Unified Marketing Measurement Platform (MMM + Incrementality)

Q: What does the Trust Engine actually mean in practice?

The Trust Engine is LiftLab's calibration layer, the mechanism that connects incrementality test results back to your AMM as causal proof. Every experiment tightens response curves, narrows forecast ranges, and sharpens reallocation recommendations. The more you test, the more precise the model becomes. Budget decisions compound in accuracy rather than being reset with each planning cycle.

Q: What does the onboarding process look like, and how quickly do we see value?

Onboarding starts with data integration, spend history, outcome KPIs, and calendar context. The AMM builds your channel-level response curves first. Constraint-aware scenarios follow. PlatformSense connects for daily signal monitoring. Most customers run their first scenario within the first planning cycle and identify their first reallocation opportunity within the first quarter.

Q: What data do we need to get started with LiftLab?

Minimum: 12 to 24 months of weekly spend by channel, one primary outcome KPI — revenue, orders, or profit, and a promotional calendar. Additional context—pricing signals, offline outcomes, and inventory events—sharpen the model but aren't required to get started. If you're unsure whether your data qualifies, a 15-minute readiness call with LiftLab's Marketing Science team will confirm.

A Trust Engine for Full-Funnel Decisions

Ad Platform dashboards inflate results. ROAS hides diminishing returns. Weekly MMMs chase noise and mistake marketplace volatility for consumer response. LiftLab separates all three, giving your team a single model where every measurement is explainable, every recommendation is testable, and every budget decision is repeatable.

LiftLab is the capital allocation engine that restores trust, giving you the speed to react to daily volatility, and the transparent econometric rigor your CFO demands.

Why Trust Breaks in Modern Marketing

Fragmented Truths

Each Ad platform optimizes its own attribution window, Google claims the conversion, Meta claims the conversion, and your MMM is left reconciling numbers that were never designed to agree. The budget meeting becomes a negotiation between vendor reports, not a decision grounded in a single source of truth.

Short-term Bias

Performance channels capture the demand that brand spend created, but without full-funnel measurement, that transfer of value is invisible. Brand loses every budget review, not because it doesn't work, but because it can't be proven in the same reporting window as a conversion.

Volatility

A CPM spike, an algorithm update, or a competitor promotion changes your channel economics overnight, but your MMM won't reflect it for weeks. By the time the model catches up, the budget decision has already been made on a stale signal.

LiftLab restores trust by making decisions

Explainable

Testable

Repeatable

How LiftLab Works

A VIRTUOUS CYCLE

Not a one-time model. A continuously compounding system.

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Integrate

Unify spend, outcomes, and business-context signals, pricing, promotions, seasonality, and offline data; into a single econometric foundation. Every model input is traceable, every data decision is auditable.

Model (Two-Stage AMM)

Separate ad auction dynamics from consumer response to build highly accurate diminishing returns curves.

Plan/Act (Constraint-Aware)

Run budget scenarios that optimize your full-funnel mix while respecting pre-committed spend, channel caps, CAC ceilings, and locked media contracts, producing a plan Finance can validate before a dollar moves.

Experiment (The Trust Engine)

Use transparent pacing experiments to calibrate your model with real-world causal proof, feeding results back into the AMM through the Trust Engine so every experiment tightens response curves and narrows forecast ranges.

Trust Engine Capabilities That Power Decisions

Agile MMM

LiftLab's Two-Stage AMM separates ad-auction cost dynamics from true consumer response, producing response curves that reflect real buyer behavior rather than marketplace noise. Know exactly where each channel's returns flatten before you overspend.

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Incrementality Testing Suite

Prove causal lift with different types of experiments, and with each completed test feeds back into your AMM through the Trust Engine, permanently improving the model rather than producing a one-time lift report.

Explore Incrementality Testing →

PlatformSense

Detect daily platform and performance shifts, simulate response options, and reallocate budgets fast with guardrails.

Explore PlatformSense →

Scenario Planner

Build Conserve, Maintain, and Accelerate scenarios against your live response curves, each returning a forecast range with mROAS trade-offs quantified at every budget boundary. The shared output Marketing and Finance both defend.

Explore Scenario Planner →

What It Looks Like in Practice

Before LiftLab

Average ROAS reporting across all channels. Paid Search holds the top spot every quarter, regardless of actual incremental contribution. The brand budget was cut in the last planning cycle because no revenue figure could be associated with it. MMM was last updated six months ago. CFO asking for a number, not a range.

After LiftLab

AMM identifies Paid Search at saturation, marginal ROI at current spend below 1x. 8% reallocated to Retail Media, where response curves show room to grow. Brand equity contribution quantified over a 26-week horizon. Scenario Planner produces Conserve, Maintain, Accelerate ranges for the next budget meeting. Finance approves the plan because they helped set the constraints.

Solutions Built For Your Priorities

One model. One forecast range. Constraints are set by both Marketing and Finance before the optimizer runs, so the budget meeting becomes a shared decision rather than a negotiation.

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Stop funding saturated channels because ROAS still looks healthy. LiftLab maps exactly where each channel's returns flatten, so the next dollar goes where it actually compounds.

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Most incrementality tests end at a lift number. LiftLab's closed-loop feeds every causal result back into your AMM, so tests don't just answer questions; they improve the model permanently.

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Your January plan was right. Markets moved. LiftLab's Scenario Planner runs Conserve, Maintain, and Accelerate simulations against live response curves, so you know what to do before Monday's budget call.

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Markets shift mid-campaign. LiftLab's PlatformSense detects efficiency changes daily and tells you exactly where to move spend before the window closes.

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Your MMM shows 0.9x ROAS on brand. The real number is 2.1x. LiftLab quantifies halo lift, ad-stock carryover, and long-term equity, so every brand dollar is visible on the P&L.

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Featured Stories

Unlocking Global Jewelry Growth with mROAS

Pandora paired agile MMM with geo tests to shift just 2% of the budget, driving +9.5% revenue and +12.4% profit across key channels.

"LiftLab's platform has been instrumental in helping us bridge the gap between long-term strategic goals and the need for frequent and short-term optimization."

Kasper Madsen,

Global Paid Media Analytics Manager, Pandora

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Scaling Channel Mix While Protecting Profit

Cinemark used LiftLab mROAS curves to see last-dollar impact, expand from 7 to 13 channels, and reallocate weekly to maximize ROI.

"LiftLab was the only vendor that offered a single comprehensive view of performance alongside a means to estimate the marginal profitability of our last dollar spent."

Jeff Rosenfeld,

SVP of Digital and Customer Experience, Cinemark

See the Results for Yourself

Frequently Asked Questions

What is LiftLab, and how is it different from a standard marketing mix model?

LiftLab is a unified marketing measurement platform built around a Two-Stage AMM that separates ad-auction cost dynamics from true consumer response, producing response curves most models can't build. Every component connects: the AMM guides incrementality tests, calibrates the model, and turns the model into executable budget decisions. It's a compounding system, not a standalone tool.

What does the Trust Engine actually mean in practice?

How does LiftLab help Marketing and Finance plan from the same numbers?

LiftLab produces one model, one forecast range, and three constraint-aware scenarios, Conserve, Maintain, and Accelerate, built with guardrails that both teams set before the optimizer runs. Finance can audit every input. Marketing can execute every output. The plan that emerges isn't Marketing's recommendation, approved by Finance. It's a shared operating contract that both teams built together.

How is LiftLab different from attribution tools and standalone MMM platforms?

Attribution assigns credit. Standalone MMMs model historical spend. Neither tells you where the next dollar should go today. LiftLab's Two-Stage AMM separates marketplace cost dynamics from consumer response, calibrates to real incrementality experiments through the Trust Engine, and runs constraint-aware scenarios against live response curves, producing a budget decision grounded in causal evidence rather than platform-reported averages.

What does the onboarding process look like, and how quickly do we see value?

What data do we need to get started with LiftLab?

Turn Every Marketing Dollar Into a Compounding Decision

UNIFIED MARKETING MEASUREMENT & OPTIMIZATION PLATFORM

The Trust Engine
for Capital Allocation.

A Trust Engine for Full-Funnel Decisions