Three capabilities that make every marketing incrementality test auditable, precise, and directly actionable inside your model.
Not every channel calls for the same test design, and geo selection should follow the same logic. LiftLab supports both Stratified Random Sampling and Synthetic Controls, deploying each where it performs best. The methodology is fully auditable, so Finance can interrogate the geo-selection process rather than accept the output on faith.
LiftLab supports Switchback tests for high-volatility environments, Strategy experiments for campaign-level shifts, and Go Dark with Pacing to map saturation curves with precision. Each design is matched to your measurement objective, not applied as a default template because it was easiest to build.
Ad platform spillover corrupts incrementality measurement. LiftLab proactively detects and corrects for effects like Performance Max automatically reallocating to Shopping in suppressed Geos’, so your causal measurement stays clean, and your budget decisions stay grounded in reality rather than contaminated platform data.
The Agile MMM identifies channels with the widest confidence intervals and the least experimental validation, so every test targets the decision with the highest planning risk, not the channel that's easiest to measure.
Choose an auditable, empirical test design tailored to your objective: Switchback, Strategy, or Pacing. No guesswork. No one-size-fits-all templates.
Execute pacing experiments that deliberately vary spend to build robust response curves. Know exactly where returns flatten before you overspend, and feed those saturation points directly back into your AMM for the next planning cycle.
Causal results feed directly into the AMM through the Trust Engine, adjusting internal saturation parameters and tightening response curves. Each experiment permanently sharpens the model, so reallocation decisions compound in precision rather than reset with every planning cycle.
Experiment Dashboard
Find Your Saturation Point
Transparent Geo Selection
The Trust Engine Loop
