A SOFT COMPUTING APPROACH TO CUSTOMER SEGMENTATION

Improper selection of segmentation variables and tools may have an effect on segmentation results and can cause a negative financial impact (Tsai & Chiu, 2004). With regards to the selection of segmentation variables, although general segmentation variables such as demographics are frequently utilised based on the assumption that customers with similar demographics and lifestyles tend to exhibit similar purchasing behaviours (Tsai & Chiu, 2004), it is believed the behavioural variables of customers are more suitable to use as segmentation bases (Hsieh, 2004). As far as segmentation techniques are concerned, two conclusions can be made. First, the cluster-based segmentation methods, particularly hierarchical and non-hierarchical methods, have been widely used in the related literature. But, the hierarchical methods are criticised for nonrecovery while the non-hierarchical ones are not able to determine the initial number of clusters (Lien, 2005). Hence, the integration of hierarchical and partitional methods (as a two-stage approach) is suggested to make the clustering results powerful in large databases (Kuo, Ho & Hu, 2002b). Second, none of those traditional approaches has the ability to establish non-strict customer segments that are significantly crucial for today’s competitive consumer markets. One crucial area that can meet this requirement is known as soft computing. Although there have been studies related to the usage of soft computing techniques for segmentation problems, they are not based on the effective two-stage methodology. The aim of this study is to propose a soft computing model for customer segmentation using purchasing behaviours of customers in a data mining framework. The segmentation process in this study includes segmentation (clustering and profiling) of existing consumers and classification-prediction of segments for existing and new customers. Both a combination and an integration of soft computing techniques were used in the proposed model. Clustering was performed via a proposed neuro-fuzzy two stage-clustering approach and classification-prediction was employed using a supervised artificial neural network method. Segmenting customers was done according to the purchasing behaviours of customers based on RFM (Recency, Frequency, Monetary) values, which can be considered as an important variable set in identifying customer value. The model was also compared with other two-stage methods (i.e., Ward’s method followed by k-means and self-organising maps followed by k-means) based on select segmentability criteria. The proposed model was employed in a secondary data set from a UK retail company. The data set included more than 300,000 unique customer records and a random sample of approximately 1% of it was used for conducting analyses. The findings indicated that the proposed model provided better insights and managerial implications in comparison with the traditional two-stage methods with respect to the select segmentability criteria. The main contribution of this study is threefold. Firstly it has the potential benefits and implications of having fuzzy segments, which enables us to have flexible segments through the availability of membership degrees of each customer to the corresponding customer segments. Secondly the development of a new two-stage clustering model could be considered to be superior to its peers in terms of computational ability. And finally, through the classification phase of the model it was possible to extract knowledge regarding segment stability, which was utilised to calculate customer retention or churn rate over time for corresponding segments.