Startup success metrics: comprehensive analysis, contextual adaptation, and application issues

Startups are an integral part of the modern economy, acting as a driving force for innovation and economic dynamism. However, their activity is characterized by a high level of uncertainty and rapid evolution, which poses significant challenges for performance evaluation and risk management. In this context, the use of metrics becomes a key instrument for making objective, data-driven decisions, particularly for software startups operating under limited resources and constant change. Despite the recognized importance of metrics, the academic literature concerning their application specifically in the context of software startups remains underexplored, indicating both the relative novelty of the topic and a growing need for research in this niche. This article aims to systematize and summarize current knowledge on key startup metrics, their application, and their relevance for performance evaluation and strategic decision-making. Special attention is given to how the relevance of metrics varies depending on the startup’s context, such as business model type and stage of its life cycle. The feasibility of using economic and mathematical modeling as a basis for integrating heterogeneous metrics into a unified aggregated indicator is substantiated, enabling the formalization of the decision-making process. Four main groups of metrics are identified: financial, customer-centric, product, and market. Unresolved issues and gaps in metric usage by startups are identified and discussed, particularly the low level of metric implementation despite the availability of modern tools for data collection and analysis. The study confirms the core logic of the Lean Startup theory and emphasizes the need for an approach to metric use that accounts for contextual dependence and temporal evolution. This research is a vital step toward closing the knowledge gap in how startups can use data to navigate uncertainty and achieve success. The article systematizes knowledge about key startup metrics and their application in performance evaluation. The results enable transparent and objective assessments of startup status for informed decision-making. Keywords: startup, performance metrics, economic-mathematical modeling, composite indicator, financial indicators, managerial decisions.