To help marketers better navigate the world of measurement and attribution, we’ve partnered with Facebook and Fospha to design a free online course.
Marketers need to know which of their marketing efforts have worked best — and this is obviously much easier said than done.
Making sense of nonlinear consumer paths to purchase is anything but straightforward.
“This is exactly why marketing attribution has become so important,” says Benjamin Royon, Marketing Science Partner at Facebook.
This microclass helps marketers understand the benefits and limitations of attribution.
What does the measurement and attribution microclass cover?
It starts with the basics — an overview of different models, why data quality is so important, and use cases for certain approaches.
It then works its way up to more advanced topics — statistical attribution models, and how marketing mix modeling works together with multi-touch attribution modeling.
Who teaches this course?
The course is taught by marketing experts from each Facebook and Fospha.
Benjamin Royon, Marketing Science Partner at Facebook, specializes in driving business impact with an approach grounded in marketing analytics and measurement. His areas of expertise include marketing attribution, experimental design, causal inference, and meta-analysis. He holds a Masters in Management from ESCP Europe
Alexandra Darmon, Head of Data Science Research at Fospha Marketing, specializes in the math and engineering behind marketing attribution models. She built and optimized Fospha’s multi-touch attribution model using linear regression, causal models, and advanced Markov Chain theory. Alexandra holds a Masters of Science in Mathematical Modeling and Scientific Computing from Oxford.
Needless to say, both are experts in their fields and have ample wisdom to impart.
What will I be able to do after taking the course?
By the end of this microclass, you will gain lots of insight around measurement and attribution, including:
- (Read more...) different attribution models work
- Recent changes in the attribution landscape
- Different types of approaches and solutions for measurement
- Main challenges and benefits of attribution
- Types of data required for attribution
- Advantages of using a data driven model compared to a standard MTA model
- How Experiments, MTA, and MMM can work together
- Common challenges clients face with attribution implementation and how to overcome them
For more information about the course and how to get started, visit the course page here.