Liftlab - Logo
Request a Demo

Trusted by the Category Leaders Who've Outgrown Their Dashboards

From global consumer brands to high-growth DTC, LiftLab is the measurement system for CMOs and CFOs. They choose LiftLab when platform attribution no longer suffices. 

Retail jewelry — global

+9.5% revenue

from a 2% budget shift

Entertainment — enterprise

7 → 13 channels

weekly mROAS reallocation

Financial software

+19% gross revenue

year-end window

DTC apparel

3.4x TikTok spend

at max profit

Ecommerce grocery

13 channels optimised

weekly CAC forecasts

You’ve Been Asked to Evaluate LiftLab.Here’s Why That’s a Harder Job Than It Looks.

You are evaluating a measurement system that will influence multi-million dollar budget decisions, shape the CMO’s narrative to the board, and reflect on your professional reputation. The stakes are significant, and potential pitfalls are real.

You’ve Been Asked to Evaluate LiftLab.Here’s Why That’s a Harder Job Than It Looks.

You are evaluating a measurement system that will influence multi-million dollar budget decisions, shape the CMO’s narrative to the board, and reflect on your professional reputation. The stakes are significant, and potential pitfalls are real.

01 — THE VENDOR BLACK BOX

Most MMM vendors call their methodology “proprietary.” That word should end the conversation.

If you cannot review how a model reaches its conclusions, you should not approve its recommendations. A vendor unwilling to explain their approach to adstock decay, uncertainty quantification, and calibration methodology is not protecting intellectual property; rather, they are avoiding scrutiny. You are responsible to your CMO and CIO for only endorsing what you can verify.

02 — THE BUILD OPTION IS ON THE TABLE

Google Meridian and Meta Robyn are free. Your team is capable. The CMO wants to know why you’re recommending a vendor instead.

Open-source MMM frameworks are technically capable, and your data science team is qualified to use them. The key consideration is whether the team that builds the system will still be available to maintain it in 18 months, given potential changes in media mix, staff turnover, and evolving business needs. The build-versus-buy decision is significant, and your recommendation should address both options transparently.

03 — THE ORGANIZATION HAS MOMENTUM

The CMO may already have a preference. Your independent judgment is exactly what’s needed — and exactly what’s inconvenient.

Your value in this buyer group lies in your willingness to challenge results that do not withstand scrutiny. Apply the same rigor to evaluation as you would to any measurement claim: request methodology documentation, seek validation evidence, and compare model outputs to known experimental results. Your recommendation should be defensible, including both your conclusion and the process that led to it.

04 — A DEMO IS NOT AN EVALUATION

A polished interface is not evidence of a sound model. Most vendor evaluations confuse the two.

Most MMM vendor evaluations consist of a 45-minute demo with illustrative outputs. The analytics team sees clean channel attribution charts and is expected to form a view. A rigorous evaluation looks different: methodology documentation reviewed before the demo, sample outputs from comparable brands compared against known holdout results, and a technical Q&A where your team can interrogate assumptions directly. That’s the evaluation your CMO and CIO expect you to have conducted before you say “yes.”

Six Requirements Every MMM Vendor Should Pass.LiftLab’s Response to Each.

These are the criteria a rigorous Head of Analytics should apply to every MMM vendor evaluation. They are also the criteria LiftLab was designed to meet — because they are the same criteria our marketing scientists apply to their own work.
  • Documented Methodology — Walkable Before the Demo

    Documented Methodology — Walkable Before the Demo

    LiftLab provides methodology documentation to analytics teams before onboarding begins. The approach to adstock decay, saturation modeling, uncertainty quantification, and calibration is documented and walkable with your data scientists. “Proprietary” is not a word in our vocabulary when an analytics team asks how the model works.
  • Uncertainty Quantified on Every Output

    Uncertainty Quantified on Every Output

    A measurement system that produces point estimates without quantifying uncertainty is communicating false precision. LiftLab quantifies the confidence range on every iROAS and mROAS estimate — so your analytics team can present “channel X’s estimated return falls within this range” rather than a single number that Finance will rightly question.
  • Reproducible, Version-Controlled Model Runs

    Reproducible, Version-Controlled Model Runs

    Every LiftLab model run is versioned. Input data, model configuration, and outputs are tracked so that when a weekly refresh produces different channel allocations from the previous week, the analytics team can trace exactly why — new spend data, updated calibration from an experiment, or changed market conditions. Reproducibility is a first-class engineering requirement.
  • Model Outputs Validated Against Experimental Results

    Model Outputs Validated Against Experimental Results

    The Trust Engine™ feeds geo holdout results and incrementality test outcomes back into the model as calibration inputs — closing the loop between what the model estimates and what experiments have demonstrated. This is the validation mechanism the Head of Analytics considers the gold standard: the model should be able to explain its results relative to what your own experiments found.
  • Per-Channel Adstock and Saturation Modeled Explicitly

    Per-Channel Adstock and Saturation Modeled Explicitly

    Without explicit per-channel modeling of diminishing returns and lagged effects, the model will systematically over-credit performance channels and under-credit brand advertising — producing the biased recommendations the analytics team will refuse to sign off on. LiftLab models these effects explicitly for every channel, and documents the approach for analytics review.
  • API-Accessible Outputs — The Analytics Team Retains Control

    API-Accessible Outputs — The Analytics Team Retains Control

    Model outputs — channel contributions, saturation curves, budget recommendations — are accessible via REST API so they can flow into your internal BI, financial planning systems, and proprietary scenario frameworks. The analytics team doesn’t hand over control of their measurement infrastructure to a vendor dashboard. LiftLab feeds your workflow; it doesn’t replace it.

 Disqualifying Signals — Walk Away If You See These

→Our methodology is proprietary.

If a vendor does not document how their model works, your CMO and CIO should not trust its outputs, and neither should you.

→Point estimates with no uncertainty communication.

Any model that produces a single ROAS number without a confidence range is communicating more certainty than the data can support.

→No geo holdout calibration loop.

A model that cannot be validated against your own experiments cannot be fully trusted. You should not be responsible for defending it if the CMO questions discrepancies with holdout results.

The Architecture Behind the Platform: Designed to Be Challenged, Not Accepted on Faith

LiftLab’s measurement system consists of four connected layers. Each layer addresses a specific failure mode of standard MMM and is fully documented for review with your analytics team before any output is used for budget decisions.

LAYER 01Cost Separation

Isolate marketplace cost dynamics from true consumer response

The first layer separates daily CPM and auction fluctuations from actual consumer demand signals. This prevents the model from misinterpreting cost events, such as competitive CPM spikes or inventory constraints, as performance changes. Most single-layer MMMs conflate these factors, resulting in channel coefficient estimates influenced by auction dynamics rather than consumer behavior. LiftLab addresses this issue before estimating response curves.

Explore Agile MMM →
LAYER 02Response Curves

Estimate per-channel response with documented adstock and saturation

The second layer fits channel-level response curves with explicit, per-channel adstock decay and saturation parameterization. Uncertainty is quantified for every iROAS and mROAS estimate, providing confidence intervals rather than misleading point estimates. Each channel’s adstock and saturation methodology is documented and available for analytics review. These outputs are reliable for presentation to Finance.

See Model Architecture →
LAYER 03Experimental Calibration

Close the loop between what the model estimates and what experiments have proven

The Trust Engine™ incorporates geo holdout results and incrementality test outcomes directly into the model as calibration inputs, updating channel incrementality estimates based on experimental evidence. This mechanism enables the Head of Analytics to answer “how does the model compare to our holdout results?” with a specific, documented alignment report rather than a verbal reassurance. When the model and experiment disagree, both teams know where to focus their review.

See the Trust Engine™ →
LAYER 04Reproducible Outputs

Constraint-aware scenarios with versioned, API-accessible, auditable outputs

Budget scenarios are constructed with real-world constraints, such as channel caps, CAC ceilings, and committed contracts, as explicit model inputs. All outputs are version-controlled and accessible via API, allowing the analytics team to integrate them into internal BI, financial planning, and proprietary scenario frameworks. Out-of-sample backtesting is conducted before any output is used for budget decisions, providing a validation baseline for the analytics team.

See Scenario Planner →

Google Meridian. Meta Robyn. Your Own Team.Why LiftLab Is Still the Right Recommendation.

You have likely already considered the open-source path. Google Meridian is well-designed, Meta Robyn is widely used, and your team has the technical capability to deploy either. Below is an honest assessment of the true costs of each option and the reasons analytics teams at similarly sophisticated brands selected LiftLab after evaluating all three.

The Real Cost of Building In-House

  • →Building the system is fast, but maintenance is ongoing. — A production MMM requires continuous recalibration as media mixes evolve, new channels are added, and external conditions change. The data scientists who build it will be required to maintain it indefinitely, often at the expense of their strategic responsibilities.
  • →It typically takes 6 to 12 months to achieve the first insight from an internal model. — By the time reliable outputs are available, the business may have already made budget decisions. LiftLab usually delivers actionable outputs within the first planning cycle, while an internal model is still in the data engineering stage.
  • →Internal models lack cross-client calibration signals. — Meridian and Robyn start from scratch using only your data. LiftLab's calibration leverages patterns across brands, categories, and channel mixes, providing a stronger starting point than any single-brand internal build.
  • →Organizational continuity risk arises when the — data scientists who built the internal model leave, taking with them the institutional knowledge of the model's design decisions, variable choices, and calibration history.

How LiftLab Complements — Not Competes With — Your Capability

  • →LiftLab manages the infrastructure layer, including — data pipelines, model maintenance, and weekly recalibration. This allows your analytics team to focus on tasks that require business context, such as experiment design, insight interpretation, and strategic recommendations.
  • →Your team continues to design experiments, including geo holdouts, switchback tests, and holdout groups. — LiftLab provides the calibration infrastructure, enhancing statistical efficiency and ensuring experimental results are integrated into the model on an ongoing basis.
  • →Build proprietary models on top of LiftLab, rather than replacing it. — LiftLab's API-accessible outputs integrate with your internal BI, FP&A, and scenario frameworks, allowing the analytics team to retain intellectual ownership of how insights are presented within the business.
  • →LiftLab delivers first outputs in 2 to 3 weeks, compared to 6 to 12 months for internal models. — While an internal model remains in data engineering, LiftLab provides actionable reallocation signals for the marketing team.

The Recommendation Frame

The strongest recommendation to your CMO and CIO is not to choose LiftLab instead of building internally, but to use LiftLab as the production measurement infrastructure. This allows your analytics team to focus on experimental design, model interrogation, and the generation of strategic insights. Such a recommendation respects your team’s capabilities and accelerates business value.

Meridian. Robyn. LiftLab.A Comparison the Head of Analytics Can Take to the CMO.

These are the three measurement options most analytics teams are weighing. Here is an honest assessment of what each offers — and what each costs at production scale.

Google Meridian

Well-designed, Bayesian, actively maintained by Google.

Meridian is a robust framework with a Bayesian architecture, thorough documentation, and support from Google’s research team. It provides a solid technical foundation for an in-house MMM. The key evaluation question is not whether Meridian works, but who on your team will manage the data pipeline, calibration cycle, model configuration, and ongoing maintenance as the media mix evolves. Google built the framework; your team operates the system.

  • →Right choice if: your team has dedicated MMM engineering capacity and leadership appetite for a 6–12 month build timeline.
Meta Robyn (Robyn)

Widely adopted, frequentist-based, strong community but limited uncertainty quantification.

Robyn is the most widely deployed open-source MMM framework and benefits from a strong practitioner community. It is approachable and well-documented. However, its main limitation for the Head of Analytics is the lack of native uncertainty quantification; outputs are point estimates without credible intervals, making it difficult to communicate model confidence to Finance. Extensions are available but require additional engineering investment. Robyn is a solid starting point, but not a production measurement platform by default.

  • →Right choice if: your team wants a fast prototype to build internal MMM fluency before committing to a production architecture.
LiftLab

Purpose-built for production deployment. Weekly cadence. Experimentally calibrated. API-accessible.

LiftLab is a production measurement system or a framework that requires an assembled team. It offers weekly model refreshes, Trust Engine™ calibration against geo holdouts, uncertainty quantification on every output, and full API access for downstream integration. Your analytics team benefits from a robust MMM without having to manage the supporting infrastructure. While the model is a commercial system and not fully under your team's control, the advantage is that maintenance is handled externally, allowing your team to focus on generating insights.

  • →Right choice if: the organization needs production-grade measurement outcomes now, not a model your team is still calibrating six months from now.

The Recommendation Frame

The strongest recommendation to your CMO and CIO is not to choose LiftLab instead of building internally, but to use LiftLab as the production measurement infrastructure. This enables your analytics team to focus on experimental design, model interrogation, and the generation of strategic insights. Such a recommendation respects your team’s capabilities and accelerates business value.

What the Analytics Team Gets Access to and What Each Capability Is Designed to Do

These are the six platform capabilities the analytics team will use, interrogate, and integrate with existing systems. Each is designed to complement analytical rigor — not replace it.

Agile Marketing Mix Modeling

Two-stage MMM architecture with documented per-channel adstock and saturation curves, uncertainty quantified on all outputs, and weekly recalibration cadence. Methodology documentation is available for analytics review before onboarding. The model the analytics team can interrogate, not just consume.

Explore Agile MMM →

Trust Engine™ — Incrementality Calibration

Geo holdout results and incrementality tests are incorporated into the MMM as calibration inputs, closing the loop between model estimates and experimental evidence. The analytics team can clearly assess the alignment between model outputs and holdout results and use this alignment report to validate the model before approving any budget recommendation.

Explore Trust Engine →

PlatformSense — Daily Signal Layer

Daily ad-platform efficiency signals are applied to stable econometric response curves, rather than raw dashboard data. When CPMs spike, PlatformSense classifies this as a cost event, not a performance signal, and adjusts accordingly. Anomaly detection reduces the manual monitoring burden on the analytics team while maintaining oversight of model inputs.

Explore PlatformSense →

Scenario Planner

We develop constraint-aware budget scenarios based on documented response curves, providing outcome ranges that accurately reflect the model’s confidence. All scenario outputs are versioned and accessible via API, allowing seamless integration with internal FP&A and BI systems. This ensures the analytics team maintains control over how recommendations are presented within the business.

Explore Scenario Planner →

Long-Term Brand Value Measurement

Long-term advertising multipliers are calibrated to your brand lifecycle and product category, using a documented methodology available for analytics team review. This capability prevents brand investment from being under-credited in standard MMMs and provides the CMO with a P&L-defensible case to protect brand budgets during Finance reviews.

Explore Long-Term Brand Value Measurement →

Diminishing Returns & Marginal ROI

Channel saturation curves derived from documented response function estimates — not interpolated averages. Marginal return at the last dollar of spend, with confidence ranges that honestly communicate when evidence is thin versus when a channel’s ceiling is clear. The number the analytics team can present to Finance and actually defend when challenged.

Explore Diminishing Returns →

Frequently Asked Questions

The CMO and CIO evaluation tracks require distinct materials from the same vendor. For the CMO, focus areas include expected business impact, methodology credibility, and integration with marketing planning. For the CIO, priorities are security posture (SOC 2 and ISO 27001), data architecture, integration approach, and vendor stability. LiftLab can prepare tailored materials for both tracks and engage in separate discussions with each stakeholder. The analytics team bridges the process by validating the measurement methodology for the CMO and verifying the security architecture for the CIO.

Your Recommendation Should Be Defensible.Start With the Evidence.

Book a technical review session with a LiftLab marketing scientist. Bring your data scientists and your hardest questions. We’ll walk through the methodology, the calibration approach, the validation evidence, and the integration architecture — and give you everything you need to make a recommendation your CMO and CIO can both trust.

Request a Technical ReviewRead the Platform Overview
Methodology documentation before the demo
Holdout calibration comparison available
CIO-level security documentation ready
SOC 2 & ISO 27001 certified
Your Recommendation Should Be Defensible.Start With the Evidence.

TALK TO US

(+1) 888-302-3861

marketing@liftlab.com

1111 Broadway, 5th floor, Oakland, CA 94607

Connect on

©2026 LiftLab Analytics, Inc. All Rights Reserved.

LiftLab is SOC 2 Compliant and has achieved an ISO 27001:2013 Certificate. Our policies and procedures have been audited to ensure security, availability, processing integrity, confidentiality, and privacy. Learn more