Marketing Mix Modelling and multi-touch attribution both claim to answer "what is actually working," and they answer genuinely different versions of that question. Using the wrong one, or investing in a sophisticated version of one when you needed the other, is a common and expensive mistake.
MTA tracks individual user journeys across touchpoints, click, view, conversion, and assigns credit across the channels involved in that specific journey. It is granular, near real-time, and works at the level of individual campaigns and even individual ads. Its core weakness is that it depends on being able to observe and stitch together individual user behaviour, which is precisely what browser privacy changes, ad blockers, and cross-device usage make increasingly difficult. MTA is also inherently biased towards channels that are easy to track, digital, click-based, and biased against channels that are not, offline, TV, out-of-home, word of mouth.
MMM works at an aggregate level. It uses statistical modelling on historical spend and outcome data, typically weekly or monthly, across all your marketing activity including channels MTA cannot see, to estimate each channel's contribution to overall results. It does not rely on tracking individual users at all, which makes it immune to the privacy and signal-loss problems degrading MTA. Its weakness is the inverse: it is slower to produce results, needs a meaningful history of varied spend data to model accurately, and gives you channel-level or campaign-group-level insight rather than the ad-level granularity MTA can theoretically provide.
MMM needs data. Specifically, it needs enough historical variation in spend across channels to statistically separate their effects, which is hard to do with a young company that has only run stable, unchanging budgets for a few months. Early-stage companies that invest in MMM before they have this history often end up with a model that looks sophisticated but is not meaningfully more accurate than a simpler approach, because there is not enough signal in the underlying data for the statistics to work with.
Get MTA hygiene right first: clean event tracking, deduplication, sensible attribution windows, server-side tracking where it helps. Add incrementality testing, geo holdouts or conversion lift studies, to validate what MTA is telling you, particularly for high-spend, easy-to-overstate channels like branded search and retargeting. Only once you have a real budget, a real history of varied spend, and channels that MTA genuinely cannot see well, offline, TV, sponsorships, does investing in MMM start to pay for itself. Skipping straight to MMM without getting the fundamentals right underneath it usually produces an expensive model built on a shaky foundation.
Mature marketing organisations increasingly run both: MMM for high-level budget allocation across channels, informed and validated with incrementality tests, and MTA plus conversion-level data for the tactical, campaign-level optimisation decisions made every day. Neither replaces the other. The mistake is picking one and assuming it answers every measurement question your business has.
If you are trying to work out what stage of measurement maturity actually makes sense for your business right now, get in touch.