Data mining and data based direct marketing activities

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.