Intelligent decision-making system for jewelry retail prediction and inventory management integrating CRM data

Ting-Ting Han ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 5, 1576-1592

Abstract: This study develops an intelligent decision support system that integrates Customer Relationship Management (CRM) data with inventory optimization to address the unique challenges of jewelry retail, characterized by high-value merchandise, emotional purchasing patterns, and seasonal demand fluctuations. A case-based empirical approach using semi-structured interviews and field observations (qualitative) alongside analysis of sales data, inventory records, and customer transactions (quantitative) is employed to evaluate the customer-centric inventory management system. Empirical evaluation in a mid-sized jewelry retail environment demonstrated significant performance improvements: a 23.5% increase in inventory turnover, 38.7% fewer stockout events, and a 14.6% higher customer satisfaction compared to control stores. The system enabled a "less inventory, better service" strategy, reducing total inventory by 12.3% while increasing product availability for high-value customers by 27.5%. The integration of CRM data with inventory management creates a transformative approach to retail operations, shifting from product-oriented to customer-oriented decision-making while simultaneously improving financial and service metrics. With a demonstrated ROI of 167% and an 18.6-month payback period, this study provides both a theoretical framework for blending customer data with inventory control decisions and a practical implementation guide for specialty retail environments.

Keywords: CRM data integration; Customer-centric inventory; Intelligent decision-making system; Inventory optimization; Jewelry retail management; Predictive analytics; Retail digital transformation. (search for similar items in EconPapers)
Date: 2025
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