Abstract
The Occurrence of the recent economic and social changes transformed the retail sector in particular the relationship between the customers and the retail stores changed significantly. In the past the retail industry focused on marketing the products without having detailed knowledge about the customers who availed products. With the proliferation of competitors the retail stores had to target on retaining their customers. To be successful in today’s competitive environment retail stores must creatively and innovatively meet their customer needs and expectations. Generic mass marketing messages are irrelevant. This paper put forwards, a new approach of customer classification based on the RFM(Mode) model and also deals with customer data to analyze and predict the customer behavior using clustering and association rule mining techniques.
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Abirami, M., Pattabiraman, V. (2016). Data Mining Approach for Intelligent Customer Behavior Analysis for a Retail Store. In: Vijayakumar, V., Neelanarayanan, V. (eds) Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC – 16’). Smart Innovation, Systems and Technologies, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-319-30348-2_23
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DOI: https://doi.org/10.1007/978-3-319-30348-2_23
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