Artificial intelligence models for service personalization and marketing optimization: the case of Air Astana (JSC)
https://doi.org/10.52821/2789-4401-2025-5-212-226
Abstract
Purpose: The study aims to develop personalized digital offers for Air Astana through artificial intelligence (AI) and machine learning (ML) to strengthen its unique selling propositions (USPs) – high service quality and transit connectivity – and to measure their quantitative impact on marketing performance during 2010–2024.
Methodology: A mixed approach combining system-based and comparative analysis, economic-mathematical modeling, and interview–survey techniques was applied. AI algorithms such as collaborative filtering, content-based filtering, uplift modeling, and reinforcement learning were used to predict consumer behavior and optimize personalization strategies.
Findings: The developed AI-driven personalization models improved digital campaign response rates by +15% and reduced marketing expenditures by –20%. Statistical validation showed a strong model fit (R² = 0.902), confirming that offer relevance significantly increases customer engagement and conversion probability.
Originality / Value: This is the first comprehensive empirical study in Kazakhstan’s aviation sector integrating real airline data with AI-based personalization. The findings demonstrate that data-driven marketing decisions can enhance Air Astana’s competitiveness as a Eurasian transit hub and ensure more efficient budget allocation.
About the Authors
A. М. Duisebayeva
Narxoz University
Kazakhstan
Kazakhstan
Aizhan Duisebayeva – PhD, Associate Professor, School of Economics and Management
Almaty
A. Adilbekkyzy
Narxoz University
Kazakhstan
Kazakhstan
Aidana Adilbekkyzy – Student of "Marketing"
Almaty
G. Toktaly
Narxoz University
Kazakhstan
Kazakhstan
Gulnur Toktaly – Student of "Marketing"
Almaty
References
1. Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2021). The future of artificial intelligence in marketing: A systematic review. Journal of Business Research, 124, 336–352. https://doi.org/10.1016/j.jbusres.2020.11.041
2. Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute. Retrieved from https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-modeling-the-impact-of-ai-on-the-worldeconomyMcKinsey&Company+2McKinsey&Company+2
3. Guerrini, A., Ferri, G., Rocchi, S., Cirelli, M., Piña, V., & Grieszmann, A. (2023). Personalization @ scale in airlines: Combining the power of rich customer data, experiential learning, and revenue management. Journal of Revenue and Pricing Management, 22(2), 171–180. https://doi.org/10.1057/s41272-022-00404-8
4. Devriendt, F., Van Belle, J., Guns, T., & Verbeke, W. (2022). Learning to rank for uplift modeling. IEEE Transactions on Knowledge and Data Engineering, 34(10), 4888–4904. https://doi.org/10.1109/TKDE.2022.3153950
5. Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. In The Adaptive Web (pp. 325–341). Springer. https://doi.org/10.1007/978-3-540-72079-9_10
6. Bobadilla, J., Ortega, F., Hernando, A., & Gutierrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109–132. https://doi.org/10.1016/j.knosys.2013.03.012
7. Jo, H., Moon, I., & Rhee, B. (2024). Dynamic pricing in airline revenue management using reinforcement learning. Expert Systems with Applications, 233, Article 120857. https://doi.org/10.1016/j.eswa.2023.120857
8. Olaya-Contreras, D., Suárez-Andrés, J., & García-Núñez, J. (2020). A survey and benchmarking study of multitreatment uplift modelling. Data Mining and Knowledge Discovery, 34(5), 1303–1336. https://doi.org/10.1007/s10618-020-00702-y
9. Radcliffe, N. J., & Surry, P. D. (2017). Uplift modelling: Literature survey and experimental evaluation. Big Data, 5(4), 273–290. https://doi.org/10.1089/big.2016.0074
10. Chandra, S., Verma, S., Lim, W. M., Kumar, S., & Donthu, N. (2022). Personalization in personalized marketing: Trends and ways forward. Psychology & Marketing, 39(8), 1529–1562. https://doi.org/10.1002/mar.21670
11. Kapoor, A., Dwivedi, Y. K., & Piercy, N. (2022). Artificial intelligence in marketing: Evolution, challenges, and opportunities. European Journal of Marketing, 56(3), 853–880. https://doi.org/10.1108/EJM-12-2021-1009
12. Alfaifi, Y. H., Hasanain, F., & Ramayah, T. (2024). Recommender systems applications: Data sources, features, challenges and future trends. Information, 15(10), Article 660. https://doi.org/10.3390/info15100660
13. Aksoy, N., Kabadayı, E., Yılmaz, C., & Koçak Alan, A. (2021). A typology of personalisation practices in marketing in the digital age. Journal of Marketing Management, 37(11–12), 1091–1122. https://doi.org/10.1080/0267257X.2020.1866647
14. Jannach, D., & Jugovac, M. (2019). Measuring the business value of recommender systems. ACM Transactions on Management Information Systems, 10(4), Article 1. https://doi.org/10.1145/3370082
15. Zangerle, E., & Bauer, C. (2023). Evaluating recommender systems: Survey and framework. ACM Computing Surveys, 55(8), Article 170. https://doi.org/10.1145/3556536
Review
For citations:
Duisebayeva A.М., Adilbekkyzy A., Toktaly G. Artificial intelligence models for service personalization and marketing optimization: the case of Air Astana (JSC). Central Asian Economic Review. 2025;(5):212-226. https://doi.org/10.52821/2789-4401-2025-5-212-226
Views:
11
Email this article 