What is mmm vs. last-click attribution?
Last-click attribution credits a conversion entirely to the final ad interaction before it happened; MMM (Marketing Mix Modeling) instead estimates each channel's incremental contribution using aggregate spend and outcome data over time, without relying on individual user-level tracking. Last-click is simple and real-time but systematically over-credits bottom-funnel and retargeting; MMM is privacy-resilient and captures upper-funnel effects, but runs on a lag and needs real statistical rigor to trust.
Last-click attribution is the default most platforms report by: whichever ad the user interacted with right before converting gets full credit. It's easy to compute and updates in real time, but it has a structural bias, a channel positioned at the bottom of the funnel (branded search, retargeting) tends to intercept credit for demand another channel actually created.
Marketing Mix Modeling takes a completely different approach: instead of tracking individual users, it looks at aggregate spend by channel over time against aggregate outcomes, and statistically estimates how much each channel actually moved the outcome, holding other factors (seasonality, pricing, promotions) constant. This makes it immune to tracking-permission changes and cross-device gaps, since it never needed user-level tracking in the first place, but it's slower to update, needs enough historical data and spend variation to identify effects reliably, and is only as good as the model specification behind it.
Neither one is simply 'more accurate' in all cases. The practical pattern that works: use last-click (or platform-reported) data for real-time optimization decisions inside a channel, and use MMM (or at minimum, incrementality tests: geo holdouts, budget experiments) periodically to sanity-check whether the channel mix implied by last-click actually reflects real incremental contribution. Treat a persistent, large gap between the two as a signal to run a real experiment before trusting either number blindly.