Enhancing Customer Segmentation Strategies in E-commerce through Data Analytics with SSBI

Nimisha Thiruvangadan, B.Tech.

Diplomová práce

Abstract:

In today’s competitive e-commerce environment, understanding customer behavior is essential for improving engagement and retention. This thesis focuses on using Self-Service Business Intelligence (SSBI) tools specifically Microsoft Power BI to perform customer segmentation. The analysis is based on the RFM (Recency, Frequency, Monetary) model, which groups customers by their purchase activity and value. The methodology includes data cleaning, model development, and visualization using interactive dashboards. Customers were segmented into eight categories, helping to tailor marketing strategies effectively. Geographic and behavioral trends were also explored to support CRM efforts. The results show that RFM analysis provides a practical, scalable way to identify valuable customer groups and re-engage inactive users. Power BI’s real-time capabilities enhanced decision-making and allowed for deeper insight into purchasing behavior. This approach offers a low-complexity, user-friendly framework for marketers. The research demonstrates how SSBI can be integrated into traditional analytics practices to support targeted marketing and strategic planning. Future enhancements could involve predictive analytics and real-time data streaming for even more refined segmentation.

Keywords

RFM Analysis, Customer Segmentation, Self Service Business Intelligence(SSBI), Power BI, E-Commerce Analytics

Jazyk práce: angličtina

Datum vytvoření / odevzdání či podání práce: 3. 4. 2025

Obhajoba závěrečné práce

• Obhajoba proběhla 5. 6. 2025
• Vedoucí: doc. Ing. Jan Tyrychtr, Ph.D.
• Oponent: Richard Vágner, externi

Česká zemědělská univerzita v Praze

Provozně ekonomická fakulta

Magisterský studijní program:
Informatics