Data preparation for marketing database creation: A case study - Pinto, Filipe (2005)
To increase effectiveness in their marketing and CRM activities many organizations are adopting strategies of Database Marketing (DBM). DBM faces today new challenges in business knowledge. Currently DBM strategies are mainly approached by classical statistical inference, which may fail when complex, multi-dimensional, and incomplete data is available. An alternative is to use Knowledge Discovery from Databases (KDD), which aims at automatic pattern extraction using Data Mining (DM) techniques. The patterns identified can be applied to the efficient characterization of the customers and to the database filtering process. This paper focus the problems commonly encountered in the data pre-processing, necessary to the success of the DM in a DBM project, through a case study.