The TV Measurement Problem
TV has always been measured through MMM because there is no user-level signal. The AI era adds two complications. First, streaming TV and CTV produce partial user-level signal that some advertisers try to use for attribution, with mixed results. Second, AI search halo effects make TV's contribution harder to isolate from other upper-funnel channels.
Step 1: Capture Linear and CTV Separately
Linear TV and CTV behave differently in MMM. Linear has broad reach and long carryover; CTV has more targeted delivery and somewhat shorter carryover. Model them as separate variables with separate adstock and saturation parameters. Aggregating produces less accurate identification of each.
Step 2: Use GRPs or Impressions, Not Spend
Spend is a noisy proxy for TV exposure because rate cards vary by daypart, network, and season. GRPs (gross rating points) for linear and verified impressions for CTV are the better exposure measures. Most MMM frameworks accept either spend or exposure as the channel input.
Step 3: Set Long Adstock Priors
Linear TV adstock half-life: three to six weeks. CTV adstock half-life: two to four weeks. Longer than digital channels. Borrowing digital priors underweights TV in MMMs and explains why some brands incorrectly conclude TV is "not working."
Step 4: Capture the AI Search Halo
TV campaigns drive AI visibility through several pathways: increased branded search produces more content discoverable to AI, PR amplification of TV moments feeds AI training data, and consumer mentions of seen ads on social media affect AI assistant context. Include AI visibility as a separate variable in the same MMM; the TV variable captures direct TV effects, the AI variable captures the AI-mediated downstream effect.
Step 5: Calibrate With Geographic Lift
Run a geographic lift test on TV every two to four quarters. Pause TV spend in matched regions for eight to twelve weeks. Outcomes are branded search lift, direct traffic, and AI visibility movement in test versus control regions. The lift estimate calibrates the MMM's TV coefficient against causal ground truth.
Step 6: Distinguish CTV-Attributed From CTV-Lift
CTV platforms offer user-level attribution products that link CTV impressions to website conversions. These products produce attribution-style estimates that often overstate CTV's incremental effect because they take credit for viewers who would have converted anyway. The MMM coefficient is the cross-channel calibrated estimate; the CTV platform attribution is the tactical optimization signal. They will disagree; reconcile with periodic lift tests.
How Presenc AI Helps
Presenc AI provides the AI visibility data that disentangles TV's direct contribution from its AI-mediated downstream contribution. Without the AI variable, TV often takes credit for AI search lift that was downstream of the TV campaign; with the AI variable, the two effects are separated and the TV coefficient reflects its direct contribution alone.