This capstone project develops an end-to-end Marketing Mix Modeling (MMM) and optimization framework for Citizens Financial Group to improve the allocation efficiency of marketing spend across multiple marketing channels and products. Traditional attribution systems frequently over-credit last-touch digital interactions while underestimating the cumulative impact of offline and upper-funnel channels. To address this limitation, we constructed a geographically pooled econometric model using weekly Designated Market Area (DMA) panel data spanning multiple years. The modeling framework separates baseline demand drivers, such as seasonality, macroeconomic conditions, holidays, and branch footprint, from incremental demand created by marketing channels. Marketing variables were transformed using adstock functions to capture lagged carryover effects and diminishing returns. Weighted Least Squares regression was then used to estimate statistically robust channel contributions. Outputs were translated into actionable business metrics including contribution share, cost per acquisition, incremental bookings, and return on investment (ROI). Based on estimated response curves, we developed a constrained budget optimizer that reallocates spend toward channels with stronger marginal returns. Scenario simulations identified approximately 1.3% to 19% potential growth without increasing total spend across products, demonstrating the business value of data driven reallocation. This project illustrates how econometric modeling can bridge advanced analytics and executive decision-making in financial services marketing.
Mentor: Alessio Brini
