Leveraging Digital Marketing and Artificial Intelligence to Assess Enhanced Customer Experiences

Introduction: Incorporation of Artificial Intelligence (AI) into Digital Marketing (DM) has changed customer engagement by providing a personalized and efficient way to give service and interact with customers. For all of these advancements, there is limited empirical research that has explored how AI-driven personalization, convenience, and service quality impact customer experience through trust. Objective: This research uses a quantitative, survey-based research design to investigate how AI and DM influence customer experience.Method: Data is collected from 350 respondents that currently engage with an AI-enabled platform. Key constructs Perceived Personalization (PP), Perceived Convenience (PC), Customer Trust (CT), Customer Experience (CE) and AI-Enabled Service Quality (AESQ) were analyzed in this research.Result: Outcomes expose that PP (β = 0.34), PC (β = 0.28), and AESQ (β = 0.31) significantly influence CT, which in turn has a strong effect on CE (β = 0.62). The model explained that CT was 62% and CE was 56% variance, with statistical support of all hypothesis.Conclusion: This analysis concludes that AI-integrated DM strategies provide a better customer experience primarily because of developing trust. There are implications that marketers find practical in designing trust-centered AI interactions to enhance overall satisfaction and loyalty.

Publication Details

Journal
Dialnet (Universidad de la Rioja)
Published
2025-01-01
Primary Topic
Glass properties and applications
Type
article
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article

Leveraging Digital Marketing and Artificial Intelligence to Assess Enhanced Customer Experiences

Dialnet (Universidad de la Rioja)
Glass properties and applications
article

Leveraging Digital Marketing and Artificial Intelligence to Assess Enhanced Customer Experiences

article en

Abstract

Introduction: Incorporation of Artificial Intelligence (AI) into Digital Marketing (DM) has changed customer engagement by providing a personalized and efficient way to give service and interact with customers. For all of these advancements, there is limited empirical research that has explored how AI-driven personalization, convenience, and service quality impact customer experience through trust. Objective: This research uses a quantitative, survey-based research design to investigate how AI and DM influence customer experience.Method: Data is collected from 350 respondents that currently engage with an AI-enabled platform. Key constructs Perceived Personalization (PP), Perceived Convenience (PC), Customer Trust (CT), Customer Experience (CE) and AI-Enabled Service Quality (AESQ) were analyzed in this research.Result: Outcomes expose that PP (β = 0.34), PC (β = 0.28), and AESQ (β = 0.31) significantly influence CT, which in turn has a strong effect on CE (β = 0.62). The model explained that CT was 62% and CE was 56% variance, with statistical support of all hypothesis.Conclusion: This analysis concludes that AI-integrated DM strategies provide a better customer experience primarily because of developing trust. There are implications that marketers find practical in designing trust-centered AI interactions to enhance overall satisfaction and loyalty.

Dialnet (Universidad de la Rioja)
Industry, innovation and infrastructure
Openalex Percentile: Top 55%
Glass properties and applications
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