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Introduction

In today’s digital age, companies are constantly searching for ways to effectively target and reach their customers to increase sales and brand loyalty. One popular method to achieve this is through customer segmentation, which divides a heterogeneous market into smaller, more manageable segments based on similar characteristics and behaviors. By understanding the unique needs and preferences of each segment, companies can tailor their marketing strategies to effectively target and engage with their customers.

One approach to customer segmentation is through the use of clustering algorithms, which group customers based on similarities in their browsing history, purchasing behavior, and demographics. By leveraging the wealth of data available online, companies can create targeted advertising campaigns that resonate with their audience and drive conversions.

This thesis explores the use of clustering algorithms and browsing history data for customer segmentation in targeted advertising. By analyzing the browsing patterns of customers, companies can gain valuable insights into their interests, preferences, and purchase intentions. Through the use of clustering algorithms, customers can be grouped into segments that exhibit similar behaviors, allowing companies to deliver personalized and relevant advertisements to each group.

Chapter 1: Introduction

1.1 Introduction
1.2 Background of the study
1.3 Problem Statement
1.4 Objective of the study
1.5 Limitation of the study
1.6 Scope of the study
1.7 Significance of the study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review

2.1 Overview of customer segmentation
2.2 Clustering algorithms in customer segmentation
2.3 Browsing history data analysis
2.4 Targeted advertising strategies
2.5 Personalization in marketing
2.6 The impact of customer segmentation on advertising effectiveness
2.7 Challenges in customer segmentation
2.8 Case studies on successful customer segmentation strategies
2.9 Future trends in customer segmentation
2.10 Summary of literature review

Chapter 3: Research Methodology

3.1 Research design
3.2 Data collection methods
3.3 Data preprocessing techniques
3.4 Clustering algorithms selection
3.5 Evaluation metrics
3.6 Data analysis techniques
3.7 Validation methods
3.8 Ethical considerations in data analysis

Chapter 4: Discussion of Findings

4.1 Analysis of browsing history data
4.2 Customer segmentation using clustering algorithms
4.3 Effectiveness of targeted advertising campaigns
4.4 Comparison of different clustering algorithms
4.5 Evaluation of segmentation results
4.6 Recommendations for future research
4.7 Implications for marketing practice
4.8 Limitations of the study

Chapter 5: Conclusion and Summary

5.1 Summary of key findings
5.2 Contributions to the field
5.3 Practical implications
5.4 Recommendations for future research
5.5 Conclusion

Thesis Overview:

In this thesis, we delve into the realm of customer segmentation for targeted advertising using clustering algorithms and browsing history data. The study aims to explore the effectiveness of clustering algorithms in segmenting customers based on their browsing history and behaviors, and how these segments can be leveraged for personalized and targeted advertising campaigns. The research methodology involves data collection, preprocessing, clustering algorithm selection, and evaluation metrics for assessing the segmentation results. Through a comprehensive literature review and analysis of findings, this thesis provides insights into the impact of customer segmentation on advertising effectiveness and offers recommendations for future research in this area.

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