Segmenting Customers for Marketing Success: A Study Using Retail Data

Purpose: Customer segmentation is a critical aspect of marketing strategy, allowing businesses to tailor their offerings to distinct groups of consumers based on behavioral, demographic, and transactional attributes. This paper explores data-driven customer segmentation techniques.

Methodology: This paper uses a publicly available dataset from an online retail store. The dataset contains over 540,000 transactions, providing a rich source of information to segment customers based on their purchasing behavior. Recency, Frequency, and Monetary (RFM) analysis and clustering techniques have been applied, such as K-means to categorize customers into distinct segments. These segments are then analyzed to uncover key insights about customer behavior, including product preferences, purchasing frequency, and spending patterns.

Findings: The results demonstrate the power of data-driven segmentation in improving targeted marketing efforts, boosting customer retention, and optimizing resource allocation.

Unique Contribution to Theory, Practice and Policy: This study provides a framework for retailers and marketers to enhance their customer segmentation strategies, ultimately improving business outcomes through personalized marketing campaigns.