Data-Driven Customer Loyalty Ranking Using SAW-Based Decision Support Framework - Diva Aurelza; Turkhamun Adi Kurniawan (Komunitas Dosen Indonesia, 2026)
In increasingly competitive business environments, maintaining customer loyalty has become a critical factor for sustaining long-term organizational performance. However, in many small and medium-sized enterprises, identifying loyal customers is still conducted subjectively, leading to inconsistent, non-transparent, and potentially biased reward allocation decisions. This study proposes a data-driven framework for customer loyalty ranking by integrating a Simple Additive Weighting (SAW)-based decision support approach. The research adopts a quantitative applied methodology using transactional data from a furniture retail business, covering a period of 12 months and involving 1,000 customer transactions. Customer loyalty is evaluated based on three key criteria: monetary value, purchase frequency, and payment reliability, which represent essential behavioral indicators of customer engagement. The SAW method is employed to normalize criteria values, assign relative weights, and compute preference scores for each customer, resulting in a systematic and objective ranking process. The proposed framework is implemented as a web-based decision support system using PHP with the CodeIgniter framework and a MySQL database to ensure structured data management and operational efficiency. The results demonstrate that the framework effectively produces consistent, transparent, and data-driven customer rankings, thereby reducing subjectivity in managerial decision-making. This study contributes by formalizing a practical decision support framework that enhances the reliability, fairness, and effectiveness of customer loyalty evaluation and reward allocation, offering a novel integration of data-driven decision-making paradigms with the SAW-based decision support system, a feature often underexplored in prior studies.