Treffer: Analyzing customer spending patterns and buying preferences with data mining techniques

Apart from using information systems for management of procurement data, sales and purchase transactions, warehouse operations, finance and human resources, information technologies are increasingly used for analysis, planning and control of business processes. In contemporary economy companies need various business intelligence techniques for analyzing business data, such as sales revenue or production costs. The term business intelligence (BI) was first introduced by Howard Dresher, Gartner Group analyst, to denote “concepts and methods to improve business decision making by using fact-based support systems”. Business intelligence as it is understood today provides tools for online analytical processing, data mining, process mining, business performance management and predictive analytics. Numerous data mining methods are used for marketing, sales and customer support: market basket analysis, clustering, neural networks, decision trees, genetic algorithms, association rules, statistical methods, etc. There are a lot of programming tools for data mining present on the market, produced by leading software companies. For example tools which are part of the statistical program packages, like Enterprise Miner (SAS) and Clementine (SPSS), specialized tools for general or business usage, such as Intelligent Miner (IBM) and Data Miner (SAS), OLAP tools, as Hiperion, Pentaho and IBM Cognos Business Intelligence. Some DBMS include data mining tools, as for exmple Microsoft SQL Server Business Intelligence and Oracle Data Mining suit Darwin. Besides the above mentioned tools, there are many others on the market such as: Advanced Miner, Affinium Model, DataDetective, DataLab, Kalidara Advisor, XLMiner and open source data mining systems as WEKA, Orange, Tanagra, Rapid Miner, KEEL, KNIME, MiningMart, MLC ++. In this paper we are considering the implementation of methodologies for market segmentation, discovering the profile of typical customers for particular kind of products, their buying preferences and cross selling ...