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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 paid search in 30% of the country, then expand […]

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, but unfortunately the approach is often wrong, and in this article

In this article: By the end, you’ll have a deep and practical understanding of counterfactuals, including how to build and evaluate them for your own marketing tests. All the code and sample data behind the forecasting and propensity-score-matching examples in this guide are available on GitHub so you can run

If you’ve ever worked in marketing you’ll hear the word “incrementality” constantly. Incrementality doesn’t care about someone who happened to see your Facebook banner ad before converting. It cares about people who converted because they saw your Facebook banner ad. It is a causal question: is marketing causing conversions? And

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 Recast engine itself

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 you, what the payload should look

We just shipped multichannel impact tests – a new way to incorporate evidence from experiments where multiple channels moved at once into your marketing mix model. Often, a team will run an incrementality test that simultaneously increases the budget on two paid media channels (like testing both YouTube and TV

We’re continuing to ship APIs at Recast. A few weeks ago we shipped programmatic access to our Planning and Optimization tools, and today we’re going further by opening up the Insights tab. It’s the homepage of Recast, where teams dig into channel performance, period comparisons, model diagnostics, and much more.

Recast APIs are Here Recast now connects to everything – your AI agents, planning tools, data warehouse, and more. We just shipped API access to Recast. Your AI agent can now run a Recast optimization, your budget spreadsheet can trigger forecasts, and your Tableau dashboard can pull Recast outputs on

GeoLift by Recast is built to help you design and analyze rigorous geographic incrementality experiments. However, when using GeoLift or any other similar tools there’s one issue you should always be aware of: the problem of “generalizability”.  The problem of “generalizability” plagues all types of experimental design – researchers and