Overview
Haus is a causal marketing measurement platform that enables enterprise brands and performance marketers to evaluate the incremental return of their advertising spend. By combining geo-based and time-based experiment frameworks with data science methodologies, Haus helps organizations separate true incremental revenue from correlational sales data.
The platform integrates marketing mix modeling and multi-channel attribution with on-demand incrementality testing. Haus also includes Architect, an internal AI agent that analyzes causal experimental data to highlight budget risks, run scenario analyses, and provide recommendations for campaign adjustments across online and offline media channels.
Key features
- Incrementality experiments: Conduct geo-based and time-based holdout tests across digital ad platforms and offline media.
- Causal media mix modeling: Receive weekly updated cross-channel budget allocation data grounded in experimental test results.
- Causal attribution: Track incremental revenue and order lift down to individual campaign ad creative levels.
- Architect AI agent: Utilize an automated optimization assistant to run what-if budget simulations and identify spend risks.
- Data platform integrations: Connect major ad networks, ecommerce platforms including Shopify and Amazon, and data warehouses.
Pros and cons
Pros
- Combines experimental incrementality testing directly with media mix modeling and attribution metrics.
- Delivers weekly model updates rather than relying on static quarterly readouts.
- Provides support from measurement strategists and data science teams on elevated tiers.
Cons
- Requires booking a demo as standard pricing details are not published publicly.
- Features such as non-geo channel testing and advanced MMM models are restricted to higher tier plans.
Best for
- Enterprise brands
- Performance marketing teams
