Marketing Analytics • Attribution • R Programming

MEDIA MIX
MODELING ANALYSIS

Built a Media Mix Modeling analysis in R to evaluate marketing channel performance, estimate the relationship between media spend and revenue, and develop budget optimization recommendations using regression-based marketing analytics.

Project Type: Marketing Analytics
Tools: R, RStudio, Quarto
Focus: Attribution + Budget Optimization
Output: Interactive HTML Report
01

PROJECT OVERVIEW

Business Problem

Marketing teams often need to understand which channels are driving performance, where budgets may be over-allocated, and how spend should be rebalanced to improve return on investment.

Objective

The goal of this project was to build a simplified Media Mix Model that connects marketing spend to performance outcomes and supports data-driven budget allocation recommendations.

02

METHODOLOGY

  • Prepared and structured marketing spend data for analysis.
  • Explored relationships between media channels and business performance.
  • Used regression modeling to estimate channel impact.
  • Evaluated diminishing returns and marginal channel contribution.
  • Translated model findings into budget reallocation recommendations.
03

TOOLS USED

R RStudio Quarto Regression Analysis Marketing Mix Modeling Budget Optimization Data Visualization Marketing Analytics
04

FULL REPORT

Interactive Quarto HTML Report

View the full Media Mix Modeling analysis, including code outputs, charts, model interpretation, and recommendations.

Open Full Report →
05

PORTFOLIO VALUE

This project demonstrates applied marketing analytics, econometric thinking, regression modeling, business interpretation, and the ability to translate technical analysis into marketing budget recommendations.

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