Abstract/Summary

Customer experience management (CXM) has emerged as a significant aspect of business strategy, acknowledged for its role in fostering sustainable competitive differentiation. In this context, the utilisation of artificial intelligence (AI) technology in CXM assumes paramount importance, as it enables organizations to revolutionize customer journeys through AI-driven CXM. Such AI advancements hold immense potential in improving customer experience and enhancing organizational performance. However, the CXM field faces challenges in reaching the expected level of maturity. This research focuses on the role of AI in actioning customer insights throughout the Customer Journey (CJ) to manage Customer Experience (CX). The study aims to explore how organizations can effectively utilize AI-derived customer insights to continuously enhance CX and develop a framework for understanding and managing CX based on AI-generated insights within service organizations in Jordan. This includes understanding how AI can be incorporated into the customer insight process and exploring the various ways organizations utilize AI-driven customer insights to comprehend and manage customer experience. Additionally, the research seeks to explore the different ways organisations assess the value of actioning customer insights derived from AI. Due to the exploratory nature of the topic, a case study approach was deemed appropriate for investigating the contemporary phenomenon in-depth and within its real-life context. The study employed a multiple embedded case study design, with the organization as the holistic unit of analysis and the process of generating and actioning AI-enabled customer insights as the embedded sub-unit of analysis. The researcher selected four service organizations in the banking and telecommunications sectors that have a customer experience management function, program, or practice in place, have adopted AI technologies, and are accessible and willing to participate. The data collection techniques provided a rich, detailed, and complete picture of the phenomenon under study. Purposeful sampling was utilized to collect evidence from multiple informants within each organization through semi-structured and in-depth interviews and document reviews. The analysis of the case studies followed the 'stacking comparable cases’ approach recommended by Miles and Huberman (2014), which involved within-case analysis, cross-case analysis, and systematic comparison and synthesis. Within-case analysis was used to describe, understand, and explain what happened in a single, bounded context, while cross-case analysis was used to identify themes that cut across cases. Finally, systematic comparison and synthesis were used to compare and contrast findings across sector-specific and all sectors. The key findings of this thesis contribute to the realms of theory and empirical research, as well as the practical role and implementation of AI in the field of CXM. Firstly, the development of the Customer Experience-Based View (CXBV) framework introduces a significant theoretical advancement, demonstrating AI’s transformative implications in CXM. Building upon the Resource-Based View (RBV) and Knowledge-Based View (KBV), the CXBV framework incorporates six critical capabilities central to successful CXM, which include customer experience strategy, customer journey management, customer intelligence approach, agile operations, the CX data-to-value process, and the harnessing of AI capabilities. Further, this study brings to light the importance of additional dimensions such as customer experience strategy, customer journey management, customer intelligence, and agile operations, collectively substantiating the CX data-to-value creation process. It emphasizes the need for a comprehensive understanding of the transformation of customer data into customer experience actions. Moreover, this research establishes the intermediary role of customer intelligence in the data-to-value creation process. It posits the necessity of interpreting and understanding customer insights prior to their implementation. The study identifies two specific categories of AI analytics, namely AI-enabled data-to-insights analytics and AI-enabled intelligence-to-action analytics, demonstrating AI’s potential to transform customer data into actionable insights. From a practitioner standpoint, this thesis offers actionable insights and recommendations, particularly tailored to the telecommunications and banking sectors. The study's findings provide insights into how organizations can leverage AI-generated customer insights to manage customer experience effectively. This is exemplified by strategies such as providing customized consumer experiences, delivering seamless customer service across touchpoints, innovating, and developing data-driven products, and refining organizational processes. The research also highlights the importance of assessing the value of AI-generated customer insights, which can enable organizations to optimize their CXM strategies and improve customer loyalty. Lastly, the findings illuminate the immense latent value of AI-generated customer insights in enhancing various customer experience outcomes. It outlines how AI can enhance customer satisfaction, loyalty, and even facilitate revenue growth while enabling strategic customer acquisition. Furthermore, AI’s predictive capabilities are highlighted as instrumental in facilitating proactive decision-making and aligning business strategies with future trends and customer expectations. These findings culminate in providing a comprehensive scholarly understanding of the intersection of AI and CXM in the business and management domain, enhancing the discourse in both academia and industry.