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.