MMMs are powerful models and that means they’re subject to what modelers call “over-fitting”. The idea is that you can build a model that fits really, really well to the data that the model is trained on, but that hasn’t found the actual underlying causal relationships in the data. Over-fitting …
Understanding Overfitting in Media Mix Modeling: Risks, Metrics, and Validation Read More »
Correlation vs causation is a huge marketing measurement problem that traditional methods often fail to account for and can lead to biased conclusions and misguided budget allocation. This is where Directed Acyclic Graphs (DAGs) come into play. DAGs offer a powerful, visual approach to mapping out and clarifying causal relationships …
How do Causal Directed Acyclic Graphs (DAGs) work in Marketing Mix Modeling: Read More »
Media mix modeling at the enterprise level can be quite complex but, when done well, has a huge impact across the whole organization. Here are 5 lessons we’ve learned to help make MMM work at the enterprise: 1. At the enterprise level, you need one central point of contact who …
5 Key Lessons for Successful Enterprise-Level Media Mix Modeling Read More »
Over the past few decades, the scientific community has grappled with what is now known as the replication crisis—a widespread recognition that many significant results published in academic research cannot be replicated under rigorous testing. This crisis has shown critical flaws in traditional statistical methods, particularly the over-reliance on P-values, …
The Replication Crisis: Challenges and Lessons for Modern Statistical Practices Read More »
Is AI revolutionizing media mix modeling? People have been asking me about how AI is changing media mix modeling, and the answer really depends on exactly how you define “AI”. The Role of Large Language Models in MMM Large language models, including ChatGPT, don’t really have a lot of applications …
Can you trust your media mix model? That is an obvious but not asked enough question that everyone using MMM should consider – in-house, with a vendor, it doesn’t matter. As you try and answer it, you’ll run into overfitting, which is one of the biggest problems in MMM. Overfitting …
Evaluating Media Mix Model Accuracy: The CRPS Score in Backtesting Read More »
For many businesses, email programs are a huge revenue generator. For every $1 marketers spend on email marketing, they receive $36 in return, according to 2,000 marketers who responded to Litmus’ 2020 State of Email Survey. Emails play the important role of nursing customers along their buying journey, drive retention, …
People often ask us about the differences between Recast’s next-generation MMM platform and Facebook’s open source MMM tool, Robyn. In this blog post we do a compare-and-contrast between these tools – we’re obviously biased but will do our best to lay out the pros and cons of each tool as …
The popularity of Marketing Mix Modeling (sometimes known as Media Mix Modeling, or simply MMM) has increased rapidly over the last few years as marketers – reliant on digital-tracking methods like multi-touch attribution – grapple with the implications of a reduced ability to track people across the internet due to …
Shifting from Legacy to Modern Marketing Mix Modeling Read More »
Nearly every business has an annual cycle: seasons where the business waxes or wanes, or specific dates like Cyber Monday, Memorial Day, or Mother’s Day, which see enormous spikes in sales. Imagine you have a business that sells hot wings — when do you think the busiest time of the …
You’re probably modeling seasonality the wrong way Read More »
