Abstract
Purpose
This study is motivated by the importance of startups in economic growth and the need for methods to evaluate their success, considering risk and uncertainty. The objective is to analyze factors that influence startups, using factor and cluster analysis. The hypothesis that advanced business analytics in startup evaluation can enhance the quality of investment decision-making was tested.
Methods
The combination of quantitative and qualitative techniques was used. Statistics about 20 startups from Latvia, Lithuania, and Estonia over five years were processed to identify success drivers and to group startups by similarity. Machine learning and social media sentiment analysis were applied to assess non-financial indicators.
Results
The results showed that indicators such as projected profitability, social media activity, and innovativeness are significant for startup ranking. The share of traditional methods in the Baltic states was 55%, while modern tools were 45%, highlighting the role of digitalization in risk assessment. Startups with high clustering coefficients and positive mention sentiment demonstrated superior performance.
Conclusions
The study demonstrated that integrating business analytics and digitalization enhances startup evaluation. The model combines financial metrics with network and sentiment analysis, offering a comprehensive framework for investors. It confirms that data-driven methods improve decision-making, reducing investment risks.