agarwal ; Prateek Jain and Mamta Rajnayak

Affiliation: Accenture Digital, Accenture Private Solution Limited. and India

Keyword(s): Store Clustering, Self Organizing Maps, Gaussian Mixture Matrix, Fuzzy C-means.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Data Analytics ; Data Engineering ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Predictive Modeling ; Symbolic Systems

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.

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Paper citation in several formats:
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 - DATA; ISBN 978-989-758-377-3; ISSN 2184-285X, SciTePress, pages 65-73. DOI: 10.5220/0007917500650073

@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 - DATA},
year={2019},
pages={65-73},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007917500650073},
isbn={978-989-758-377-3},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - DATA
TI - Comparative Analysis of Store Clustering Techniques in the Retail Industry
SN - 978-989-758-377-3
IS - 2184-285X
AU - Agarwal, K.
AU - Jain, P.
AU - Rajnayak, M.
PY - 2019
SP - 65
EP - 73
DO - 10.5220/0007917500650073
PB - SciTePress

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