Optimizing Marketing Spending with Python

Marketing mix modeling, the study of how each marketing channel contributes to revenue generation, becomes increasingly important as businesses need to optimize their budgets. Understanding the relationships between marketing expenses, sales, and other variables is essential for accurately analyzing data and predictive modeling and decision-making.

In this project, we will explore marketing spending optimization using a Python library called LightweightMMM. We will also explore how to measure the relations between sales and marketing spending to make better decisions.

We will start with the fundamentals by assessing and preparing data. We will then explore LightweightMMM and its potential to account for factors like trends, seasonality, and diminishing returns. We will then be ready to move on to evaluating our model quality and using it to find the optimal spending for each marketing channel, depending on our budget.