Artificial Intelligence (AI) is revolutionizing the way businesses approach customer engagement, particularly in the context of Business-to-Business (B2B) marketing. This thesis investigates the role of AI-optimized processes in enhancing Customer Lifetime Value (CLV) across various stages of the digital marketing journey. It addresses critical gaps in existing research by focusing on how AI influences the B2B customer journey, which is marked by its complexity and the dominance of digital interactions. The study explores how AI can empower businesses to identify, nurture, and retain high-value customers, ultimately driving sustainable growth and competitive advantage. The research adopts a mixed-methods approach that combines qualitative and quantitative analyses to achieve these objectives. The qualitative component involves eleven expert interviews, which provide in-depth insights into the challenges and opportunities of leveraging AI and marketing automation tools in B2B contexts. These interviews are coded and analysed using MAXQDA to identify recurring themes and trends. The quantitative component employs a survey designed to assess the impact of AI on CLV, with data analyzed through IBM SPSS and SmartPLS. This comprehensive methodology ensures a nuanced understanding of the interplay between AI applications and CLV outcomes. The findings reveal that AI significantly enhances CLV across all customer journey stages— pre-purchase, purchase, and post-purchase. AI enables precise customer targeting, facilitates personalised interactions, and employs predictive analytics to anticipate customer needs. Key drivers of CLV, such as customer satisfaction, commitment, loyalty, and brand credibility, are amplified through AI-driven strategies. However, the study also highlights challenges in adopting AI, including integration complexities, data quality issues, and the need for organizational readiness. Despite these obstacles, the research identifies significant opportunities for AI to transform B2B marketing through advanced applications like dynamic segmentation, predictive retention models, and hyper-personalized campaigns, all of which can substantially boost CLV. This thesis makes a valuable contribution by bridging the literature gap concerning AI application in B2B contexts, where customer interactions and lifetime value are uniquely intricate. It provides actionable insights for businesses on aligning AI technologies with the distinct dynamics of B2B relationships, emphasising the strategic role of AI in optimising digital marketing processes. By demonstrating AI’s potential to reshape the customer journey, this research serves as a roadmap for organizations aiming to enhance profitability and customer retention in an increasingly digitalized marketplace.
AI-Optimized Processes: Investigating the Impact on Customer Lifetime Value Across Various Stages of the Digital Marketing Journey
Carreon, J. (Author). 2024
Student thesis: Master's Thesis