Degree Name

Doctor of Philosophy


School of Business


In today's highly competitive retail environment, customer analytics has emerged as a powerful strategic tool. Customer analytics, the concept of data analytics-driven customer-centric value generation in multi-channels to render superior customer experience, has become one of the most vital aspects of retail firms' customer-centred marketing management. However, despite its strategic importance, there has been little investigation into customer analytics capabilities in the retail context. While many retail firms have attempted to adopt advanced analytics, there is a lack of empirical evidence regarding customer analytics capability and its impact under the influence of artificial intelligence (AI) orientation. To address this gap, this study investigates the foundational elements of customer analytics capability and its impact on marketing outcomes, drawing on the resource-based view capability and market orientation theoretical paradigm. Through a multi-phase research design, Study 1 develops the research model, incorporating a systematic literature review, thematic analysis, interviews, and small-scale surveys. Study 2 then follows multiple phases to develop the research instruments for measuring customer analytics capability, while Study 3 confirms the validation of the research model. The empirical findings of this research confirm that customer analytics capability comprises three value-centric dimensions (value creation, value delivery, and value management capability) and nine sub-dimensions (trend-based offering, learning, tailoring, process consistency, content consistency, support and recovery, data integration, privacy assurance, and security). These dimensions combine to accelerate retail performance, and the study's findings reveal that AI orientation plays a vital moderating role in strengthening the link between customer analytics capability, market-responsive agility, and sustained competitive advantage. This research contributes to the literature on retail customer analytics by advancing knowledge on foundational capacities that form customer analytics capability in an AI environment. In practice, managers can use customer analytics capability and AI to gain a competitive advantage in the marketplace, enabling them to respond rapidly to changing customer demands, make prompt strategic decisions, and quickly implement customer-centric marketing activities. Overall, this empirical research provides novel insights into customer analytics capability, market-responsive agility, and sustained competitive advantages in the AI spectrum, and can guide retail firms in transforming their business to compete more effectively.

FoR codes (2020)

350301 Business analytics, 350302 Business information management (incl. records, knowledge and intelligence), 350605 Marketing management (incl. strategy and customer relations), 350607 Marketing technology

This thesis is unavailable until Wednesday, August 14, 2024



Unless otherwise indicated, the views expressed in this thesis are those of the author and do not necessarily represent the views of the University of Wollongong.