From Data to Decisions: Mathematical and Computational Frameworks in Marketing—A Systematic Review and Bibliometric Analysis

Abstract

The increasing availability of large-scale consumer data and advances in computational technologies have transformed marketing into a data-driven discipline. Mathematical and computational frameworks play a critical role in supporting decision-making processes by enabling organizations to analyze complex market dynamics, predict consumer behavior, optimize resource allocation, and improve strategic planning. This study has four objectives: to characterize the intellectual structure of the field, to classify the mathematical and computational methods employed and the marketing functions to which they are applied, to examine emerging technological developments, and to identify research and methodological gaps. A systematic literature review was conducted, reported in accordance with the PRISMA 2020 guidelines and organized through the Literature Review based on Systematic Bibliometry (LRSB) protocol. A single, fully documented query executed in Scopus returned 56 records, of which 45 met the eligibility criteria and were retained for both bibliometric science mapping and qualitative synthesis. The corpus spans 1992–2025. The review examines the integration of mathematical modeling, optimization techniques, machine learning algorithms, simulation methods, and data analytics within contemporary marketing practices. Drawing on the interdisciplinary convergence of marketing, engineering, mathematics, and computer science, the paper examines how computational approaches enhance market segmentation, customer engagement, demand forecasting, pricing strategies, advertising performance, and customer relationship management. The analysis highlights the growing relevance of predictive and prescriptive analytics in converting raw data into actionable insights that support evidence-based managerial decisions. Furthermore, the study discusses emerging trends, including artificial intelligence, real-time analytics, and intelligent decision support systems, that are reshaping the marketing landscape. The review also documents families of decision-support methods that are well established in the wider literature but almost absent from the retrieved corpus—multi-criteria decision making, mathematical programming beyond the linear case, metaheuristic optimization and Bayesian decision models—and treats this absence as a substantive research gap. Challenges related to data quality, model interpretability, computational complexity, and ethical considerations are also addressed. The findings suggest that the effective integration of mathematical and computational frameworks can significantly improve organizational competitiveness by increasing decision accuracy, operational efficiency, and responsiveness to market changes. This research contributes to a broader understanding of how advanced analytical methods support the transition from data collection to strategic decision-making in modern marketing environments.