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 the reader to essential and detailed aspects of both fields for understanding the intentions.
The empirical part contains a short introduction to each company within the thesis, and short summaries of the interviews conducted. In the following analysis part the authors have created a model to make the possible uses of Data Mining more understandable to the reader. Furthermore, this part contains an analysis of the interviews in relation to the topic at hand and the theories used. In the conclusions the authors answer their research question, namely; what are the main advantages and disadvantages of Data Mining as support to Direct Marketing activities? In the absolute end of the thesis the authors criticise their own work and give suggestions for further research within the topic.
Inhaltsverzeichnis (Table of Contents)
- Introduction
- Background
- Problem Discussion
- Problem Formulation
- Purpose of this Thesis
- Limitations
- Theoretical Relevance
- Practical Relevance
- Methodology
- Preunderstanding
- Research Journey
- Paradigms
- The subjective – objective dimension
- Ontology: Nominalism vs. Realism
- Epistemology: Anti-positivism vs. Positivism
- Human Nature: Voluntarism versus Determinism
- Methodology: Ideographic versus Nomothetic
- Radical change- Regulation
- Scientific approach
- Theory
- Data Mining
- Data Mining Methods
- Data Clustering
- Classification
- Modes of Data Mining
- Descriptive Data Mining
- Predictive Data Mining
- Overview of Data Mining Techniques
- Market Basket Analysis/Association Mining
- Artificial Neuronal Nets
- Traditional Statistical Approach
- Data Warehouse
- Direct Marketing
- Benefits and Growth of Direct Marketing
- Customer Databases
- Database Marketing
- Mass Marketing versus One-to-One Marketing
- Major Channels of Direct Marketing
- Direct Mail
- Catalogue Marketing
- Telemarketing
- Ethical Issues in Direct Marketing
- Empirical Part
- Telia
- Skandia
- SEB (Skandinaviska Enskilda Banken)
- TUI Deutschland GmbH
- Kreissparkasse Grafschaft Diepholz
- OLB (Oldenburgische Landesbank AG)
- Analysis
- Usage of Data Mining today
- Further Development of Data Mining
- Main Advantages of Data Mining
- Main Disadvantages of Data Mining
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This master thesis aims to explore the use of Data Mining as a tool for supporting Direct Marketing activities. The authors investigate the advantages and disadvantages of using Data Mining in a business context, focusing on specific companies' experiences and perspectives. They aim to provide a comprehensive overview of the current usage and future development of Data Mining within direct marketing strategies.
- The role of Data Mining in Direct Marketing
- Benefits and drawbacks of implementing Data Mining technologies for businesses
- Case studies from various companies utilizing Data Mining techniques
- Analysis of the current and future potential of Data Mining within Direct Marketing
- Ethical implications of Data Mining in a marketing context
Zusammenfassung der Kapitel (Chapter Summaries)
The thesis begins by introducing the topic of Data Mining and Direct Marketing, establishing the context and outlining the problem discussed throughout the work. Chapter 2 delves into the methodology used, exploring different theoretical perspectives and approaches related to the research. Chapter 3 delves into the theoretical foundations of Data Mining and Direct Marketing, explaining important concepts, methods, and techniques used in both fields. This chapter focuses on providing a detailed overview of Data Mining techniques, outlining various methods and approaches, and analyzing their applications in Direct Marketing. Chapter 4 presents the empirical part of the study, focusing on individual case studies from different companies. Each company's use of Data Mining, its specific implementation, and the advantages and disadvantages experienced are discussed. The authors then analyze these case studies and present their findings in Chapter 5. This analysis focuses on the current usage and future development of Data Mining, highlighting both the advantages and disadvantages of its application. The authors offer their conclusions, drawing on their research findings and discussing potential criticisms of their work. Finally, they suggest avenues for further research within the field.
Schlüsselwörter (Keywords)
Data Mining, Direct Marketing, Customer Databases, Market Basket Analysis, Artificial Neuronal Nets, Telemarketing, Ethical Issues, Case Studies, Business Applications, Data Analysis, Marketing Strategies.
- Quote paper
- T. Brüggemann (Author), P. Hedström (Author), M. Josefsson (Author), 2004, Data mining and data based direct marketing activities, Munich, GRIN Verlag, https://www.grin.com/document/20798
