Journal of Marketing Analytics

, Volume 3, Issue 3, pp 110–121 | Cite as

New approach to customer segmentation based on changes in customer value

Original Article

Abstract

In today’s fast moving world of marketing from product-orientation to customer-orientation, the management of customer treatment can be seen as a key to achieve revenue growth and profitability. Knowledge of customer behavior can help marketing managers re-evaluate their strategies with the customers and plan to improve and expand their application of the most effective strategies. B2B or business customers are more complex, their buying process is more complicated and their sales value is greater. The business marketers usually prefer to cooperate with fewer but larger buyers than the final consumer marketer. As a business transaction requires more decision makings and more professional buying effort than the consumer market does, the efficient relationship with business customers is of paramount importance. Most customer segmentation approaches based on customer value fail to account for the factor of time and the trend of value changes in their analysis. In this article, we classify customers based on their value using the RFM model and K-means clustering method. Then, an assessment of changes over several periods of time is carried out. The originality of this research lies in its incorporation of time and trend of customer value changes in improving the accuracy of predictions based on the past behavior of customers. For this purpose, we used the POS customer transactions.

Keywords

segmentation Customer lifetime value Temporal Data Mining RFM model 

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Copyright information

© Palgrave Macmillan, a division of Macmillan Publishers Ltd 2015

Authors and Affiliations

  1. 1.K. N. Toosi University of TechnologyTehranIran

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