A Structural Equation Modelling Approach to Analyse the Impact of AI-Driven Personalization for Customer Acquisition

Enhancing customer acquisition in e-commerce requires a strategic integration of trust, customer engagement, transparent data policies, and AI-driven experiences. This study explores how these factors collectively shape consumer decision-making and brand loyalty. The research objectives include, Examining the influence of trust and transparency on first-time purchases, Evaluating AI’s role in personalization and customer retention and Identifying engagement strategies that drive acquisition. A quantitative research design using Structural Equation Modelling (SEM) was employed to analyse factor relationships. Data was collected from a diverse sample of 400 e-commerce consumers, spanning different age groups and online shopping behaviours. Key findings indicate that AI-powered personalization and chatbot efficiency significantly enhance customer engagement. Price transparency and clear policies strongly influence consumer trust, while brand reliability plays a pivotal role in fostering long-term customer relationships. Although customer engagement has a notable impact, trust and AI-driven personalization emerged as the strongest drivers of acquisition. Based on these insights, the study recommends: Enhancing AI-driven chatbots for better customer interaction, Improving pricing transparency to build consumer confidence, Strengthening trust-building initiatives to drive long-term loyalty Limitations include a geographically restricted sample and potential self-reporting biases. Future research could explore industry-specific applications and cross-cultural differences in customer acquisition strategies, refining digital commerce models for sustainable growth.