Be Customer Wise or Otherwise: Combining Data Mining and Interactive Visual Analytics to Analyse Large and Complex Customer Resource Management (CRM) Data

• Main Authors: KAM, Tin Seong, MISRA, Aditya Hridaya, JI, Jun Yao
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2013
• Subjects: Customer Analytics; Cluster Analysis; customer segmentation; Customer Live-time Value; RFM; data visualization; data mining; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/2051; http://support.sas.com/resources/papers/proceedings13/105-2013.pdf
• Institution: Singapore Management University

In this competitive world, more and more companies, such as our project sponsor, a global logistics company, are exploring the potential use of data mining techniques to make informed and intelligent marketing strategies. We conducted a customer segmentation study using a comprehensive set of customer transaction and profile data. This paper aims to report on our experience gained in using the interactive visual analytics and data mining techniques of SAS® JMP to perform customer segmentation analysis in combination with RFM (Recency, Frequency and Monetary), a method used for determining the Customer Lifetime Value (CLV). We share our views on how interactive visual analytics and data mining techniques can empower everyday data analysts to gain useful insights and formulate informed decisions by demonstrating the interactive data visualization techniques of JMP such as graph builder and parallel plots.