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MMM Master Classes
MASS Analytics MMM Master Classes are designed to help you master the full process of delivering a marketing effectiveness project. Useful insights and tips are shared to help you develop an in-depth knowledge around MMM.
Whether you are an advertiser or an agency, you will find in these classes all what you need to know to run MMM projects effectively and efficiently.
Introduction to MMM Master Classes
We have designed a comprehensive course on Marketing Mix Modeling composed of eight episodes that cover the full process of delivering a MMM project to help you master the MMM practice.
In each episode we share different perspectives and best practices based on MASS Analytics’ experience running MMM for multiple verticals.
Introduction to Marketing Mix Modeling
MMM is based on applying advanced statistical methods (econometrics) to historical data to understand the impact of every single sales driver, measure the MROI, and predict future performance.
Discover the world of Marketing Mix Modeling, how it fits in the whole Marketing Strategy, and how it became an essential tool for data-driven decision making.
The Marketing Mix Modeling Workflow
A successful MMM exercise needs to be reliable, repeatable, scalable, and accessible to people with little marketing analytics background. Adhering to a clear and structured workflow is therefore a must.
Discover the workflow and the different phases involved in the process of creating an efficient Marketing Mix Modeling project end-to-end.
Data Required in MMM
The first step in ensuring the success of a Marketing Mix Modeling project is to make sure the data collection phase captures all the data needed for the project.
Learn about the different data sources and categories that need to be collected and discover tools that can help you run this phase successfully.
Once all data needed for the project is collected, the data check phase starts. Exploring, charting, and analyzing each piece of data is an essential step.
Learn about some of the important univariate and multivariate statistics as well as exploration techniques that help you build sound modeling hypothesis.
A robust model should make sense both statistically and commercially. Raw variables need to be transformed to reflect the true consumer behavior.
Discover the most popular processors in MMM including AdStock and learn about calendar variables, processed variables, and chain processing.