Journal of Data Analytics and Artificial Intelligence Applications

SUBMITINFORMATION
EnglishTürkçe

Research Article


DOI :10.26650/d3ai.1607791   IUP :10.26650/d3ai.1607791    Full Text (PDF)

Enhancing SME Operations with Machine Learning and Business Intelligence: A Case Study of Kolay.ai

Rabia Yörük

Small- and medium-sized enterprises (SMEs) face significant challenges in adopting advanced machine learning (ML) and business intelligence (BI) technologies because of limited resources, expertise, and financial constraints. This paper explores the transformative potential of ML and BI in improving financial management, customer engagement, and operational efficiency in SMEs by using Kolay.ai as a case study. Kolay.ai is a scalable, cloud-based platform that offers features such as sales prediction, customer segmentation through RFM analysis, personalised recommendations, and advanced data visualisation. These tools enable SMEs to optimise inventory management, enhance customer retention, and improve cross-selling opportunities. The platform also provides financial forecasting and company valuation tools, empowering SMEs to maintain healthy cash flows and make informed strategic decisions. By demonstrating Kolay.ai’s ability to streamline operations and enhance financial performance, this study highlights the practical implications and scalability of affordable, AI-driven BI solutions tailored to SME needs, contributing to the growing discourse on democratising access to advanced technologies. 

Keywords: Business Intelligence, machine learning, predictive analytics, data visualisation, financial management, smes, cloud platforms, operational efficiency

PDF View

References

  • [1] A. Gupta and V. Varma. 2019. Reinforcement Learning for Pricing and Revenue Management in E-commerce. J. Revenue Pricing Manag. 18, 1 (2019), 55-62 google scholar
  • [2] Abdel-Rahman Tawil, Mahmoud Mohamed, Xavier Schmoor, Konstantinos Vlachos, and Dima Haidar. 2023. Trends and Challenges Towards an Effective Data-Driven Decision Making in UK SMEs: Case Studies and Lessons Learnt from the AnalYsis of 85 SMEs. arXiv Preprint (2023). https:doi.org/10.48550/arXiv.2305.15454 google scholar
  • [3] Andreja Popovlc, RaY HackneY, Paulo S. Coelho, and JuriJ Jakllc. 2012. Towards Business Intelllgence SYstems Success: Effects of MaturitY and Culture on AnalYtical Decision Making. Decision Support SYstems 54, 1 (2012), 729-739. https:doi.org/10.1016/ j.dss.2012.08.017 google scholar
  • [4] C. Cortes and V. Vapnik. 1995. Support-vector networks. Mach. Learn. 20, 3 (1995), 273-297. http://doi.org/10.1007/BF0099401 google scholar
  • [5] C. J. C. H. Watkins and P. DaYan. 1992. Q-learning. Mach. Learn. 8, 3-4 (1992), 279-292. http://doi.org/10.1007/BF00992698 http:doi.org/10.1007/BF0099269 google scholar
  • [6] H. Chen, R. H. L. Chiang, and V. C. StoreY. 2012. Business Intelligence and AnalYtics: From Big Data to Big Impact. MIS Q. 36, 4 (2012), 1165-1188. https:doi.org/10.2307/4170350 google scholar
  • [7] Hsinchun Chen, Roger H. L. Chiang, and Veda C. StoreY. 2012. Business Intelligence and AnalYtics: From Big Data to Big Impact. MIS QuarterlY 36, 4 (2012), 1165-1188. https:doi.org/10.2307/41703503 google scholar
  • [8] I. Met, A. Erkoc, S. E. Seker, M. A. Erturk, and B. Ulug. 2024. Product Recommendation SYstem With Machine Learning Algorithms for SME Banking. Int. J. Intell. Syst. 2024, 1 (2024), 5585575. https:doi.org/10.1155/2024/55855 google scholar
  • [9] I. T. Jolliffe. 2002. Principal Component Analysis. Springer. https:doi.org/10.1007/b9883 google scholar
  • [10] J. A. Hartigan and M. A. Wong. 1979. Algorithm AS 136: A K-means clustering algorithm. J. Roy. Stat. Soc. C (Appl. Statist.) 28, 1 (1979), 100-108. https:doi.org/10.2307/234683 google scholar
  • [11] J. H. Frledman. 2001. GreedY Functlon Approxlmatlon: A Gradlent Boostlng Machine. Ann. Statist. 29, 5 (2001), 1189-1232. http:// doi.org/10.1214/aos/101320345 google scholar
  • [12] K. P. MurphY. 2012. Machine Learning: A Probabilistic Perspective. MIT Press google scholar
  • [13] Konstantlna Ragazou, Ioannls Passas, Alexandros Garefalakls, and Constantln Zopounldls. 2023. Buslness Intelllgence Model Empowerlng SMEs to Make Better Declslons and Enhance Thelr Competltlve Advantage. Dlscover AnalYtlcs 1, 2 (2023). https:dol.org/10.1007/s44257-022-00002-3 google scholar
  • [14] Lucas Grlesch, Jonas RlttelmeYer, and Kurt Sandkuhl. 2023. Towards AI as a Servlce for Small and Medlum-Slzed Enterprlses (SME). In The Practlce of Enterprlse Modellng, 37-53. Sprlnger. https:dol.org/10.1007/978-3-031-48583-1_3 google scholar
  • [15] M. Alnoukarl and A. Hanano. 2017. Integratlon of Buslness Intelllgence wlth Cloud Computlng: A Practlcal Approach. J. Theor. Appl. Inf. Technol., 95, 1 (2017), 63-72. http://dol.org/10.4018/978-1-7998-5040-3.ch00 google scholar
  • [16] Markus Schönberger. 2023. Artlficlal Intelllgence for Small and Medlum-Slzed Enterprlses: IdentlfYlng KeY Appllcatlons and Challenges. Journal of Buslness Management 21 (2023). Retrleved from https:journals.rlseba.eu/lndex.php/jbm/artlcle/vlew/336 google scholar
  • [17] Nlck DrYdakls. 2023. Artlficlal Intelllgence and Reduced SMEs' Buslness Rlsks: A DYnamlc Capabllltles AnalYsls Durlng the COVID-19 Pandemlc. SSRN Electronlc Journal (2023). https:dol.org/10.2139/ssrn.4114609 google scholar
  • [18] Quoc HuY Pham and Kleu Phuong Vu. 2023. Blg Data ln Relatlon wlth Buslness Intelllgence Capabllltles and E-Commerce Durlng COVID-19 Pandemlc ln Accountant’s Perspectlve. Future Buslness Journal 9, 40 (2023). https:dol.org/10.1186/s43093-023-00221-4 google scholar
  • [19] S. Fosso Wamba, S. Akter, A. Edwards, G. Chopln, and D. Gnanzou. 2017. How 'blg data' can make blg lmpact: Flndlngs from a sYstematlc revlew and a longltudlnal case studY. Int. J. Prod. Econ. 165 (2017), 234-246. http://dol.org/10.1016/j.ljpe.2014.12.03 google scholar
  • [20] S. Kumar and A. Ramesh. 2018. Machlne Learnlng ln Buslness: A Conceptual Framework. J. Bus. Anal. 1, 1 (2018), 1-17 google scholar
  • [21] S. Sonl, M. Sharma, and T. Slngh. 2020. Machlne Learnlng for SMEs: Adoptlon and Benefits. Int. J. Data Sci. Anal. 6, 3 (2020), 112-119 google scholar
  • [22] T. Chen and C. Guestrln. 2016. XGBoost: A Scalable Tree Boostlng SYstem. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16), 785-794. https:dol.org/10.1145/2939672.293978 google scholar
  • [23] Tanla Guarda, Manuel F. Santos, Cesar Sllva, and Rul Lopes. 2013. Business Intelllgence for SMEs: A Proposal for an Information SYstem to Improve Small and Medlum Enterprlses Performance. Procedla TechnologY 9 (2013), 728-733. https:dol.org/10.1016/ j.protcY.2013.12.080 google scholar
  • [24] Thomas H. Davenport and Jeanne G. Harrls. 2007. Competing on Analytics: The New Science of Winning. Harvard Buslness Revlew Press. google scholar

Citations

Copy and paste a formatted citation or use one of the options to export in your chosen format


EXPORT



APA

Yörük, R. (2025). Enhancing SME Operations with Machine Learning and Business Intelligence: A Case Study of Kolay.ai. Journal of Data Analytics and Artificial Intelligence Applications, 0(0), -. https://doi.org/10.26650/d3ai.1607791


AMA

Yörük R. Enhancing SME Operations with Machine Learning and Business Intelligence: A Case Study of Kolay.ai. Journal of Data Analytics and Artificial Intelligence Applications. 2025;0(0):-. https://doi.org/10.26650/d3ai.1607791


ABNT

Yörük, R. Enhancing SME Operations with Machine Learning and Business Intelligence: A Case Study of Kolay.ai. Journal of Data Analytics and Artificial Intelligence Applications, [Publisher Location], v. 0, n. 0, p. -, 2025.


Chicago: Author-Date Style

Yörük, Rabia,. 2025. “Enhancing SME Operations with Machine Learning and Business Intelligence: A Case Study of Kolay.ai.” Journal of Data Analytics and Artificial Intelligence Applications 0, no. 0: -. https://doi.org/10.26650/d3ai.1607791


Chicago: Humanities Style

Yörük, Rabia,. “Enhancing SME Operations with Machine Learning and Business Intelligence: A Case Study of Kolay.ai.” Journal of Data Analytics and Artificial Intelligence Applications 0, no. 0 (Feb. 2025): -. https://doi.org/10.26650/d3ai.1607791


Harvard: Australian Style

Yörük, R 2025, 'Enhancing SME Operations with Machine Learning and Business Intelligence: A Case Study of Kolay.ai', Journal of Data Analytics and Artificial Intelligence Applications, vol. 0, no. 0, pp. -, viewed 5 Feb. 2025, https://doi.org/10.26650/d3ai.1607791


Harvard: Author-Date Style

Yörük, R. (2025) ‘Enhancing SME Operations with Machine Learning and Business Intelligence: A Case Study of Kolay.ai’, Journal of Data Analytics and Artificial Intelligence Applications, 0(0), pp. -. https://doi.org/10.26650/d3ai.1607791 (5 Feb. 2025).


MLA

Yörük, Rabia,. “Enhancing SME Operations with Machine Learning and Business Intelligence: A Case Study of Kolay.ai.” Journal of Data Analytics and Artificial Intelligence Applications, vol. 0, no. 0, 2025, pp. -. [Database Container], https://doi.org/10.26650/d3ai.1607791


Vancouver

Yörük R. Enhancing SME Operations with Machine Learning and Business Intelligence: A Case Study of Kolay.ai. Journal of Data Analytics and Artificial Intelligence Applications [Internet]. 5 Feb. 2025 [cited 5 Feb. 2025];0(0):-. Available from: https://doi.org/10.26650/d3ai.1607791 doi: 10.26650/d3ai.1607791


ISNAD

Yörük, Rabia. “Enhancing SME Operations with Machine Learning and Business Intelligence: A Case Study of Kolay.ai”. Journal of Data Analytics and Artificial Intelligence Applications 0/0 (Feb. 2025): -. https://doi.org/10.26650/d3ai.1607791



TIMELINE


Submitted26.12.2024
Accepted23.01.2025
Published Online27.01.2025

LICENCE


Attribution-NonCommercial (CC BY-NC)

This license lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge you and be non-commercial, they don’t have to license their derivative works on the same terms.


SHARE




Istanbul University Press aims to contribute to the dissemination of ever growing scientific knowledge through publication of high quality scientific journals and books in accordance with the international publishing standards and ethics. Istanbul University Press follows an open access, non-commercial, scholarly publishing.