УДК 338
DOI: 10.36871/ek.up.p.r.2026.02.09.027

Авторы

Al-Shaibani Eskander Taher Saif,
Peoples’ Friendship University of Russia (RUDN University), Moscow, Russia

Аннотация

This article develops and empirically validates a hybrid customer segmentation framework for e-commerce that integrates Recency–Frequency–Monetary (RFM) analysis with unsupervised K-means clustering and non-transactional behavioral signals. The framework is applied to the Brazilian E-Commerce Public Dataset by Olist — approximately 96,000 unique customers, 100,000 completed orders, from January 2017 to August 2018. Descriptive analysis confirms that 87.4% of customers purchased exactly once, the top 10% of customers generated approximately 45% of total revenue, and the Pareto concentration ratio (top 20% → ~72% of revenue) substantially exceeds typical benchmarks. Customer experience signals (aggregated review ratings) and payment-method patterns are shown to improve segment interpretability beyond transactiononly RFM, particularly by separating high-value dissatisfied customers — identified as a distinct archetype representing approximately 3% of the customer base and ~15% of platform revenue — from high-value loyal customers. The K-means solution with five clusters, validated using the silhouette coefficient and Davies– Bouldin index, produces actionable segment profiles that map directly to differentiated retention, recovery, and conversion strategies. The article concludes with segment-specific managerial recommendations and discusses implications for competitive strategy in high-churn emerging-market e-commerce environments.

Ключевые слова

E-commerce, customer segmentation, RFM analysis, K-means clustering, customer experience, online reviews, payment behavior, retention, churn risk, marketing analytics

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