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Introduction
The landscape of digital marketing measurement has undergone a dramatic transformation. With the absolute deprecation of third-party cookies, the collapse of Multi-Touch Attribution (MTA) signal fidelity, and the rise of opaque, AI-driven bidding algorithms, performance marketers face an unprecedented challenge. They need to answer the ultimate question: Where should our next marketing dollar go to drive true incremental growth?
In response, Marketing Mix Modeling (MMM) has staged a massive resurgence. However, the legacy enterprise consultancies of the past—which delivered static, quarterly slide decks based on retroactive econometric modeling—are no longer viable for agile digital brands. In 2026, the modern standard is Causal MMM: continuous, machine-learning-driven modeling that calibrates statistical regression with randomized, real-world experiments.
Among the leading solutions driving this revolution are Recast, LiftLab, Measured, and Haus. Each of these marketing analytics platforms approaches the measurement challenge with a distinct methodology, operational cadence, and pricing model. Choosing the right software requires a deep understanding of your brand’s analytical maturity, data infrastructure, and decision-making speed. In this guide, we break down the definitive comparison of Recast vs. LiftLab vs. Measured vs. Haus to help you identify the perfect measurement engine for your business.
Quick Comparison Table
Before diving into the technical details of each platform, here is an at-a-glance comparison of how these four marketing mix modeling giants stack up in 2026.
| Platform | Core Philosophy | Calibration Loop | Best For | Onboarding Time | Estimated Cost (2026) |
|---|---|---|---|---|---|
| Recast | Bayesian MMM transparency & rigorous weekly forecasting | Weekly out-of-sample forecast validation | Analytical, data-science-first growth brands | 2–4 Weeks | $2,000 to $8,000 / month |
| LiftLab | Agile MMM & real-time media budget optimization | Two-stage modeling & continuous experiments | E-commerce, CPG, and DTC media buyers | 4–6 Weeks | Custom (Spend-based) |
| Measured | Enterprise-grade triangulated measurement | Massive automated geo-testing library | Mid-market to enterprise multichannel brands | 2–4 Weeks | $50,000 to $200,000+ / year |
| Haus | Experiment-first causal science | Geo-experiments with synthetic controls | Growth & finance teams prioritizing scientific proof | 3–5 Weeks | Custom (Basics, Core, & Plus tiers) |
Detailed Breakdown
Recast: Accuracy-First Bayesian MMM
Recast, headquartered in Brooklyn and founded by Michael Kaminsky and Tom Vladeck, stands as the champion of statistical transparency and Bayesian rigor. The platform operates with a firm “Nowhere to Hide” philosophy, openly exposing model coefficients, credible intervals, and posterior distributions to its users. Unlike black-box algorithms, Recast lets internal data scientists dissect every aspect of the model, making it a favorite for brands with high analytical maturity.
Rather than treating media performance as static, Recast’s model re-estimates channel ROIs weekly. This allows marketing teams to immediately capture seasonal shifts, creative fatigue, and competitive pressure. Recast is also highly regarded for its multi-stage funnel modeling, tracing upper-funnel demand generation (like TV or TikTok video views) down to lower-funnel search performance and branded affiliate clicks. Additionally, they have integrated their dedicated “GeoLift by Recast” to incorporate experimental lift results into the model calibration process, ensuring that Bayesian assumptions remain grounded.
Recast’s average annual contract lands at approximately $35,000, with typical pricing ranging from $2,000 to $8,000 per month depending on data complexity and channel scale. The software is ideal for mid-market to enterprise brands spending $1M to $50M annually who want deep, continuous econometric insights without relying on sluggish, human-heavy consultancies.
Pros: Extremely transparent Bayesian modeling with a clear view of statistical math; weekly automated refreshes; out-of-sample forecast validation scorecards to prove accuracy.
Cons: Requires a high level of internal analytical sophistication to maximize utility; the depth of technical data can feel overwhelming to non-technical, execution-only teams.
LiftLab: Agile MMM & Capital Allocation
LiftLab focuses on “Agile Marketing Mix Modeling” and real-time capital allocation. Where other platforms provide highly retrospective reports, LiftLab is designed to answer operational questions for day-to-day media buyers. It excels at separating platform auction-cost dynamics from actual consumer purchase responses.
A hallmark of LiftLab’s approach is its constraint-aware scenario planning. Instead of generating abstract budget recommendations that fail to reflect operational realities, LiftLab incorporates media caps, minimum spend commits, CAC threshold limits, and organizational budget boundaries into its media plan optimizer. The platform integrates a continuous stream of online and offline experimentation to ensure the underlying econometric curves are updated to capture diminishing marginal returns.
Pricing for LiftLab is custom, structured around the scale of media spend under management and the depth of experimental integration. It is a premier choice for e-commerce, direct-to-consumer (DTC), and challenger consumer packaged goods (CPG) brands that require an active forecasting and execution dashboard to balance multiple sales channels (including Amazon, retail marketplaces, and direct sites).
Pros: Outstanding for day-to-day media buying and direct budget rebalancing; constraint-aware scenario optimization prevents unrealistic recommendations; separates auction cost dynamics from baseline consumer responses.
Cons: Heavy implementation cycle requiring robust data pipeline setup; custom quote-only pricing makes it inaccessible for smaller-budget brands.
Measured: Unified Triangulated Measurement
Measured is widely recognized as the market leader in enterprise triangulated measurement. In 2026, the company continues to champion a “Triangulated” approach, combining Causal MMM, automated geo-testing, and direct ad platform signals into a cohesive, single source of truth. Under the leadership of CPO Mahesh Jeswani, Measured rolled out massive upgrades to its Causal MMM platform, providing enterprise teams with exceptional visibility and control over how models are constructed.
The defining strength of Measured is its massive, automated library of pre-built geo-testing and audience-split experiments. Measured handles the heavy data engineering, market selection, and deployment logistics, letting brands spin up tests across walled gardens (Meta, YouTube, TikTok) and offline media seamlessly. By feeding these continuous causal outputs back into the Causal MMM, the platform ensures that the econometric model is consistently calibrated against empirical ground truth.
Measured’s pricing is customized for enterprise buyers, with annual subscriptions typically starting at $50,000 and scaling past $200,000 depending on overall media scale. The platform provides a hands-on managed service element, making it perfect for large organizations that want to build a world-class marketing science stack without hiring an army of internal data scientists.
Pros: Massive automated testing library; fast 2–4 week onboarding; highly actionable executive-ready dashboards.
Cons: Premium enterprise price point; less customizable for technical teams wanting to alter raw code.
Haus: Experiment-Led Marketing Science
Haus, under Zach Epstein’s leadership, is a powerhouse of causal marketing science. Backed by over $55M in funding, Haus treats randomized experimentation as the supreme source of truth. The platform’s methodology rests on “experiments first, model second,” claiming that econometric models are structurally vulnerable to multicollinearity and noise without continuous, real-world holdouts.
Haus uses advanced synthetic control methodologies to design and run highly precise geo-lift and user-level experiments. The platform translates complex experimental results into a clear “Causal MMM” (cMMM) and “Causal Attribution” engine. In 2026, Haus expanded its product line into three structured plans: Basics (featuring self-serve US-only geo-testing on major platforms), Core (which includes international testing, dedicated Measurement Strategists, and native data warehouse syncs), and Plus.
Pricing for Haus is custom, designed for mid-market to enterprise-grade operations. The platform’s expert support, which includes direct guidance from PhD economists and measurement engineers, ensures that growth and finance teams can coordinate on scientifically defensible data.
Pros: Industry-leading geo-testing using synthetic controls; direct access to PhD-level marketing science support; very clean, intuitive UI.
Cons: Highly reliant on a continuous culture of testing; less robust for long-term retrospective planning if testing cadences slow down.
How to Choose
Selecting the best platform in 2026 depends on your operational design and internal technical talent. Consider these three critical factors when making your decision:
1. Internal Talent vs. Managed Platform: If your brand has a dedicated data science and analytics team that wants to dig into raw statistics and priors, Recast or Haus (with raw data warehouses) are the natural choices. If you lack in-house data scientists and need a platform that manages data pipelines, deploys tests, and delivers clean, executive-ready insights, Measured is the clear winner.
2. Cadence of Action: Are you adjusting budgets weekly across dozens of digital tactics? LiftLab’s agile, constraint-aware framework is tailor-made for performance-driven media buyers. Are you focused on proving overall portfolio efficiency to a skeptical CFO with randomized experiments? Haus’s causal-first framework provides the scientific backing needed to justify large-scale capital deployments.
3. Budget & Channel Diversity: Mid-market brands with tighter budgets and a heavy focus on digital spend can leverage Recast’s transparent weekly SaaS modeling. Larger, multichannel enterprises spanning TV, retail marketplaces, Amazon, and heavy digital will benefit most from Measured’s comprehensive triangulation and massive pre-built testing suite.
Frequently Asked Questions
What is Causal MMM, and why is it standard in 2026?
Traditional MMM relies on historical regression, which confuses correlation with causation. Causal MMM continuously calibrates the statistical model using the results of real-world randomized experiments (like geo holdouts), separating actual incremental lift from baseline organic conversions. In 2026, with third-party cookie deprecation, Causal MMM is the industry standard for privacy-native, defensible marketing measurement.
Do these platforms require cookies or tracking pixels?
No. None of these four platforms rely on individual-level tracking, third-party cookies, or PII. By utilizing aggregated media spend and transaction-level data, these platforms are entirely immune to privacy-related signal loss, Apple’s App Tracking Transparency (ATT), or state-level data privacy legislation.
Do I need an in-house data science team to run these platforms?
No. While Recast and Haus cater heavily to brands with internal analytics resources, platforms like Measured and LiftLab provide robust managed-service layers, dedicated strategist support, and automated experiment configuration, enabling standard growth-marketing teams to operate them successfully.
How long does onboarding take?
Most modern platforms can ingest historical data and launch their initial models within 2 to 6 weeks, a massive upgrade from the traditional consultancies of the past that took several months to construct static models.
Verdict
In 2026, there is no single “best” MMM platform, but rather a “best fit” for your specific organizational design.
If your organization is data-science-first and demands radical statistical transparency with “nowhere to hide,” Recast is the premium choice.
If you are an e-commerce or retail brand needing day-to-day agile budgeting with strict platform constraints and auction-cost modeling, LiftLab delivers the most execution-focused toolkit.
For multichannel enterprises that require a comprehensive, turnkey ecosystem combining Causal MMM with an automated, massive library of geo-testing across walled gardens, Measured stands as the definitive, industry-standard solution.
Finally, if your marketing strategy is anchored on scientific experimentation and causal validation above all else, Haus is the premier marketing science platform for your team.
Prices and features mentioned are accurate as of the date of publication. Always check the official provider website for the most current pricing and availability.
