AI-Powered Marketing: Analyzing the Impact of Artificial Intelligence on Customer Experience Personalization in the Digital Era

Riri Putri Dika; Ahmad Vajri Rahman; Yani Sri Mulyani

doi:10.37034/jems.v8i2.295

Authors

Riri Putri Dika Universitas Putra Indonesia YPTK Padang
Ahmad Vajri Rahman Universitas Putra Indonesia YPTK Padang
Yani Sri Mulyani Universitas BSI

DOI:

https://doi.org/10.37034/jems.v8i2.295

Keywords:

Artificial Intelligence, Personalization, Customer Experience, Perceived Relevance, Interaction Quality

Abstract

This study investigates the influence of artificial intelligence on customer experience personalization within digital platforms by examining three key predictors: AI-driven personalization, perceived relevance, and interaction quality. Using a quantitative approach, data were collected from 120 active users of AI-enabled platforms through purposive sampling and screening procedures conducted between January and February 2025. A total of 16 validated Likert-scale items were used to measure the constructs, and the measurement model demonstrated strong reliability and convergent validity, with AVE values ranging from 0.62 to 0.74 and Composite Reliability values between 0.86 and 0.91. Regression analysis revealed that AI-driven personalization had the strongest positive effect on customer experience personalization (β = 0.41, p < 0.001), followed by perceived relevance (β = 0.28, p < 0.001) and interaction quality (β = 0.22, p = 0.002). The model accounted for 58% of the variance in personalized customer experiences (R² = 0.58), indicating a robust explanatory power. These findings demonstrate that personalization is a multidimensional construct shaped by technological intelligence, cognitive alignment, and user–system interaction fluency. The study highlights the importance of integrating AI capabilities with meaningful content delivery and seamless interface design, offering practical insights for digital platforms seeking to enhance personalization and user engagement. Future research may explore moderating factors such as trust, privacy concerns, platform characteristics, and cross-cultural variations.

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