Design and evaluate a personalized AI-driven marketing campaign for e-commerce success

Thesis Abstract

The rapid expansion of e-commerce platforms has heightened the need for innovative marketing strategies that effectively engage consumers and drive sales amidst increasing competition and customer diversity. This study addresses the challenge of designing and evaluating a personalized marketing campaign powered by artificial intelligence (AI), aiming to optimize customer engagement, enhance conversion rates, and improve overall e-commerce success. The primary objective is to develop a robust framework that integrates AI-driven personalization techniques within existing marketing channels, and subsequently assess their effectiveness through empirical analysis. The research adopts a mixed-methods approach, combining quantitative and qualitative data collection and analysis. The quantitative segment employs a quasi-experimental design involving a sample of 600 active online shoppers from a leading e-commerce retailer, selected through stratified random sampling to ensure diversity across age groups, geographic locations, and purchasing behaviors. Prior to campaign implementation, participants complete a baseline questionnaire assessing their preferences, behavioral intent, and satisfaction levels. An AI-driven personalization system—integrating machine learning algorithms such as collaborative filtering, content-based filtering, and clustering techniques—is developed to deliver tailored product recommendations, promotional messages, and content over an 8-week period. Data collection instruments include system logs, transaction records, post-interaction surveys, and in-depth interviews with a subset of 30 participants. Reliability and validity of survey instruments are ensured through pilot testing and Cronbach's alpha analysis. Quantitative data are analyzed using multiple regression analysis and ANOVA to evaluate the impact of personalization levels on purchase frequency, average order value, and customer satisfaction. Thematic analysis is employed to interpret qualitative feedback, providing insights into user perceptions and engagement drivers. The analytical framework is grounded in the Elaboration Likelihood Model (ELM) to explain how personalized content influences consumer processing and decision-making, complemented by the Technology Acceptance Model (TAM) to assess technology adoption factors. Model specifications incorporate variables such as personalization relevance, customer trust, and perceived convenience. Expected findings indicate that AI-driven personalized marketing significantly enhances customer engagement and transactional outcomes, with statistically notable improvements in purchase intent, customer satisfaction, and loyalty metrics. The study anticipates revealing the moderating effects of demographic variables and digital literacy levels on personalization efficacy. Findings are also expected to confirm that tailored content positively influences consumers’ perceived relevance and trust, aligning with established behavioral theories. This research contributes to the academic discourse by providing empirical evidence on the operational effectiveness of AI-enabled personalization within e-commerce marketing, expanding theoretical understanding of consumer responses to automated customization, and proposing a scalable model for practitioners. The study underscores the importance of integrating AI technologies with consumer behavior theories to develop targeted marketing strategies that are adaptive and customer-centric. In conclusion, the study recommends the adoption of AI-driven personalization in e-commerce marketing strategies to foster sustainable competitive advantage. It advocates for ongoing refinement of AI algorithms based on user feedback and behavioral analytics, emphasizing the necessity for ethical considerations related to data privacy and transparency. Future research directions include longitudinal studies to measure long-term impacts and explorations of cross-cultural applicability of AI personalization frameworks, thereby contributing to the advancement of intelligent marketing systems that are both effective and ethically responsible.