Designed a unified, algorithmic marketing analytics architecture that merges top-down market mix modeling with bottom-up multi-touch attribution. This engine allows enterprises to mathematically prove ROI across advertising channels and dynamically redistribute budgets to maximize Return on Ad Spend (ROAS). 1. Integrated macro-level Google Merchandise Store spend logs with micro-level Criteo touchpoint sequences (2.5GB+ dataset). 2. Architected a highly tuned Marketing Mix Model (MMM) using Meta's Robyn framework, leveraging geometric adstock transformations and Hill functions to quantify lagged ad effects and diminishing returns. 3. Developed robust Multi-Touch Attribution (MTA) models, benchmarking Markov Chains and Shapley values against traditional heuristic rules to properly allocate conversion credit. 4. Designed a mathematical Reconciliation Engine utilizing non-linear solvers to fuse MMM budget bounds with MTA crediting, maximizing global Marginal ROAS. 5. Evaluated model precision using NRMSE, MAPE, and algorithmic fairness metrics against naive last-click baselines. 6. Deployed a full-stack Streamlit dashboard to visualize ROI saturation curves, touchpoint heatmaps, and actionable budget reallocation strategies.