Data mining and data based direct marketing activities

Table of Contents

1.Introduction

1.1 Background

1.2 Problem Discussion

1.3 Problem Formulation

1.4 Purpose of this Thesis

1.5 Limitations

1.6 Theoretical Relevance

1.7 Practical Relevance

2. Methodology

2.1 Preunderstanding

2.2 Research Journey

2.3 Paradigms

2.4 The subjective – objective dimension

2.4.1 Ontology: Nominalism vs. Realism

2.4.2 Epistemology: Anti-positivism vs. Positivism

2.4.3 Human Nature: Voluntarism versus Determinism

2.4.4 Methodology: Ideographic versus Nomothetic

2.4.5 Radical change- Regulation

2.4.6 Scientific approach

3. Theory

3.1. Data Mining

3.1.1 Data Mining Methods

3.1.1.1 Data Clustering

3.1.1.2 Classification

3.1.2 Modes of Data Mining

3.1.2.1 Descriptive Data Mining

3.1.2.2 Predictive Data Mining

3.1.3 Overview of Data Mining Techniques

3.1.3.1 Market Basket Analysis / Association Mining

3.1.3.2 Artificial Neuronal Nets

3.1.4 Traditional Statistical Approach

3.1.5 Data Warehouse

3.2. Direct Marketing

3.2.1 Benefits and Growth of Direct Marketing

3.2.2 Customer Databases

3.2.2.1 Database Marketing

3.2.2.2 Mass Marketing versus One-to-One Marketing

3.2.3 Major Channels of Direct Marketing

3.2.3.1 Direct Mail

3.2.3.2 Catalogue Marketing

3.2.3.3 Telemarketing

3.2.4 Ethical Issues in Direct Marketing

4. Empirical Part

4.1 Telia

4.2 Skandia

4.3 SEB (Skandinaviska Enskilda Banken)

4.4 TUI Deutschland GmbH

4.5 Kreissparkasse Grafschaft Diepholz

4.6 OLB (Oldenburgische Landesbank AG)

5. Analysis

5.1 Usage of Data Mining today

5.2 Further Development of Data Mining

5.3 Main Advantages of Data Mining

5.4 Main Disadvantages of Data Mining

6. Conclusion

6.1 Critics to own work

6.2 Suggestions for further research

Research Objectives and Topics

This thesis examines the utility of Data Mining (DM) technologies as a support mechanism for direct marketing activities. The primary research question addresses the identification of the main advantages and disadvantages companies face when implementing these tools to enhance their marketing effectiveness and customer targeting strategies.

• Integration of Data Mining with direct marketing strategies.
• Comparative analysis of traditional versus advanced data-driven marketing approaches.
• Evaluation of organizational challenges and technical requirements for DM implementation.
• Ethical considerations, including customer privacy and data security.
• Practical insights from various industry sectors (telecommunications, finance, travel).

Excerpt from the Book

Summary of Chapters

1. Introduction: This chapter provides fundamental information regarding Data Mining and its relevance in modern marketing environments, culminating in the formal problem statement of the thesis.

2. Methodology: This section details the research approach, including the authors' preunderstanding, the research journey, and the philosophical framework based on Burrell & Morgan’s dimensions.

3. Theory: This core chapter establishes the theoretical foundation by examining Data Mining techniques and the principles of Direct Marketing, highlighting the synergy between both fields.

4. Empirical Part: This section presents summarized findings from interviews conducted with professionals at six companies across different business sectors.

5. Analysis: The authors analyze the interview data, introducing a model (DM Flow-chart) to illustrate the different paths companies take when deploying DM in their marketing efforts.

6. Conclusion: This final chapter synthesizes the research findings, identifying the primary advantages and disadvantages of using DM in direct marketing, and provides critical reflection and suggestions for future research.

Keywords

Data Mining, Direct Marketing, Database Marketing, Knowledge Discovery, Association Rules, Market Basket Analysis, Customer Segmentation, Artificial Neural Networks, CRM, Marketing Efficiency, Consumer Behavior, Data Warehousing, Predictive Analytics.

Frequently Asked Questions

What is the core focus of this thesis?

The thesis focuses on evaluating the usage of Data Mining technologies as a supportive tool for direct marketing activities, specifically identifying key benefits and challenges.

What are the primary themes discussed?

Key themes include the synergy between data-based marketing and traditional methods, implementation hurdles, technical requirements, and the strategic importance of customer relationship management.

What is the central research question?

The authors seek to answer: What are the main advantages and disadvantages in using DM technologies as support to direct marketing activities?

Which methodology was applied?

The authors utilized an exploratory research design with a positivistic approach, conducting qualitative interviews with industry professionals across various sectors.

What is covered in the main body of the work?

The main body comprises a theoretical review of Data Mining and Direct Marketing, followed by an empirical analysis of real-world corporate practices and a concluding evaluation.

Which keywords best characterize this research?

Keywords include Data Mining, Direct Marketing, Database Marketing, Knowledge Discovery, Market Basket Analysis, and CRM.

How does Data Mining contribute to the "Market Basket Analysis"?

Data Mining identifies association rules within large datasets, allowing marketers to determine the likelihood of products being purchased together, thereby optimizing cross-selling strategies.

What ethical risks are associated with modern Data Mining in marketing?

The primary ethical risk identified is the potential invasion of privacy, as companies collect increasingly detailed personal data, potentially making customers feel exposed or suspicious of the marketer's knowledge.