EVALUATING THE EFFECTIVENESS OF MARKETING STRATEGIES USING BUSINESS INTELLIGENCE TOOLS - HORBACHENKO, Stanislav; CHEPURNA, Olena; КUKLINOVA, Tетіана; TABALA, Kseniia (Herald of Khmelnytskyi National University. Economic sciences, 2025)
This article examines how Business Intelligence tools can be used to evaluate the effectiveness of marketing strategies in the context of digital transformation. The purpose of the study is to develop a comprehensive approach that unites financial and customer-oriented indicators into a single framework that can be embedded in Business Intelligence processes for ongoing monitoring and managerial decision-making. The authors contribute by developing an integrated indicator of marketing strategy effectiveness. This indicator aggregates several core measures– return on investment, return on advertising spend, customer lifetime value, customer retention rate, and click-through rate– after normalization to a common scale and weighting based on the analytic hierarchy process. The article details an implementation workflow that includes data integration from diverse sources, data harmonization, automated calculation of the integrated indicator, and visualization within Business Intelligence platforms. The approach also incorporates scenario analysis to demonstrate how changes in budget allocation and channel optimization influence the indicator, thereby directly linking analytical outputs to resource management. The scientific novelty lies in combining a transparent multi-criteria weighting procedure with robust normalization that preserves the interpretability and comparability of each component. This approach goes beyond traditional single-metric evaluations by integrating both financial results and customer-behavior dynamics, allowing for reliable comparisons across channels and time periods. The method is platform-independent, compatible with major Business Intelligence solutions, and designed for straightforward adoption in organizational practice. The practical significance is demonstrated through application to a realistic electronic-commerce marketing scenario in which three budget allocation strategies are evaluated. The integrated indicator reflects that reallocation of resources to more effective channels results in a measurable improvement in overall marketing performance. The approach provides managers with a practical tool for continuous campaign adjustment, strategic portfolio management across marketing channels, and early identification of underperforming investments. The main conclusion is that the proposed framework supports tighter alignment between marketing actions, financial returns, and customer engagement. For practitioners, it offers a ready-to-implement solution for embedding advanced evaluation metrics into routine Business Intelligence analytics. For researchers, it establishes a replicable foundation for future studies, particularly for learning optimal weighting from historical data. The study notes that data quality and expert input on indicator weights remain important for the model’s reliability. Future research should focus on cross-industry comparisons and machine-learning-based refinement of weights, which would further enhance external validity and decision-support capabilities.
Keywords: decision support; business analytics; marketing budget optimization; composite index of marketing strategy effectiveness (IMSE); multi-criteria systems analysis; attribution modeling; analytic hierarchy process (AHP); key performance indicators (KPI)