# MMM vs MTA > MMM reads spend and sales totals to judge each channel. MTA follows each buyer's touchpoints. Data needs, limits and when to use each, with a worked example. - **Author**: Marxx - **Published**: 2026-11-11 - **Category**: Tracking - **URL**: https://marxx.ai/library-compare/mmm-vs-mta --- MMM (marketing mix modeling) uses totals, such as weekly spend and weekly sales, to estimate what each channel adds. MTA (multi-touch attribution) follows individual buyers and splits the credit for each order across the ads they touched. Use MTA to steer day to day. Use MMM for big budget calls across many channels. ## Side by side | | MMM | MTA | |---|---|---| | Data it uses | Totals of spend and sales, by week or day, often by region | Each order's journey, from your tracking | | Question it answers | How much did each channel add to total sales? | Which ads and clicks led to this order? | | Speed | Slower. Needs weeks or months of totals before it says much | Faster. Reads each order as it comes in | | Strength | Covers offline, creators and channels with few clicks. Needs no user-level tracking | Shows campaigns, ad sets and single creatives | | Weak spot | Needs a long run of data and statistical skill. Says little about one ad | Misses buyers it cannot track. The credit rule is a choice, not a fact | | Examples | Meta's Robyn and Google's Meridian, both open source | Northbeam and Triple Whale, which both list multi-touch attribution | ## What the tools say about themselves Meta describes Robyn as an experimental, semi-automated, open-source MMM package, under the MIT licence ([Robyn on GitHub](https://github.com/facebookexperimental/Robyn), checked 2026-10-20). Google describes Meridian as an MMM framework for in-house models, based on Bayesian causal inference and released under Apache-2.0. It says MMM uses aggregated data ([Meridian on GitHub](https://github.com/google/meridian), checked 2026-10-20). Both are free to use. The data work and the skill to read the output are not free. Northbeam's pricing page lists media mix modeling (MMM+) and incrementality as optional extras on its higher plans. Triple Whale's lists Compass, an add-on that unifies MTA, MMM and incrementality and comes with Enterprise (both checked 2026-10-20). See [Northbeam vs Hyros](/compare/northbeam-vs-hyros). ## Why MTA gets harder every year MTA depends on seeing the buyer's path. Safari has blocked third-party cookies by default since March 2020 ([WebKit](https://webkit.org/blog/10218/full-third-party-cookie-blocking-and-more/)). Each blocked path leaves a hole that the tool has to guess. MMM avoids this because it never looks at one person. ## A worked example Say you spent INR 10,00,000 last month: Meta INR 5,00,000, Google INR 3,00,000, YouTube INR 1,00,000 and creators INR 1,00,000. - **MTA** credits YouTube with INR 40,000 of sales, because few buyers clicked a YouTube ad before ordering. That is a ROAS of 0.4. - **MMM** finds that in weeks when YouTube spend rose, total sales rose too. It estimates INR 1,60,000 from YouTube. That is a ROAS of 1.6. Neither number is the truth. Check them with a test: switch YouTube off in some regions for three weeks and compare sales. That is a [geo lift test](/glossary/geo-lift-test). See [incrementality](/glossary/incrementality). Now the decision. Say your [break-even ROAS](/glossary/break-even-roas) is 1.2. MTA says cut YouTube (0.4 is below 1.2). MMM says keep it (1.6 is above 1.2). If your break-even is 2.0, both say YouTube loses money. ## How to choose - **One or two channels:** use platform numbers and [MER](/compare/roas-vs-mer), plus simple tests. - **A growing store with several paid channels:** add an MTA tool. See [the best attribution tools for D2C brands](/best/best-attribution-tools-for-d2c-brands). - **Many channels and large budgets:** add MMM, and calibrate it with holdout tests. ## FAQ ### Can I use MMM and MTA together? Yes. MTA gives quick reads on single ads, and MMM checks the big picture. Test results from a holdout test keep both honest. ### Does MMM need cookies? No. It works on aggregated data, so it needs no user-level tracking. Google says so on the Meridian page. ### Which is more accurate? Neither, on its own. They answer different questions and have different blind spots. A controlled test is the only way to see what ads truly caused. --- - [More Tracking articles](https://marxx.ai/library-compare/category/tracking) - [All articles](https://marxx.ai/library-compare)