Data mining for customer segmentation and profiling: a case study for a fast moving consumer goods (FMCG) company
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Date
2006.
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Thesis (M.A.)-Bogazici University. Institute for Graduate Studies in Social Sciences, 2006.
Abstract
Data mining is a process of extracting hidden information from largedatabases by analyzing data from different perspectives. Segmentation and profiling analyses are data mining applications used to detect valuable customers of companies. Determining discrete valuable customer segments allows companies tofocus on these groups and reallocate their limited sources to serve them.The aim of this study is to propose a base for the customer relationship management activities by using data mining tools and applications for a FMCG company. Customer master data and sales transactions of customers are converted tomeaningful information that can be used for customer relationship management activities. Customer segments and city segments are constructed using the buyingbehavior data of customers as the input. Nonhierarchical clustering algorithm is used to implement the segmentation analyses. Profiles of customer and city segments aredefined using the characteristics of customers included in these segments.Results of the customer and city segmentation analyses are combined bydeveloping a new reporting environment with OLAP functionalities. Meaningful information obtained at the end of the analyses will help company to developeffective customer relationship management activities focusing on the valuable customers and valuable cities which will result in increasing the long term profitability of the company.