Data-Driven Decision Making on Amazon: A Methodology for Assessing Product Potential and Competition

• Autor: Korshun, Artem
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Universidad Nacional de Ingeniería
• Repositorio: Revistas - Universidad Nacional de Ingeniería
• Lenguaje: español
• OAI Identifier: oai:oai:revistas.uni.edu.pe:article/2598
• Enlace del recurso: https://revistas.uni.edu.pe/index.php/iecos/article/view/2598
• Nivel de acceso: acceso abierto
• Materia: Decision-making; Competence; Methodology; Toma de decisiones; Competencia; Metodología

Amazon Marketplace, especially for small and medium-sized businesses, represents a strategic tool for selecting and positioning products. While research exists on market access criteria, evaluating a product's potential and the competition requires more precise approaches. This study proposes an integrated methodological framework based on three axes: keyword relevance, market size, and level of competition, adapted to the context of sellers on Amazon. The methodology combines quantitative and qualitative techniques. Tools such as Helium 10 and Keepa are used to analyze keywords, pricing, and competitive dynamics, along with market research. Keyword frequency analysis and the identification of commercial terms allow for measuring visibility and opportunities of interest. Subsequently, financial viability is assessed by considering profit margins and inventory turnover. Keepa helps identify pricing strategies, competitor longevity, and niche sustainability, while Helium 10 detects pricing anomalies and unethical practices. The results show that the correct selection of keywords directly impacts visibility, and that accurate market size estimation reduces risks in saturated or declining niches. Niches with moderate competition, favorable financial metrics, and keyword relevance between 30% and 60% were identified, ensuring greater stability and conversion. In conclusion, this methodological framework offers a clear and strategic guide for Amazon sellers, addressing shortcomings in product and competitor evaluation, and proposes future studies with predictive tools to optimize the accuracy and scalability of keyword selection.