Personalized AI Marketing Chatbots for Retail Experience Enhancement

Thesis Abstract

The rapid digitization of consumer retail interactions has intensified the need for intelligent, context-aware communications that enhance shopper experience while supporting firm-level performance metrics. This study addresses the problem of how personalized AI marketing chatbots can be designed and deployed to elevate retail experience through adaptive messaging, real-time recommendations, and seamless omni-channel engagement. The aim is to investigate the mechanisms by which AI-driven chatbots influence customer satisfaction, perceived value, and purchase conversion, and to identify the organizational capabilities required to sustain these benefits. Specific objectives are (1) to evaluate the impact of personalized chatbot interactions on customer satisfaction and perceived value in fast-moving consumer goods (FMCG) and electronics retail settings; (2) to examine the effect of chatbot-driven recommender systems on basket size and cross-selling performance; (3) to assess the moderating role of user trust, privacy concerns, and prior chatbot experience on engagement and outcomes; (4) to explore the operational and ethical considerations of deploying chatbots at scale, including data governance, transparency, and human–AI collaboration; and (5) to develop a theoretical model linking customer experience, trust constructs, and behavioral outcomes in AI-mediated retail contexts. A mixed-methods research design is employed. The quantitative strand uses a quasi-experimental field study across three retailer partners, sampling 1,200 purchase occasions (400 per retailer) over a six-week period, with customers randomly exposed to either personalized AI chatbot assistance or standard chatbot/no chatbot support. Data collection instruments include in-chat sentiment metrics, post-interaction surveys measuring satisfaction (CSAT), perceived value (PV), trust, perceived privacy risk, and behavioral outcomes such as add-to-cart and conversion rates. The qualitative strand comprises 28 semi-structured interviews with customers, store managers, and data engineers, complemented by 16 hours of chatbot interaction logs for thematic triangulation. Analytical techniques include multiple regression to test direct effects of personalization on satisfaction and PV, logistic regression for conversion likelihood, ANOVA to compare treatment effects across retailer segments, moderation analyses to assess trust and privacy concerns, and thematic analysis of interview data guided by the Theory of Reasoned Action and the Technology Acceptance Model. Additionally, a structural equation model (SEM) will be employed to evaluate the proposed causal pathways among customer experience, trust, engagement, and purchasing behavior. Content- and behavior-based personalization will be operationalized through collaborative filtering, context-aware prompts, and dynamic offers, with system quality, information quality, and service quality modeled as antecedents per the Delone–McLean framework. The study is expected to reveal that personalized AI chatbots significantly improve customer satisfaction and PV, with positive effects on basket size and conversion, moderated by user trust and privacy perceptions. It is anticipated that recommender-driven interactions will yield higher cross-sell rates, particularly when supported by transparent disclosure of data usage and opt-in personalization settings. The findings should demonstrate that trust acts as a critical amplifier for engagement, while perceived privacy risk dampens the effectiveness of personalization, underscoring the necessity for privacy-by-design approaches, explainable AI features, and clear human–AI handoffs in critical decision moments. The contribution to knowledge includes (i) a validated empirical model linking AI-mediated personalization, customer experience, and purchasing behavior in real-world retail settings; (ii) a nuanced understanding of how trust and privacy concerns influence the efficacy of chatbots; (iii) practical guidelines for designing scalable, ethically sound chatbot architectures that balance personalization gains with governance requirements; and (iv) a framework for evaluating omni-channel AI-assisted retail experiences that integrates customer metrics with operational performance. Policy and managerial implications emphasize the importance of transparent data governance, consent management, and explainability to foster trust and sustained engagement. Recommendations address algorithmic transparency, user-controlled personalization levels, monitoring dashboards for KPI alignment (CSAT, PV, average order value, conversion), and collaborative routines between marketing, IT, and legal teams to ensure compliance and responsible innovation. The study concludes that when designed with user-centric transparency and robust governance, personalized AI marketing chatbots can substantially enhance retail experience and business outcomes across diverse product categories.