Who This Is For
Marketing mix modelers, marketing science analysts, and econometricians responsible for refitting MMMs and recommending channel-level budget allocation. If you have ever stared at a base demand intercept that is growing faster than any explanatory variable in the model and wondered what it is hiding, this page is for you. The answer is increasingly AI search, and the fix is mechanical once the right data is available.
Why AI Visibility Is Now an MMM-Critical Input
Standard MMMs built between 2020 and 2024 do not include any AI search variable. The result is that ChatGPT, Claude, Perplexity, and Gemini referrals are absorbed into the base demand intercept along with brand equity and word of mouth. The model says base demand is rising; in fact, AI search has been doing more of the work, and the brand is systematically underinvesting in it.
For MMM practitioners, this is a model spec problem with a clean solution: add a weekly AI visibility series as a media-equivalent variable, calibrate adstock and saturation priors appropriately, refit. The harder problem is sourcing a reliable weekly AI visibility series, which is the gap Presenc AI exists to close.
What the AI Visibility Variable Should Look Like
The right input is LLM share of voice computed against a locked prompt set covering category, use-case, comparison, and decision queries across the major AI platforms weighted by audience relevance. Weekly granularity. Stable methodology over the modeling window. Regional segmentation available for any geographic lift testing that calibrates the model.
The wrong inputs include: single-platform mention counts (too narrow, breaks platform-switching dynamics), unweighted cross-platform mentions (over-counts irrelevant platforms), and methodology that shifts mid-period (destroys the time series). Presenc AI exports the right input by default; the operational discipline is to lock the methodology before measurement starts.
Adstock and Saturation Priors
Geometric adstock with half-life of two to four weeks is the standard starting point. Long-cycle B2B may extend to eight weeks; short-cycle consumer may compress to one to two weeks. The single most common error is borrowing the paid search prior (one week or less), which produces an undervalued AI coefficient.
Hill or S-curve saturation with the half-saturation point prior in the middle of the observed AI visibility range. The intuition is that being consistently mentioned in the top three for a category produces diminishing returns past that threshold.
Validation Checks for MMM Practitioners
Three checks before trusting the new decomposition. First, the AI variable should have a non-trivial contribution (greater than 1 percent of decomposed revenue). Second, the base demand contribution should drop relative to the pre-AI-variable model. Third, the holdout MAPE should improve. If any check fails, the spec is wrong.
Common spec issues: AI series too short (use vendor backfill), multicollinearity with PR or branded search (orthogonalize), adstock too short (extend the half-life prior), saturation too weak (tighten the half-saturation prior).
Calibration Discipline
MMM coefficients are correlational. The calibration step is a geographic lift test on AI visibility inputs (PR, content syndication) every two to three quarters. The lift estimate is causal ground truth; the MMM coefficient on the AI variable should agree with it within confidence interval. Persistent disagreement is a signal to revisit the spec.
How Presenc AI Helps
Presenc AI exports weekly LLM share of voice in the exact CSV format that Robyn, LightweightMMM, PyMC-Marketing, and commercial MMM platforms expect. Historical backfill of at least 52 weeks is included by default. Prompt-set governance metadata travels with the export so the AI variable is documented to the same standard as the rest of the MMM. For MMM practitioners adding AI as a discrete channel for the first time, Presenc is the missing data layer.