Not every lift-testing or incrementality experimentation platform is created equal. Customers have used GeoLift by Recast to design and analyze hundreds of incrementality experiments using the platform’s best-in-class algorithms. We believe these have led to the most accurate and actionable test results in the market. In fact, many Recast customers […]
Why Recast’s Synthetic Control Methods are Better Read More »
If you’ve already run geographic-based incrementality experiments using other tools or methods, you can use GeoLift by Recast –– to reanalyze your historical test data. This can help validate previous results from other vendors or in-house methods, apply more sophisticated synthetic control methods, or simply get a second opinion on
How to Reanalyze Past Incrementality Experiments with GeoLift by Recast Read More »
Time series data is special because the order in which observations occur is meaningful. For example, looking at sunglasses sales at Sun-Glasses-For-All (an imaginary brand), we can see that knowing yesterday’s sales gives you information about today’s sales. And sales this time last week gives you information about today’s sales
Gaussian Processes for Time Series Read More »
There’s a lot of confusion from marketers about the right way to use lift tests or incrementality experiments to calibrate the results of a media mix model (MMM). We see questions like: You need to be thoughtful when incorporating information from incrementality experiments into an MMM, so in this post
How to Use Lift Tests to Calibrate an MMM Read More »
Most marketers want to test everything. But you don’t have unlimited budget, time, or team capacity. So you need to be ruthless about prioritizing what actually gets tested. That means knowing where a test will generate the most value — not just statistically, but strategically. Here’s how we help the
How to Prioritize Experiments When You Can’t Run Them All Read More »
Model transparency and accountability are foundational to our work at Recast. We openly publish model performance metrics to customers and use them internally to assess and improve the quality of Recast’s media mix models. Three of these metrics – Median Holdout Accuracy, Marketing Dollars Saved Using Recast, and Average Annual
Recast Model Performance Metrics Explained Read More »
Media mix modeling (MMM) isn’t theoretically hard to do, but it’s really hard to do well. Every step of building an MMM is a potential place for it to break, inject bias, and can lead to inaccurate results. One of these “steps” we find that is often not discussed enough
Why Model Configuration Makes or Breaks Your MMM Read More »
At Recast, our mission is to build the world’s most rigorous Marketing Mix Modeling (MMM) platform. And this starts right from the very beginning – during our configuration process. If you don’t get this right, your model is set to be wrong from the very beginning. Our configuration process is
How Recast Configures the World’s Most Rigorous MMM: A Step-by-Step Guide Read More »
At Recast, we set out with an ambitious goal: to build the world’s most rigorous Marketing Mix Modeling (MMM) platform. This mission influences everything we do, from the way we allocate resources internally to how we obsess over model validation. At its core, our approach is about scientific rigor, transparency,
How Recast Is Building the World’s Most Rigorous MMM Platform Read More »
Last updated: August, 2024 At Recast, we hold strong views about the right way to do Media Mix Modeling (MMM) that can be surprising to teams accustomed to outdated methodologies. Specifically, we believe that in order to be valuable, a Media Mix Model must be: This article explains how these
The Recast Philosophy Read More »
