Haus

Name: Haus
Brand: Haus

AI Native

Smarter marketing investments start with Haus.

Est 2021|Valuation $450.3M|Raised $61.9M|23 Ref Customers

LinkedIn 24K|Web traffic 38K|FTEs 180|HQ San Francisco, California, US

MarketsMarketing Mix Modeling (MMM)Marketing Attribution

Published Oct 15, 2026

01Common complaints

1. Organizations that require extensive third-party reviews to de-risk procurement. - Haus has limited coverage on major review platforms and lacks the review volume available for some mature vendors. 2. Organizations that cannot operationalize experiments. - If a company cannot reliably set holdouts, maintain consistent media execution, and accept the time needed to achieve statistical significance, an experiment-first approach will likely underperform. 3. Organizations primarily focused on tactical, daily attribution for optimization. - An MTA-focused tool (e.g., Northbeam) may better match the workflow where daily optimization and near-real-time channel/creative reporting are the priority. 4. Organizations with strict cost constraints and only occasional testing needs. - For periodic tests, pay-per-test vendors or lower-cost subscription offerings may be more economical than an enterprise sales-led contract.

Disqualifiers (poor fit)

Very small budgets or geographically concentrated demand.
Organizations unwilling to run holdouts due to perceived revenue risk.
Teams that primarily require day-to-day attribution dashboards rather than causal decisioning.

Publicly acknowledged limitations

Public materials emphasize privacy-preserving measurement and causal inference but do not provide a comprehensive list of platform constraints.

Likely limitations to verify (not fully documented publicly)

Feature availability by pricing tier: details are unclear without published tier and feature documentation.
SSO/SCIM/audit log capabilities: not clearly documented in public materials and should be validated during diligence.
Data residency and regional hosting options: not clearly documented publicly and should be requested during procurement.

Roadmap indicators

Haus announced Causal Attribution as a newer product in mid-2024. Procurement teams should ask whether specific features are generally available (GA) versus in beta, and whether functionality is consistent across channels.

What’s missing (churn reasons, deal-breakers, negative reviews)

With limited presence on major review platforms, it is difficult to quantify common complaints (support quality, bugs, missing features) from independent customers. This is a meaningful gap versus competitors with extensive user reviews.

02Why they buy

Unique selling points that appear supported by public materials

An experiment-first measurement posture with a coherent product set (GeoLift, Causal MMM, daily causal attribution).
Publication of aggregated experiment analyses that can help teams set benchmarking expectations.
Claims of SOC 2 Type II compliance suitable for enterprise procurement discussions.

Where competitors may have clear advantages

Measured: breadth of experiment library, integrations, and a portfolio optimization narrative.
AppsFlyer: depth for mobile-first companies and consolidation within a mobile measurement platform.
Low-cost vendors (Stella/Maxma): transparent pricing and lower-cost entry for organizations that require less enterprise support.

Common themes in "why customers chose Haus" (inferred from case studies)

Desire to challenge platform-reported ROAS and standard attribution.
Need for measurement approaches that function under privacy constraints.
Preference for causal/experimental approaches rather than purely correlative MMM.
Interest in higher testing velocity where organizational processes permit frequent experiments.

03Field reports

Review-platform availability (G2/Capterra/TrustRadius)

The vendor maintains profiles on major review platforms, but public, structured review coverage is limited compared with many established SaaS vendors, which increases reliance on vendor case studies and informal community commentary for customer perspectives.

Direct quotes (vendor case studies / community)

Because structured review platforms have limited Haus-specific content, the most quote-rich sources are vendor case studies and community posts. These are vendor-provided or self-asserted and should be considered marketing-selected: Vendor website testimonials/case studies (marketing-selected): - A senior marketing leader at a major entertainment brand: “The Haus platform provided clarity in our data and led to better investments that materially reduced costs.” - An executive at a consumer goods company: “The insights and ROI from early testing paid for our contract within the first period.” - A fintech customer case study: “We get pitched measurement partners all the time, but Haus felt different. Using their experimentation platform has created immediate business value…” Community / social (unverified; context-limited): - Community posts summarize aggregated experiment sets and report positive average lifts in client KPIs. These posts are self-asserted and not independently verified.

Consumer finance app — Geo experimentation velocity and budget reallocation

- Reported outcome: The customer ran multiple geo experiments over a period and estimated meaningful annual budget reallocation and an increase in core customer metrics. - Critical evaluation: - These results are vendor-published and rely on a customer estimate; no independent verification or methodology disclosure was found in public sources. - The detail about testing cadence suggests Haus can support relatively high experiment throughput for teams that are organized to run tests.

Aggregated platform analysis — benchmark claims

- The vendor published an analysis aggregating many client experiments over an extended timeframe and reported average lifts on a major advertising platform and additional insights about omnichannel impacts. - Critical evaluation: - This is a vendor-produced meta-analysis and may be influenced by selection effects (which brands chose to test, experimental design, and definitions of an "experiment"). - Publishing quantified, experiment-based benchmarks aligns with Haus’s experimental measurement positioning, but readers should evaluate selection and methodology assumptions when interpreting these benchmarks.

04Best fit

Company sizes (practical guidance)

- Most suitable: mid-market to enterprise advertisers with sufficient geo dispersion and spend to run statistically meaningful holdouts. - Often a good fit when the organization has: - Multiple channels (e.g., Meta, Google, CTV, YouTube, retail media). - Omnichannel sales (direct-to-consumer plus retail/offline) where platform-reported ROAS may under-credit upper-funnel activity. - Finance scrutiny requiring defensible causal measurement.

Industries / verticals

Based on public customer mentions and case studies, Haus appears to have traction in:

Direct-to-consumer (DTC) and e-commerce
Consumer subscriptions and services
Omnichannel consumer brands with offline lift considerations

Roles and personas

Decision makers: VP Growth, VP Marketing, CMO, Head of Performance Marketing, Marketing Analytics/Measurement lead.
Key collaborators: Finance (FP&A), Data/Analytics, Media agency partners.

Qualification criteria (good fit)

Commitment to a testing roadmap rather than one-off experiments.
Stable KPI definitions and sufficient historical data to support experiments.
Ability to enforce geo splits/holdouts without frequent contamination.

05The pitch

What the product does (core value proposition)

Haus is a B2B marketing measurement and optimization platform focused on incrementality—that is, estimating the causal impact of advertising by comparing outcomes in exposed versus unexposed groups. In practice, Haus frames its offering around experiment-grounded measurement rather than click-based attribution, and converts experiment results into recommendations for budget allocation. Haus offers a set of products oriented around experiment-driven causal inference: - GeoLift: on-demand geo/region experiments (treatment vs. holdout). - Causal MMM: media mix modeling framed as grounded in experimental results. - Causal Attribution: daily/ongoing channel incrementality reporting that adjusts platform metrics using experiment-derived incrementality factors.

Target market and market positioning

Haus appears positioned for: - Mid-market to enterprise brands with meaningful paid media budgets (especially omnichannel and/or privacy-constrained environments). - Performance/growth and marketing analytics teams that need measurement they can defend to finance/executives. - Brands that can support experimentation requirements (geo-level testing, holdouts, stable KPI tracking, sufficient scale for statistical power). Public materials reference use of AI technologies and describe vendor-provided consulting; the offering combines software with services rather than being a purely self-serve product.

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