AI THAT LISTENS: HOW PERSONALIZED SUGGESTIONS SHAPE USER EXPERIENCE AND RETENTION IN CROSS-BORDER E-COMMERCE

Abstract

This study looks at how personalised suggestions driven by AI affect user experience and retention in cross-border e-commerce. We define "AI that listens" as algorithms that customise product recommendations based on the unique data and preferences of each customer. We used a mixed-methods approach to do a survey of e-commerce users in Kazakhstan and in-depth interviews with marketers in the region. Quantitative findings indicate that perceived personalisation significantly boosts customer satisfaction and loyalty. For instance, people who trusted a platform more were much more likely to be satisfied and want to buy from it again. Personalisation made the model about 5% more accurate (Hassan, Abdelraouf, & El-Shihy, 2025). Qualitative feedback supports the notion that personalised recommendations (e.g., in the local language or currency) enhance the shopping experience by making it more engaging and pertinent. Users also brought up issues with privacy and openness, though (Madhuri, Shireesha, Reddy, & Kumar, 2024). These results show that listening AI can improve e-commerce results if used responsibly by changing to fit the needs of consumers in different countries. Marketers should put money into personalisation technologies while making sure that data privacy and explainability are protected.