Sarcouncil Journal of Engineering and Computer Sciences
Sarcouncil Journal of Engineering and Computer Sciences
An Open access peer reviewed international Journal
Publication Frequency- Monthly
Publisher Name-SARC Publisher
ISSN Online- 2945-3585
Country of origin-PHILIPPINES
Impact Factor- 3.7
Language- English
Keywords
- Engineering and Technologies like- Civil Engineering, Construction Engineering, Structural Engineering, Electrical Engineering, Mechanical Engineering, Computer Engineering, Software Engineering, Electromechanical Engineering, Telecommunication Engineering, Communication Engineering, Chemical Engineering
Editors

Dr Hazim Abdul-Rahman
Associate Editor
Sarcouncil Journal of Applied Sciences

Entessar Al Jbawi
Associate Editor
Sarcouncil Journal of Multidisciplinary

Rishabh Rajesh Shanbhag
Associate Editor
Sarcouncil Journal of Engineering and Computer Sciences

Dr Md. Rezowan ur Rahman
Associate Editor
Sarcouncil Journal of Biomedical Sciences

Dr Ifeoma Christy
Associate Editor
Sarcouncil Journal of Entrepreneurship And Business Management
Marketing Mix Modeling: A Statistical Approach to Measuring and Optimizing Marketing Effectiveness
Keywords: Marketing Mix Modeling, Econometric Analysis, Budget Optimization, Channel Attribution, Predictive Forecasting
Abstract: Marketing Mix Modeling represents a sophisticated econometric framework that transforms marketing measurement from intuitive decision-making to evidence-based strategic planning through advanced statistical modeling techniques. The procedure employs multivariate regression analysis and time-series econometric approaches to decompose aggregate business performance into constituent elements attributable to specific marketing channels while accounting for external market factors and baseline trends. Contemporary implementations leverage computer-assisted optimization algorithms that navigate complex multi-channel investment landscapes, incorporating cross-channel synergistic effects and temporal variations to identify optimal resource allocation strategies. Predictive capabilities within MMM frameworks utilize multimedia communication synergies and adaptive learning mechanisms to generate reliable forecasts about future marketing effectiveness while maintaining statistical rigor through comprehensive validation techniques. The integration of Marketing Mix Modeling with enterprise planning systems enables organizations to translate analytical insights into actionable strategic decisions, addressing critical business challenges including budget optimization, channel performance evaluation, and marketing accountability demonstration across diverse industry sectors
Author
- Sumit Kumar Singh
- Independent Researcher USA