Επιχειρηματική αναλυτική στην πράξη : τμηματοποίηση πελατειακής βάσης και ανάλυση καλαθιού αγορών για ηλεκτρονικό κατάστημα αλυσίδας στο χώρο του λιανεμπορίου
Business analytics in practice : customer segmentation and e-shop market basket analysis
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Keywords
Τμηματοποίηση πελατών ; Τμηματοποίηση πελατειακής βάσης ; Ανάλυση καλαθιού αγοράς ; Customer segmentation ; Market basket analysis ; e-Shop ; DeliveryAbstract
In an era of data-driven decision-making, businesses increasingly rely on advanced analytics to
gain insights into customer behavior and optimize operational strategies. This thesis explores two
fundamental methodologies in customer analytics: Customer Segmentation and Market Basket
Analysis (MBA), applied to a real-world dataset from an online supermarket delivery platform.
The study begins with an Exploratory Data Analysis (EDA) to understand transaction patterns,
customer behaviors, and product sales distribution. The Customer Segmentation approach
utilizes the RFM (Recency, Frequency, Monetary) model combined with clustering algorithms
such as K-Means and DBSCAN to categorize customers into meaningful segments. The results
classify customers into Loyalists, Potential Loyalists, and Churners, enabling targeted marketing
strategies.
Subsequently, the Market Basket Analysis employs association rule learning techniques,
specifically Apriori and FP-Growth algorithms, to uncover purchase correlations and product
affinities. The analysis identifies significant purchasing patterns and suggests optimal product
bundling strategies to enhance cross-selling and upselling opportunities.
Comparative evaluations between clustering techniques and association rule methods provide
insights into the strengths and limitations of each approach. Additionally, findings from the
segmentation analysis are integrated into the MBA process, offering cluster-specific
recommendations to optimize business outcomes.
The thesis concludes with a synthesis of key findings, outlining actionable business strategies
for customer retention, personalized marketing, and revenue maximization. By leveraging data
driven methodologies, this study illustrates the potential of customer analytics to enhance
strategic decision-making in the online retail sector.
Postgraduate Studies Programme
Κυβερνοασφάλεια και Επιστήμη ΔεδομένωνDepartment
Σχολή Τεχνολογιών Πληροφορικής και Επικοινωνιών. Τμήμα ΠληροφορικήςNumber of pages
117Language
GreekCollections
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Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα
Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα