A hybrid approach for managing retail assortment by categorizing products based on consumer behavior

Karki, Dhiraj (2018) A hybrid approach for managing retail assortment by categorizing products based on consumer behavior. Masters thesis, Dublin, National College of Ireland.

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Abstract

Managing product assortment and shelfspace has been a challenge for every retailer. Retailers face the decision on what products to keep and how much quantity. Assortment and inventory management practices can have a considerable impact on the overall business of the retailer. Studies in assortment management have been limited to understanding transactional data and creating rules for making assortment decision. Hardly any product level information apart from sales is used while making these decisions. This study focusses on understanding product-customer relationship and using it as an input for managing assortment. Certain products and categories may have significant impact on customer buying behavior and therefore it is important to identify and categorize such products based on their impact on customer behavior. For this study ideal customer segments were identified using un-supervised k-means clustering. Products we clustered into different categories using fuzzy c-means clustering method. The product buying behavior of ideal customer cluster was studied to identify products which are preferred by them using association rule mining ARM. Based on their preference these products were assigned extra weights or minimum threshold. Weighted association rule mining WARM method was used to create assortment rules, which were then compared to a general assortment strategy to test whether certain category of products need to have extra weight or minimum support threshold based on their impact on customer buying behavior.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software H Social Sciences > HF Commerce > Marketing > Consumer Behaviour H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Retail Industry
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Caoimhe Ní Mhaicín
Date Deposited:	06 Nov 2018 12:20
Last Modified:	06 Nov 2018 12:20
URI:	https://norma.ncirl.ie/id/eprint/3446

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