LiftLab quantifies brand and performance impact in the same model, giving brand investment the same financial defensibility as performance spend over a 6 to 52-week horizon, so you walk into every budget meeting with a number Finance can audit, not a case you have to argue.
PlatformSense detects CPM shifts, auction volatility, and creative fatigue daily, applying live signals to stable response curves so you react to what's actually changing in the market, not what the Ad Platform wants you to believe is changing.
LiftLab gives Finance a methodology they can interrogate, not just trust, built on transparent, auditable incrementality testing rather than Ad Platform-reported attribution, so marketing spend holds up under financial scrutiny.
One model. One forecast range. Constraints both Marketing and Finance set before the optimizer runs, so the budget meeting becomes a decision, not a negotiation.
See Use Case
Stop funding saturated channels because average ROAS still looks healthy. LiftLab maps exactly where each channel's returns flatten, so the next dollar goes where it actually compounds.
See Use CaseMost incrementality testing ends 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.
See Use CaseYour January plan was right. Markets moved. Run Conserve, Maintain, and Accelerate simulations against live response curves, so you know what to do before Monday's budget call.
See Use CaseA VIRTUOUS CYCLE
Not a one-time model. A continuously compounding system.
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.
Separate ad auction dynamics from consumer response to build highly accurate diminishing returns curves. LiftLab's Two-Stage AMM goes beyond traditional media mix modeling by isolating marketplace cost noise before measuring true consumer demand.
Run budget scenarios that optimize your mix while respecting pre-committed spend, channel caps, CAC ceilings, and locked media contracts, producing a plan Finance can validate before a dollar moves.
Feed causal proof from transparent incrementality testing back into your AMM through the Trust Engine, tightening response curves and narrowing forecast ranges with every test cycle.
Separates real consumer demand from noise, so every budget decision compounds.
Quantify what's driving performance, where the returns flatten, and how the next dollar should move.
Explore Agile MMM →
Prove causal lift with auditable geo experiments, then feed results back into your AMM to permanently sharpen every budget decision.
Explore Incrementality Testing →
PlatformSense applies live auction signals to stable response curves daily, so your team reacts to what's actually changing in the market without rebuilding the model each time.
Build full-funnel, constraint-aware budget scenarios. Compare ranges and trade-offs to align Marketing and Finance before spending is ever committed.
Explore Scenario Planner →
"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."
"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."
"Liftlab’s solution gave us the confidence to make significant changes to our media mix. Our business results proved we made the right choice."
Built for enterprise teams that can't afford to get marketing effectiveness measurement wrong.
SOC 2 & ISO 27001 certified. Enterprise-grade protection and audit-ready data integrity for total financial peace of mind.
Unified data ingestion across your entire media stack, eliminating platform bias and resolving conflicting signals before the model ever runs.
Go from raw data to boardroom-ready scenario ranges in weeks, with weekly model reviews that keep your team acting on current signal, not last quarter's assumptions.
