SYSTEMATIC APPROACHES TO IDENTIFY AND START PROFITABLE PRODUCTS ON AMAZON: KEY CRITERIA, RESEARCH METHODS AND FINANCIAL ANALYSIS

Abstract

The study focuses on data-driven product selection for Amazon entrepreneurship. Most scholarly work on this domain isolates single levers—keywords, pricing, or competitor benchmarking—yet offers no integrative roadmap that marries market intelligence with operational feasibility. This fragmentation leaves small- and medium-sized sellers ill-equipped to navigate saturated, policy-heavy niches. To bridge this gap, the study formulates and tests a five-stage product-selection framework designed to convert scattered insights into a coherent, data-driven decision flow. We fuse qualitative screening with quantitative modelling: Stage 1 filters categories for structural entry barriers; Stage 2 validates sub-niches via sentiment- and trend-analysis; Stage 3 stress-tests keyword relevance; Stage 4 profiles competitors across lifecycle and compliance variables; and Stage 5 runs a Monte-Carlo-based financial feasibility model that embeds FBA fees, ad-spend volatility, and logistics shocks. Data were captured with Helium 10, Google Trends, and MerchantWords, then triangulated against recent peer-reviewed literature (2019–2025). Sellers relying on ad-hoc intuition face a 2.8-times higher risk of entering over-saturated niches and a 37% lower probability of breakeven within six months. Conversely, the integrative framework improved market-entry accuracy by 46% and increased forecasted profit margins by 18% under median-case scenarios, with sensitivity tests confirming that dynamic cost inputs—rather than keyword volume—drive most forecast error. The study is the first to operationalise a holistic Amazon product-selection protocol that simultaneously accounts for demand signals, competitive intensity, and cash-flow resilience, offering a replicable template for SMEs and groundwork for future AI-augmented recommendation engines.