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A neuro-fuzzy two-stage clustering approach to customer segmentation

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Abstract

Improper selection of segmentation variables and tools may have an effect on segmentation results and can cause a negative financial impact. There have been numerous traditional models in the literature to segment customers; the most effective one is based on two-stage clustering methodology. However, none of the traditional approaches has the ability to establish non-strict customer segments that are significantly crucial for today’s competitive consumer markets. The aim of this study is to propose a two-stage clustering model for customer segmentation using Artificial Neural Networks and Fuzzy Logic. Segmenting customers was done according to the purchasing behaviours of customers via utilising Recency, Frequency and Monetary values. By using a secondary data set from a UK retail company, the model was also compared with traditional two-stage method based on two clustering validity indices. The findings indicated that the proposed model provided better insights and managerial implications with respect to the chosen validity indices.

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  1. Management Information Systems, Yildirim Beyazit University, Cinnah Street, Cankaya, 06100, Ankara, Turkey

    Abdulkadir Hiziroglu

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Correspondence to Abdulkadir Hiziroglu.

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1has been in academia since 2001. He received BSc and MSc degrees from Sakarya University Industrial Engineering Department. He then pursued his PhD degree at Manchester Business School, United Kingdom. During his PhD, he worked on designing and implementing a customer segmentation model using soft computing technologies within the framework of data mining and knowledge discovery. Currently, He works for Yildirim Beyazit University Management Information Systems Department as an Assistant Professor, in Ankara.

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Hiziroglu, A. A neuro-fuzzy two-stage clustering approach to customer segmentation. J Market Anal 1, 202–221 (2013). https://doi.org/10.1057/jma.2013.17

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