Personalized Digital Retail Assistants via AI for Omnichannel Engagement

Thesis Abstract

The rapid evolution of digital retail environments necessitates intelligent, platform-agnostic tools capable of delivering seamless and personalized customer experiences across multiple channels. This study investigates the development and impact of Personalized Digital Retail Assistants (PDRAs) powered by artificial intelligence to enhance omnichannel engagement. The problem addressed is the fragmentation of customer interactions across channels, which diminishes personalization, increases friction in the purchase journey, and undermines customer loyalty. The aim is to design, validate, and assess an AI-driven PDRA architecture that integrates contextual data, user preferences, and real-time channel dynamics to deliver consistent, personalized recommendations, assistance, and proactive engagement across online, mobile, social, and in-store touchpoints. Specific objectives include (1) identifying key personalization signals and interaction modalities that drive satisfaction and conversion, (2) architecting a PDRA framework that interoperates with existing CRM and e-commerce platforms, (3) evaluating the PDRA’s impact on perceived personalization, channel coherence, and sales metrics, (4) examining privacy, trust, and ethical considerations in omnichannel AI deployment, and (5) proposing governance mechanisms for continuous learning and fairness. A mixed-methods approach is employed. The quantitative strand adopts a quasi-experimental design in two retail chains (n=1,200 customers per chain) with randomized exposure to PDRA-enabled versus standard omnichannel experiences over a 12-week period, collecting metrics on conversion rate, average order value, basket size, repeat purchase rate, and net promoter score. Regression analyses and propensity-score matching are used to estimate causal effects while controlling for channel, demographic, and behavioral covariates. A qualitative strand uses semi-structured interviews with 40 customers and 20 frontline staff, followed by thematic analysis to elucidate perceived personalization quality, system interpretability, and workflow integration challenges. Additionally, the PDRA’s internal data logs (n?2.5 million interaction events) are analyzed via sequence analysis and random forest feature importance to identify dominant personalization signals and predictive factors for engagement. The study draws on the Social Exchange Theory and the Privacy Calculus framework to interpret trust, perceived value, and risk in AI-mediated interactions, and it adopts a modular AI architecture comprising a user model, a decision engine, and a channel-adapter layer linked to existing CRM and ERP systems. Key expected findings include (a) statistically significant improvements in conversion rate and average order value for customers interacting with PDRA-enabled channels compared with control conditions; (b) higher perceived personalization and channel coherence scores, moderated by user privacy concerns and prior trust in AI; (c) identification of critical personalization signals (e.g., real-time intent signals, shopping history, and cross-channel context) that predict engagement; (d) evidence of effective multi-channel orchestration reducing customer effort and increasing task completion rates; and (e) insights into deployment constraints, including data governance, system latency, and explainability requirements. The contributions to knowledge encompass (i) a scalable architectural blueprint for AI-driven PDRA systems that harmonize data across e-commerce, mobile apps, social platforms, and in-store interfaces; (ii) empirical evidence on the performance and customer experience benefits of omnichannel AI personalization in retail; and (iii) a nuanced understanding of ethical and privacy considerations in real-time personalization, with practical governance recommendations. The study concludes that well-designed PDRA systems can materially enhance omnichannel engagement and commercial outcomes when anchored in robust data governance, transparent decision logic, and continuous learning mechanisms. Recommendations include (a) adopting standardized data schemas and interoperable APIs to facilitate cross-channel data fusion, (b) implementing privacy-preserving techniques and user-consent models to mitigate perceived risk, (c) incorporating explainable AI components to support customer and staff trust, and (d) establishing ongoing monitoring and bias mitigation processes to sustain fairness and performance over time. The findings aim to inform retail practitioners, policymakers, and scholars about the strategic and operational implications of deploying AI-driven personalization at scale across omnichannel environments.