Real-Time Customer Data Analytics and Marketing Agility: A Study of E-Commerce SMEs in the Post-Pandemic Market

• Abbas Ahsun

  Texas University

  Author

The post-pandemic e-commerce landscape has intensified competitive pressures on small and medium enterprises (SMEs), yet many continue to rely on retrospective reporting and intuition-driven marketing decisions. While large firms have increasingly adopted real-time customer analytics to enhance marketing agility, SMEs face persistent resource constraints and a scarcity of validated, scalable frameworks tailored to their operational realities. This study addresses this gap by proposing and validating a real-time customer data analytics framework designed specifically for e-commerce SMEs, integrating transaction-level behavioral signals with an RFM-based segmentation engine and a gradient-boosted predictive layer. Using a design-science methodology combined with retrospective transaction data from 847 e-commerce SMEs and prospective simulation, the framework was tested against baseline static marketing approaches. Results indicate that the proposed framework achieved 89.4% accuracy in predicting segment-level campaign response, a 31.2% reduction in campaign adjustment lead time, and a 22.7% improvement in marketing return on investment relative to static baselines. The findings demonstrate that real-time analytics capability functions as a dynamic capability enabling SMEs to compress decision cycles and deploy marketing resources with greater precision. The study contributes a replicable, resource-efficient framework and offers actionable guidance for SME managers seeking to operationalize customer data without substantial technological investment.

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Copyright (c) 2026 Abbas Ahsun (Author)

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