Recast

Upper funnel channels like Meta Prospecting drive direct sales when someone sees an ad then buys something. Every marketer knows this. But they also create assisted sales through channels like Brand Search. How does that work? Upper funnel activity often

TLDR: Recast's research team made model fitting for our Bayesian MMM 5-20x faster and reduced the median runtime from hours to just minutes. We'll explain how in this article. Runtimes limit how quickly you can deploy a model and how many iterations you c

Recast's research team developed a proprietary method that accounts for a paid media channel's changing footprint in your MMM. It corrects a blind spot that leads standard models to mistake a growing addressable market for a saturating market. Say you run

Get started with Recast MCP now We just shipped two new ways to use Recast inside the AI assistants your team already works with. Add our plugin to Claude in a single click, or connect over MCP from ChatGPT, Codex, or anywhere else. Either way, it's the R

A head-to-head simulation study benchmarking CausalPy, Google Matched Markets, Meta GeoLift, and Google Causal Impact across 32,000 model fits.

Many people's instincts when building a marketing mix model is to include as many control variables as possible. This often seems intuitively correct since many people are taught that "more controls is better" when they learn about regression analysis, bu

Many platforms ship two products under the same banner: an app your team clicks through and an API that feels like it was built by different people for different objectives. Moving between them (figuring out which endpoint maps to the screen in front of y

In this article: What is a counterfactual? - Intuitively understand the concept of counterfactuals Counterfactuals in marketing measurement - How this applies to measuring the impact of paid media spend Why RCTs are the gold standard - And why they're nea