Evidence-backed marketing decisions that unlock growth
The AI-powered incrementality platform leading enterprises use to optimize tens of billions in annual marketing spend.
Trusted by global companies managing $30B+ in annual ad spend
Analyzing $30B+ in ad spend every year teaches you one thing:
Getting clear signal on the impact of your marketing is the deciding factor between success and failure.
Run incrementality experiments to get clear signal
Identify the marketing activities causing business outcomes by comparing impacts from groups exposed to your marketing against groups who were not.
Partner with Haus’ expert team to configure on-demand Incrementality tests that measure the impact of your marketing investments.
Turn signal into business outcomes
Causal MMM
Allocate budget across channels with decision-ready data - updated every week.
Causal Attribution
Inform micro spend allocations down to the ad with evidence-backed attribution.
Architect: The Causal Marketing Agent
Architect uses causal data and frontier AI to spot risks and opportunities, conduct "what-if" analyses, and provide causal recommendations on next best action.
Driving ROI across your entire organization
Every member of your team is more effective, every AI tool in your stack is more reliable, and every dollar of spend works harder.
Your trusted source for evidence-backed marketing decisions
A community of experts at the world’s leading businesses awaits
Can A Model Predict Incrementality?
Stanford Professor Brett Gordon joins the podcast to discuss his influential research around predicting incremental impact of marketing campaigns using experiments.
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What To Do After A Bad Incrementality Test Result
Haus Measurement Strategists Dean Gordon and Ike Armstrong sit down to talk about next steps after that difficult moment when you get a test result that “punches you in the chin.”
Incrementality Testing at Scale: Lessons from Newton CMO Aaron Zagha
Newton Baby CMO Aaron Zagha on building a data-driven marketing org, betting early on incrementality, and driving 30% efficiency gains with Haus.
The latest from Haus
Fast, Confident, and Wrong: The Risk of Noisy Incrementality Tests
Fast, Confident, and Wrong: The Risk of Noisy Incrementality Tests
We simulated a year of marketing decisions 36 million times. Accuracy protected the business. Volume didn't.
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Is Meta's Incremental Attribution Outperforming Standard Attribution?
Is Meta's Incremental Attribution Outperforming Standard Attribution?
A year ago, Haus data showed Meta’s standard attribution performing better than their Incremental Attribution setting. Fresh analysis tells a new story.
High Demand, Higher Stakes: Measurement During Peak Season
High Demand, Higher Stakes: Measurement During Peak Season
In this guide, we outline a better way to plan, test, and measure marketing during peak demand periods.
In this guide, we outline a better way to plan, test, and measure marketing during peak demand periods.
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Four years ago, we were a small team with a big point of view. Today, we're trusted by the most sophisticated marketing organizations in the world.
