Data-Driven AI Product Roadmap Prioritization for SaaS Companies: A Valuation-Based Framework - Ozbayburtlu, Bora; Balcioglu, Yavuz Selim; Sezen, Bulent (International Journal of Basic and Applied Sciences, 2025)
This study develops a valuation-based framework for prioritizing artificial intelligence investments in Software-as-a-Service product ‎roadmaps by empirically examining the relationship between company fundamentals and market valuations. Using a dataset of 94 SaaS ‎companies across 82 industry classifications, the research employs machine learning techniques including ElasticNet and Random Forest ‎models to analyze valuation drivers and unsupervised clustering to identify company archetypes. The study successfully addresses research ‎questions concerning fundamental valuation patterns, company classification, and industry-level valuation dynamics. The clustering analysis ‎identified three distinct SaaS company archetypes with meaningful business model characteristics following resolution of data parsing errors ‎that initially produced implausible values. Archetype 0, designated Established Enterprises, comprises thirty companies averaging 5.25 bil-‎lion dollars in annual recurring revenue with 18,500 employees, including industry leaders such as Microsoft, Salesforce, and Oracle. Ar-‎chetype 1, representing High-Growth Scalers, contains fifty-seven companies averaging 842 million dollars in revenue with 2,400 employ-‎ees, demonstrating efficient unit economics with revenue per employee of approximately 351,000 dollars. Archetype 2, comprising Emerg-‎ing Ventures, includes seven companies averaging 135 million dollars in revenue with 450 employees, representing earlier-stage market ‎entrants establishing presence in specialized segments. These empirically validated archetypes provide a foundation for tailoring AI invest-‎ment strategies to company maturity stage and resource constraints‎.
Keywords: Saas Valuation; AI Product Roadmap; Machine Learning; Enterprise Value Drivers; Strategic Product Management; Data Quality İn Financial Modeling; Company Archetypes.