Marketing Mix Modeling (MMM) Project with Microsoft Excel: Level 1

Welcome to my first Marketing Mix Modeling (MMM) project repository. This project is an application of MMM techniques using Excel, focusing on analyzing the impact of various sales drivers on the number of account subscriptions (KPI) relative to an online book store.

Overview

In this project, I leverage Excel and its Analysis ToolPak to perform data exploration, regression analysis, and transformation techniques such as Adstock and Diminishing Returns to gain insights into the relationships between different factors and subscription numbers which will help the business derive future data-based marketing decisions.

Key Steps

1. Data Exploration: I begin by exploring the raw dataset and mapping each independent variable against the KPI (accounts subscriptions).
2. First Regression Modeling: A primary linear regression model is built using raw data.
3. Data Transformation: I perform Adstock and Diminishing Returns transformations on raw media data to enhance model accuracy.
4. Final Modeling: The final linear regression model is developed using transformed data, and coefficients are analyzed to measure the contribution and ROI of each factor to total subscriptions.
5. Business Recommendations: Based on the model results, I generate insights and recommendations for optimizing marketing strategies and maximize profit.

Repository Contents

• MMM project with Excel.xlsx: Excel file containing the complete MMM project detailing data, analysis, and recommendations.
• README.md: You're currently reading it! This file provides an overview of the project and its contents.

Getting Started

To explore the full details of the MMM project and delve into the analysis, simply download or clone this repository and open the MMM project with Excel.xlsx file in Excel.

Course Information

This project is inspired by the Marketing Mix Modeling course provided by Mass Analytics on Udemy. For those interested in enhancing their marketing analytics and regression skills, I highly recommend checking out the course.

Feedback and Contributions

Feedback, suggestions, and contributions are always welcome! If you have any questions or ideas for improving this project, feel free to open an issue or submit a pull request.