Last-Click vs MMM: Overview
Last-click attribution gives 100 percent of conversion credit to the final touchpoint before conversion. Marketing mix modeling (MMM) decomposes outcomes across channels based on aggregate time-series patterns. For traffic influenced by ChatGPT and other AI assistants, the difference between the two is the difference between attributing zero and attributing the full causal share.
How Last-Click Sees ChatGPT
It does not. ChatGPT interactions produce no tracked referrer for most users. A buyer who asks ChatGPT for a vendor recommendation, mentally notes the brand, and types the brand name into a browser the next day arrives as a direct visit. Last-click credits the conversion to "direct" or to whatever paid channel happened to be active at conversion time. ChatGPT contributed everything except the last click, and gets credited zero.
This is not a configuration error. It is the architectural consequence of using a model that requires user-level signal for a channel that does not produce one. No amount of attribution tuning can fix it.
How MMM Sees ChatGPT
MMM operates on weekly aggregate data. When weekly ChatGPT visibility (measured as LLM share of voice on a stable prompt set) is added as a media-equivalent variable, the model regresses revenue on the visibility series alongside spend on every other channel. The coefficient on the AI variable is the channel's aggregate contribution.
The contribution is correlational on its own and causal when calibrated against periodic geographic lift tests on AI visibility inputs. Either way, ChatGPT gets credited the share of revenue the model attributes to it, instead of getting credited zero.
What the Numbers Typically Look Like
For a B2B SaaS brand with active AI visibility work, last-click typically attributes 0 to 3 percent of revenue to "AI" (mostly via the small subset of ChatGPT interactions that pass user-agent or referrer data). MMM typically attributes 8 to 18 percent of revenue to the same channel. Survey self-attribution typically lands in the same 8 to 18 percent range. Geographic lift tests typically confirm a causal effect in the same range, within wider confidence intervals.
The gap between last-click (zero) and MMM (mid-teens percent) is the under-investment that last-click drives. Brands using last-click as their primary measurement framework are systematically underspending on AI visibility because the channel looks free or invisible.
Feature Comparison
| Dimension | Last-Click | MMM |
|---|---|---|
| ChatGPT attribution | ~0% (structurally blind) | 5-20% typical (model-derived) |
| Data requirement | Tracked touchpoints | Weekly aggregate series |
| Setup cost | Free (built into analytics) | $100K-$300K initial |
| Privacy resilience | Degrades with cookie loss | Unaffected |
| Causality | Correlational, biased | Causal when lift-test calibrated |
| Budget allocation implications | Overspend on closing channels | Balanced cross-channel |
| AI search era fitness | Poor | Strong |
The Strategic Implication
Brands measuring with last-click as primary will underinvest in AI visibility, PR, content, and every other channel whose value is captured by AI search before any tracked touchpoint. The result is compounding disadvantage relative to brands that have moved their primary measurement to MMM with an AI variable. The window for this shift is now; the gap will widen as AI search captures more research volume.
How Presenc AI Helps
Presenc AI provides the weekly ChatGPT visibility series that MMM needs to value the channel. Without that series, the MMM cannot see ChatGPT either. With it, ChatGPT becomes a discrete channel in the model with its own contribution, response curve, and budget allocation recommendation. The combination of MMM and Presenc AI is the operational fix for the last-click blind spot.