AI-Driven Customer Journey Mapping and Personalization in SMEs: Predicting and Prescribing Optimal Touchpoints for Enhanced Customer Experience.

Small and medium-sized enterprises (SMEs) compete in markets where customers move across an increasing number of digital and physical touchpoints, yet most of these firms still map and manage the customer journey with intuition and fragmented data rather than systematic analysis. This thesis examines, through a structured review of the marketing, information systems and service research literature published mainly between 2016 and 2025, how artificial intelligence (AI) reshapes customer journey mapping and personalization in the specific context of resource-constrained smaller firms. The discussion is organised around four objectives: tracing the evolution of customer journey mapping and personalization in SMEs; analysing how AI improves the analysis of customer behaviour and the visibility of the journey; evaluating how predictive and prescriptive analytics help identify and act on optimal touchpoints; and assessing the opportunities, limitations and ethical implications that accompany AI-driven personalization. The evidence indicates that AI shifts customer journey mapping from a static, retrospective artefact towards a continuously updated and forward-looking capability, in which machine learning, natural language processing and predictive analytics convert behavioural traces into anticipations of customer needs. Predictive models help SMEs estimate which touchpoints matter and when intervention is most valuable, while prescriptive approaches translate those estimates into concrete recommendations about timing, channel and content. The literature also points towards real constraints for SMEs, including limited data volumes, scarce analytical skills and dependence on external platforms, alongside ethical concerns about privacy, transparency and the risk that personalization is perceived as surveillance. The analysis highlights that the value of AI for smaller firms depends less on the sophistication of individual algorithms than on the integration of data, capabilities and governance around a clearly understood customer journey. The thesis closes by identifying gaps in the current literature and outlining implications for SME customer experience strategy.