Attribution & marketing mix modeling · Haus

Haus

Incrementality-testing platform that runs randomized geo and holdout experiments to measure marketing's causal lift.

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Haus is incrementality-testing-led, like Measured: rather than modeling attribution from click or view data, it designs and runs controlled experiments — geo holdouts, PSA (public service announcement) tests, or matched-market tests — and measures the actual causal difference in outcomes between exposed and unexposed groups. This sidesteps the assumptions baked into multi-touch attribution and gives a statistically defensible answer to whether a channel drove incremental revenue, at the cost of needing enough scale and patience to run a proper test rather than reading a dashboard in real time. Output is typically used to calibrate or validate a marketing mix model, or directly to make channel-level budget decisions.

At a glance

Vendor Haus
Pricing model Quote only
Free tier No
Deployment Cloud
Open source No
Best for Advertisers with enough scale to run randomized holdout experiments and want causal proof of channel lift, not modeled estimates.

Pricing

Custom quotes based on ad spend and number of tests; pricing is not published.

Pricing has not been verified yet — see the vendor's site.

Features

  • Randomized geo holdout and matched-market experiment design
  • Statistical significance testing on experiment results
  • Incremental lift measurement by channel
  • Experiment calibration for marketing mix models
  • Automated test scheduling and monitoring
  • Channel-level budget recommendation from test results

Integrations

Profile last reviewed September 21, 2026

Alternatives

Measured Incrementality-testing platform that measures marketing's true causal lift through geo-based experiments rather than modeling clicks. Recast Bayesian marketing mix modeling delivered as a managed SaaS, combining statistical MMM with geo experiments.

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Terms to know

Incrementality testing An experiment measuring the true causal lift of a marketing activity by comparing it against a held-out control. Statistical significance A threshold judgment that an observed effect is unlikely to be due to chance alone, given the data and test used. Causal inference The set of methods used to determine whether one variable actually causes a change in another, not merely correlates with it. Marketing mix modeling A statistical technique estimating how much each marketing channel and external factor contributes to results. Multi-touch attribution An attribution method that splits conversion credit across every touchpoint in a customer's path, not just one.

Related guides

How to choose a marketing attribution or MMM tool Attribution, media mix modeling, and incrementality testing answer the same question three different ways. Pick the method before you pick a vendor. How to measure marketing without third-party cookies First-party data, server-side collection, modeled conversions, clean rooms, MMM and incrementality tests — the methods that replace what cookies used to do. How to run marketing mix modeling What data an MMM needs, the build-vs-buy choice between open-source libraries and managed vendors, and how to validate the model with real experiments.