Measured vs. Haus: Which Incrementality Platform Fits Your Team?
A single experiment can tell you what happened last quarter. Real advantage comes from turning causal proof into a budget that keeps adjusting, every week, across every channel.
Key Advantages of Measured Over Haus
One model, full budget, every week
Triangulation built on full-funnel signal, not a pixel
Built for continuous reallocation, not just experiment findings
Real-time decisioning for performance marketers
Measured combines always-on media mix modeling with native incrementality testing, giving you a continuously updated, causally grounded view of what’s driving revenue across every channel.
We refresh on demand, weekly by default, so you can see what’s working while budget is still in motion, not after the quarter closes. Our models are triangulated by design: incrementality experiments establish causal truth, MMM extends that truth across your full portfolio, and platform attribution is treated as a comparative signal, never as ground truth on its own. Market selection for our experiments is matched, not randomized: we build test and control pairs from a modeled view of each market’s historical performance, then validate the match with synthetic-control-style placebo testing before a test ever runs.
Experiment-first incrementality with a pixel-based attribution layer
Haus approaches measurement from a marketing science perspective, putting causal experiments at the center of the platform. Its geo-based incrementality tests are the core product, with a Causal MMM layer, launched in October 2025, added on top to extend into scenario simulation and saturation analysis. Academic economists sit on its science advisory board, which gives the methodology real credibility with analytically minded teams. Market selection for those tests is randomized.
On paper, that adds up to triangulation: experiments, MMM, and attribution. The difference is in the attribution leg. Haus’s attribution runs on a pixel, which does a solid job capturing click-to-convert ecommerce activity but can’t see view-based media, cross-device behavior, or the full-funnel, omni-channel paths that define enterprise media strategies. For a mid-market or SMB advertiser focused on ecommerce and click-driven media, that tradeoff may never surface. For enterprise teams running upper-funnel, offline, and omni-channel investments, it means a meaningful share of the budget sits outside what the attribution layer can observe, and the translation work falls back on the team.
Measured vs. Haus: How They Compare
Capability
Measured
Haus
What are they saying about Measured
See what measured can find in your media mix
Frequently asked questions
Both platforms triangulate experiments, MMM, and attribution, but they’re built on different foundations. Haus is experiment-first: geo testing is the core product, with a Causal MMM layer added in Oct 2025 and a pixel-based attribution layer that reads click-to-convert activity. Measured triangulates incrementality testing, media mix modeling, and complete platform attribution from the start, covering view-based, cross-device, and omni-channel media, with a managed analyst layer that turns results into continuous budget decisions.
Both run rigorous geo-based incrementality experiments; Haus was built around this method first and has strong academic backing on its science advisory board. Measured runs incrementality testing as one of three integrated methodologies, using it to calibrate an always-on MMM rather than as a standalone product.
No, by choice rather than limitation. Random assignment is a valid, available method, and Measured could use it, but with a limited pool of geos, pure randomization can still leave test and control groups poorly matched by chance. Measured instead builds matched-market pairs from a modeled view of each market’s historical performance, then validates the match with synthetic-control-style placebo testing before a test ever runs, which is a stricter bar than randomization alone.
Measured fits enterprise and scaling DTC brands that need measurement to be defensible across marketing and finance, with a managed layer and paired analyst turning triangulated results into an always-on budget plan.
Haus fits teams building a marketing science function from scratch, or brands that want causal experiments to anchor budget conversations early, before continuous full-funnel reallocation is the primary need. Typically SMB or mid-sized businesses benefit most from Haus.