Marketing mix modeling: Python tutorial with example dataset

Marketing mix modeling is a data-driven technique used to evaluate the impact of various marketing channels, such as TV, radio, and newspapers, on a company's sales or conversions. With the advent of online marketing and big data, marketing is increasingly viewed as a mathematical discipline, where methods like marketing mix modeling offer significant opportunities for data science and machine learning applications. This modeling technique is distinct from other attribution models as it provides a comprehensive measure of the impact of marketing channels over different time periods. The article provides a detailed guide on building a marketing mix model using Python, beginning with exploratory data analysis to assess relationships between variables, followed by constructing a model using ordinary least squares regression. The goal is to identify high ROI channels and optimize marketing budgets while predicting future outcomes. The effectiveness of the model is evaluated using metrics such as the adjusted R-squared and p-values, indicating the significance of each channel, with practical insights into how well the model predicts sales based on marketing spend.