Research Reports

We simulated a year of marketing decisions 36 million times. Accuracy protected the business. Volume didn't.

A year ago, Haus data showed Meta’s standard attribution performing better than their Incremental Attribution setting. Fresh analysis tells a new story.

We analyzed dozens of incrementality experiments to learn how leading studios and streaming platforms use testing to answer the questions that matter most.

AppLovin is proving it can drive efficient DTC growth — now the question is how durable that performance is as the channel matures.

How automating hundreds of causal models a week – and grading them with a blind exam – yields better outcomes for businesses.

We analyzed over a hundred incrementality tests before, during, and after BFCM 2025 and uncovered dramatic delayed conversion effects.

Can TikTok scale? How much omnichannel impact does TikTok have? Haus’ analyses of hundreds of TikTok incrementality tests answers these questions and more.

Our large-scale analysis of real-world, branded search incrementality experiments unpacks when branded search is most effective and how best to leverage it.

An exclusive Haus analysis show Meta is incremental in most cases — but is the platform's move toward automation improving incremental efficiency?

An exclusive Haus analysis shows YouTube often delivers powerful new customer acquisition and retail halo effects that traditional metrics miss.

Acknowledging uncertainty enables brands to make better, more profitable decisions.

Post-treatment windows offer a unique glimpse into the lingering impacts of advertising campaigns after they’ve concluded.

We analyzed experiments from leading brands to understand the incremental impacts of including vs. excluding branded terms in PMAX campaigns.

It is profitable to ignore statistical significance when making marketing investments.