How an organization can optimise the allocation of its digital marketing budget

The rapid expansion of digital marketing channels and the increasing demand for accountability have transformed digital marketing budget allocation into a critical managerial and research challenge. Organizations must distribute resources across multiple digital platforms while responding to dynamic consumer behavior, technological innovation, and competitive pressures. This dissertation investigates how organizations can optimize the allocation of their digital marketing budgets by integrating data-driven decision-making, performance metrics, customer journey mapping, and advanced analytical tools. A mixed-methods research design was adopted, combining qualitative semi-structured interviews with marketing professionals, a quantitative consumer survey, and secondary performance data from Eurobank’s digital marketing campaigns. Qualitative data were analyzed using thematic analysis, quantitative data were examined through Partial Least Squares Structural Equation Modeling (PLS-SEM), and secondary campaign data were analyzed using regression techniques to assess relationships between cost allocation and performance outcomes. The findings indicate that digital marketing budget optimization is primarily driven by data-driven decision-making supported by key performance indicators such as Return on Investment (ROI), Cost per Click (CPC), and Cost per Acquisition (CPA). Results highlight the importance of flexible and dynamic budgeting practices, real-time reallocation mechanisms, and structured portfolio allocation models that balance performance stability with experimentation. Furthermore, artificial intelligence and predictive analytics enhance forecasting accuracy and allocation responsiveness, although managerial judgment remains essential. Environmental and competitive dynamics were found to moderate the effectiveness of data-driven allocation processes.