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You will learn to:
Understand the fundamentals of marketing mix modeling.
Evaluate the quality of marketing mix models.
Correlate sales and marketing spending using LightweightMMM.
Fine-tune marketing mix models for optimal results.
Skills
Data Science
Machine Learning
Optimization
Prerequisites
Good understanding of Python
Basic understanding of statistics
Basic understanding of marketing
Technologies
NumPy
Pandas
Matplotlib
Project Description
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.
Project Tasks
1
Introduction
Task 0: Get Started
Task 1: Import Necessary Libraries
2
Load and Explore the Dataset
Task 2: Load the Dataset
Task 3: Explore the Dataset
3
Prepare the Data and the Model
Task 4: Preprocess the Data
Task 5: Train a Marketing Mix Model
4
Assess the Model
Task 6: Check for Convergence
Task 7: Evaluate the Model
Task 8: Check for the Prediction Quality
Task 9: Check Parameter Estimations
Task 10: Assess Model Insights
5
Implement the Model
Task 11: Optimize Media Spending
Task 12: Persist the Model
Congratulations!