Data mining and data based direct marketing activities

ISBN-13: 9783638700900 / Angielski / Miękka / 2007 / 68 str.

T. Bruggemann; P. Hedstrom; M. Josefsson

Opis

Master's Thesis from the year 2004 in the subject Business economics - Marketing, Corporate Communication, CRM, Market Research, Social Media, grade: 1,7 (A-), Vaxjo University (School of Management and Economics), course: International Business Environment, 40 entries in the bibliography, language: English, abstract: Widespread changes within business environments in recent years has demanded acquisitions of new tools that are more skilled to cope with new challenges and demands in business. Advances in computer technologies, higher accessibility of computer associated tools and decreased prices of general computer-related products are reasons enough for at least considerations about higher usage of new technologies. Particularly in direct marketing activities discussed technology is called Data Mining. Companies are faced with hosts of data collected in their data repositories. Of course, companies want to make use of their data and aim to discover interesting patterns of knowledge within their data repositories. Direct marketers which can be involved in catalogue marketing, telemarketing or widely known direct-mail marketing are intensive users of Data Mining Technologies. Because of that, the authors strive to do research concerning reasons for and advantages and disadvantages with using Data Mining as support for direct marketing activities. Chapter 1 deals with general information for the reader as support for delving into the topic. The included problem discussion finishes with the final problem formulation of this thesis. Chapter 2 is about the Methodology which includes considerations of Gummesson. The following theoretical part is divided into two major parts, Data Mining and Direct Marketing, which underpin the whole thesis. The authors want to inform the reader about important and sophisticated contents concerning both Data Mining and Direct Marketing. Without overloading the implementations about Data Mining and Direct Marketing, the authors conduct t