Data-Driven AI Product Roadmap Prioritization for SaaS Companies: A Valuation-Based Framework
-
https://doi.org/10.14419/k0sr8689
Received date: October 2, 2025
Accepted date: October 27, 2025
Published date: November 9, 2025
-
Saas Valuation; AI Product Roadmap; Machine Learning; Enterprise Value Drivers; Strategic Product Management; Data Quality İn Financial Modeling; Company Archetypes. -
Abstract
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.
-
References
- Arora, A., Branstetter, L., & Drev, M. (2019). Going soft: How the rise of software-based innovation led to the decline of returns to R&D. Man-agement Science, 65(2), 445-462.
- Audretsch, D. B., & Feldman, M. P. (2004). Knowledge spillovers and the geography of innovation. Handbook of Regional and Urban Economics, 4, 2713-2739. https://doi.org/10.1016/S1574-0080(04)80018-X.
- Babina, T., Fedyk, A., He, A., & Hodson, J. (2024). Artificial intelligence, firm growth, and product innovation. Journal of Financial Economics, 161, 103857. https://doi.org/10.1016/j.jfineco.2023.103745.
- Blaszczyk, P., Gerdsri, N., & Damrongchai, N. (2021). Applying digital technologies in technology roadmapping to overcome individual-biased assessments. Technological Forecasting and Social Change, 170, 120898.
- Bresnahan, T. F., & Trajtenberg, M. (1995). General purpose technologies: 'Engines of growth'? Journal of Econometrics, 65(1), 83-108. https://doi.org/10.1016/0304-4076(94)01598-T.
- Coad, A. (2010). Exploring the processes of firm growth: Evidence from a vector autoregression. Industrial and Corporate Change, 19(6), 1677-1703. https://doi.org/10.1093/icc/dtq018.
- Coad, A., Segarra, A., & Teruel, M. (2016). Innovation and firm growth: Does firm age play a role? Research Policy, 45(2), 387-400. https://doi.org/10.1016/j.respol.2015.10.015
- Criscuolo, C., Martin, R., & Overman, H. G. (2019). Some causal effects of an industrial policy. American Economic Review, 109(1), 48-85. https://doi.org/10.1257/aer.20160034.
- D'Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., ... & Sculley, D. (2022). Underspecification presents challenges for credibility in modern machine learning. Journal of Machine Learning Research, 23(226), 1-61.
- Damodaran, A. (2021). Investment valuation: Tools and techniques for determining the value of any asset (4th ed.). Wiley.
- Dichev, I. D., Graham, J. R., Harvey, C. R., & Rajgopal, S. (2013). Earnings quality: Evidence from the field. Journal of Accounting and Econom-ics, 56(2-3), 1-33. https://doi.org/10.1016/j.jacceco.2013.05.004.
- Giglio, S., Kelly, B., & Xiu, D. (2022). Factor models, machine learning, and asset pricing. Annual Review of Financial Economics, 14, 337-368. https://doi.org/10.1146/annurev-financial-101521-104735.
- Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. Review of Financial Studies, 33(5), 2223-2273. https://doi.org/10.1093/rfs/hhaa009.
- Hall, B. H., & Oriani, R. (2006). Does the market value R&D investment by European firms? Evidence from a panel of manufacturing firms in France, Germany, and Italy. International Journal of Industrial Organization, 24(5), 971-993. https://doi.org/10.1016/j.ijindorg.2005.12.001.
- Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30-50. https://doi.org/10.1007/s11747-020-00749-9.
- Huergo, E., & Jaumandreu, J. (2004). How does probability of innovation change with firm age? Small Business Economics, 22(3-4), 193-207. https://doi.org/10.1023/B:SBEJ.0000022220.07366.b5.
- Jovanovic, M., Sjödin, D., & Parida, V. (2022). Co-evolution of platform architecture, platform services, and platform governance: Toward plat-form archetypes. Technovation, 115, 102466. https://doi.org/10.1016/j.technovation.2020.102218.
- Martinsuo, M., & Geraldi, J. (2024). Project portfolio formation as an organizational routine. International Journal of Project Management, 42(7), 102-114. https://doi.org/10.1016/j.ijproman.2024.102592.
- Riasanow, T., Galic, L., Böhm, M., Krcmar, H., & Böhmann, T. (2021). Core, intertwined, and ecosystem-specific clusters in the digital transfor-mation of five ecosystems. Electronic Markets, 31, 235-253. https://doi.org/10.1007/s12525-020-00407-6.
- Shaffer, M. (2024). Which multiples matter in M&A? An overview. Review of Accounting Studies, 29, 1343-1378. https://doi.org/10.1007/s11142-023-09768-7.
- Vishnevskiy, K., Karasev, O., & Meissner, D. (2022). Technology roadmapping for digital transformation: A framework and case. Technological Forecasting and Social Change, 174, 121288.
-
Downloads
-
How to Cite
Ozbayburtlu, B., Balcioglu, Y. S., & Sezen, B. (2025). Data-Driven AI Product Roadmap Prioritization for SaaS Companies: A Valuation-Based Framework. International Journal of Basic and Applied Sciences, 14(7), 225-243. https://doi.org/10.14419/k0sr8689
