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RFAT customer segmentation based on improved K-means algorithm


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RFAT customer segmentation based on improved K-means algorithm
Liu Zhiyi1Chen Gong2
1.Department of Computer Science and Information Engineering; 2.Department of Electronic Information and Electrical Engineering,Changzhou Institute of Technology,Changzhou,213002,China
customer classification recency frequency average monetary trentd K-means algorithm initial clustering centers number of clusters
The traditional K-means algorithm has sensitivity to the initial cluster centers,meanwhile it is difficult for users to determine the optimal number of clusters in advance.In order to solve these problems,a new improved K-means algorithm is proposed here.The algorithm can optimize the initial center points through computing the maximum distance of objects.At the same time,it can find the optimal number of clusters by using a new evaluation function.The results can reduce the dependence on the parameters.When the improved algorithm is used to analyze customers of a firm,the RFAT customer classification model is proposed.The new model has four segmentation variables to assess the customer's value:Recency,Frequency,Average Monetary and Trend.The customers RFAT-value is analyzed by using clustering.The business strategy for different customer groups is also pointed out.The application results show that the RFAT model and the improved K-means algorithm proposed here can classify customers effectively.It also can fully reflect the customer's current value and appreciation potential.


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Last Update: 2014-08-31