Segmenting Customers for Marketing Success: A Study Using Retail Data
DOI:
https://doi.org/10.47941/jbsm.2271Keywords:
Customer Segmentation, Retail Data, RFM analysis, Clustering, Marketing StrategyAbstract
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.
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Copyright (c) 2020 Saurabh Kumar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
