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New Behavioral Segmentation Methods to Understand Consumers in Retail Industry


Affiliations
1 Universiti Sains, Malaysia
2 Mansoura University, Egypt
 

Behavioral segmentation is considered as one of the most important concepts of modern marketing. Traditional customer segmentation models require months of analytical work, resulting in discrete consumers’ insights that are outdated to match the dynamic body of the consumers they are meant to represent. Personalization and consumer experience are make or break factors for the retail industry. This study looks towards maximizing Consumer Lifetime Value (LTV) to accommodates the dynamics in consumer shopping behavior for a medium size retailer. using (LTV) matricto investigate behavioral changes in the consumer shopping history gaining knowledge from behavioral and demographic variables stored in POS database converted into RFM dataset format. In addition, this study applies soft clustering Fuzzy C-Means (FCM) and hard clustering Expectation Maximization (EM) algorithms to classify individual consumers exhibit similar purchase history into specific groups. For measuring the algorithms accuracy, weuse cluster quality assessment (CQA). The CQA shows EM algorithm scales much better than Fuzzyy C-Means algorithm with its ability to assign good initial points in the smaller dataset.

Keywords

Customer Segmentation, Clustering, LTV Matric, Retailing.
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  • New Behavioral Segmentation Methods to Understand Consumers in Retail Industry

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Authors

Fahed Yoseph
Universiti Sains, Malaysia
Nurul Hashimah Ahamed Hassain Malim
Universiti Sains, Malaysia
Mohammad AlMalaily
Mansoura University, Egypt

Abstract


Behavioral segmentation is considered as one of the most important concepts of modern marketing. Traditional customer segmentation models require months of analytical work, resulting in discrete consumers’ insights that are outdated to match the dynamic body of the consumers they are meant to represent. Personalization and consumer experience are make or break factors for the retail industry. This study looks towards maximizing Consumer Lifetime Value (LTV) to accommodates the dynamics in consumer shopping behavior for a medium size retailer. using (LTV) matricto investigate behavioral changes in the consumer shopping history gaining knowledge from behavioral and demographic variables stored in POS database converted into RFM dataset format. In addition, this study applies soft clustering Fuzzy C-Means (FCM) and hard clustering Expectation Maximization (EM) algorithms to classify individual consumers exhibit similar purchase history into specific groups. For measuring the algorithms accuracy, weuse cluster quality assessment (CQA). The CQA shows EM algorithm scales much better than Fuzzyy C-Means algorithm with its ability to assign good initial points in the smaller dataset.

Keywords


Customer Segmentation, Clustering, LTV Matric, Retailing.

References