A Data-driven Evaluation of Early-Stage Startups based on the Integrative Framework of the Balanced Scorecard and RFM Analysis

Identifying promising startups and supporting investment decisions has long been a critical research focus. Among various approaches, the MCDM-based scoring method has gained attention for incorporating multidimensional factors with interpretability. However, the absence of a structured framework for selecting evaluation criteria limits its ability to capture the distinct characteristics of startups. To address, this study proposes an integrated framework employing the balanced scorecard (BSC) to organize evaluation dimensions in a systematic manner. For each perspective, RFM-based methods are developed to assess elements such as investment history and human capital maturity. The proposed integrative framework enables a more systematic and effective assessment of early-stage startup, contributing to strategic decision making in high uncertainty environments.