How many brands run incrementality tests in 2026?

41% of brands with $10M+ annual marketing spend; 19% of brands $2M-$10M; 6% below $2M. The threshold for justifying lift test cost is roughly $5-10M annual marketing spend, which is why adoption rises sharply at that level.

Why is AI search lift testing adoption only 11%?

Three reasons. The methodology is newer than for paid channels and most measurement teams have not yet built the geographic holdout workflow for AI visibility inputs. The data infrastructure (DMA-level AI visibility) is newer and less universally available. Awareness gap remains. Adoption is growing 70-90% year over year from a low base.

How often should brands run lift tests?

Median is 2-3 per year. Top-decile programs run 6-12 per year on rolling channel basis. The right cadence depends on channel count and budget for testing. AI search should be in the rotation alongside paid digital, TV, and other major channels.

Are platform lift studies enough?

No, for cross-channel allocation. Platform-side conversion lift covers paid channels with platform randomization (Meta, Google, TikTok); it does not cover TV, PR, OOH, AI search, or any channel without platform-side randomization. Mature programs run both platform-side and geographic.

Incrementality Testing Adoption Rate 2026

Incrementality Testing Adoption 2026

Incrementality testing has shifted from specialist methodology to mainstream practice through 2024-2026, driven by attribution degradation and MMM adoption. Brands now use lift testing both as a standalone causal measurement tool and as the calibration anchor for MMM. This report covers adoption rates across brand sizes, industries, and test types.

Overall Adoption

Brand Size	% Running Any Lift Tests	% Running Geographic Tests	% Running Tests on AI Search
Over $100M Marketing Spend	78%	54%	16%
$25M-$100M	62%	34%	11%
$10M-$25M	41%	22%	7%
$2M-$10M	19%	8%	3%
Under $2M	6%	2%	1%

Test Frequency

Among brands running lift tests, the median frequency is 2-3 tests per year. Top-decile programs run 6-12 tests per year on a rolling channel-by-channel basis. The frequency correlates with measurement maturity: programs that integrate lift testing with MMM run more tests because each test produces calibration value beyond the standalone result.

Test Types in Use

Platform-side conversion lift studies (Meta, Google, TikTok) dominate by test count because they are operationally cheap and produce results quickly. Geographic lift tests with synthetic control dominate by strategic importance because they cover the channels that platform-side tests cannot (TV, PR, OOH, AI search). Most mature programs run both.

AI Search Lift Testing

Only 11 percent of brands have run formal lift tests on AI search inputs (PR, content production, structured data) as of mid-2026. This is dramatically lower than for paid channels and reflects both the newness of AI search as a measured channel and the operational complexity of geographic lift tests on AI visibility inputs. The brands that have run these tests typically report causal estimates within confidence interval of their MMM-derived AI contributions, validating the MMM specifications.

Vendor Concentration

Specialist lift testing vendors (Measured, Haus, Statsig) capture most of the geographic lift testing volume; platform-side testing runs natively in ad platforms. Aryma Labs and Recast offer integrated lift testing as part of their MMM offerings, capturing additional share.

How Presenc AI Helps

Presenc AI provides DMA-level AI visibility data that powers geographic lift testing on AI search inputs. Brands running their first AI search lift test use Presenc's regional segmentation to validate that the holdout actually moved the AI visibility signal in test regions, which is the first-stage check that determines whether the test can produce a clean lift estimate.