ANALYTICS IN MARKETING PROJECT MANAGEMENT AS A BASIS FOR MAKING MANAGEMENT DECISIONS - Kim , O. O. (The actual problems of regional economy development, 2025)
Analytics, including the use of analytical tools in management decision-making, is one of the key innovative factors in business development at the current stage of development. Management decisions are the basis of business functioning, and further developments will depend on whether an effective decision is made at each stage of the life cycle. In marketing project management, there are two components of analytical processes - defined by the essence of the marketing project (analytics of processes and metrics for traffic acquisition, conversion, LTV, ROMI, average price per lid, etc.) and defined by the project management framework (monitoring and analytics of project implementation, preparation and updating of the burndown chart, Stakeholder engagement plan, risk management plan, WBS, RBS, budget planning and execution, EVM metrics, BAC-PV-EV-AC model, CPI - cost performance index, SPI - schedule performance index, EAC and other related metrics). Alignment of all analytics methodologies, project planning and execution metrics is the essence of marketing project analytics. Decision-making in marketing projects is an appropriate balancing act between efficiency and the three main project constraints - scope, schedule, and budget. Given clear prioritization of project constraints (e.g., hard budget and adaptive scope and schedule constraints, or hard scope and adaptive budget anad schedule constraints as well as other combinations of priorities), appropriate decision-making frameworks should be applied that will lead to the best overall marketing project results. The ideal situation would be to maximize positive project metrics that represent scale (marketing and financial metrics of the result of marketing activities) while meeting the schedule and budget (CPI and SPI equal to one or 100%), but in the actual implementation of a marketing project, various problems may arise that require data-driven management decisions. The purpose of the article is to identify effective frameworks for data-driven management decision-making, taking into account different areas of analytics and project constraints.