tl;dr
MMM is Klar's most advanced attribution model — built on top of Data-Driven Attribution
It goes beyond click data by ingesting spend, impressions, engagement signals, and post-purchase survey answers to redistribute direct and branded traffic back to the channels that originally created the demand
When PPS is connected and mapped, it also surfaces Word of Mouth as its own channel — visible across all attribution models once set up
Use MMM for cross-channel budget decisions, ideally with a 28-day window
You'll find it in the Attribution Deep Dive under Tracking → Attribution → Deep Dive → model selector
Why click-based attribution isn't enough
Every user journey is unique. Static models like First-Touch or U-Shape apply one fixed rule to all journeys — they miss the nuance.
Think of it like scoring a goal in football. Not everyone who touches the ball creates equal value. Our Data-Driven model already handles this — it weights each touchpoint based on its realistic impact on a conversion.
But here's the limitation of all click-based models: you don't need a touchpoint to have had an impact on a conversion.
What can't click-based attribution see?
There are marketing activities that create real awareness and demand — but leave no trackable click:
Word of Mouth — customers who heard about you from a friend and went directly to your store
Upper-funnel brand awareness — a channel like TikTok creating demand that converts through other channels in the end
Offline channels — out-of-home, podcast sponsorships, PR coverage (primarily made visible via PPS; MMM helps quantify their revenue impact)
These still drive conversions. They just show up as Direct or Branded Search in click-based models like Multi-Touch Attribution or First-/Last Click — because the customer knew your brand before they searched.
How MMM solves this
MMM is built on top of Klar's Data-Driven Attribution. It ingests millions of signals across all your channels and runs them through machine learning models to answer the question: which channels actually created this demand?
The signals it uses:
Spend impact — did a spike in Meta spend correlate with a rise in branded search the next week?
Impressions impact — did higher TikTok impressions precede a lift in direct new-customer conversions?
Engagement impact — click-through rates, video views, and other engagement signals
Post-purchase survey (PPS) answers — zero-party data confirming which channel the customer recalls seeing first
Using these correlations, MMM redistributes direct and branded conversions back to the channels that originally created that demand. When PPS is connected, Word of Mouth also appears as its own standalone channel — this is powered by PPS, and available across all attribution models.
Let me explain you the whole thing a bit more vividly and how it relates to the football goal from above in this video: 👇
What changes when you switch to MMM?
MMM typically produces these directional shifts compared to last-click or Data-Driven:
Meta moves significantly up — upper-funnel touchpoints are highly under-credited by click data
TikTok moves up substantially — as a top-funnel channel, it's almost invisible to last-click
Branded Search and Direct drop — they were capturing demand, not creating it
Word of Mouth appears as a new channel, typically representing 5–50%+ of new-customer conversions (varies by brand)
⚠️ Important: These are directional patterns, not benchmarks. The actual redistribution depends on your specific channel mix and spend levels.
MMM gives you a more holistic and honest picture of which channels are actually driving revenue — especially for budget decisions where you need to know where to invest more and where to cut.
Example — how MMM redistributes a conversion
Take a customer journey that, based on clicks alone, looks like this:
A single branded search click before purchase. But that can't be the whole story — the customer knew your brand from somewhere before they searched for it.
To understand where that demand actually came from, MMM looks at the correlation between other channels and the branded/direct conversions:
Spend impact — did a spike in Meta spend precede a rise in branded search?
Impressions impact — did higher TikTok impressions correlate with more direct new-customer visits?
Engagement signals — video views, click-through rates across all channels
Post-purchase survey answers — zero-party data confirming what the customer remembers
Running these signals through machine learning lets Klar derive which channels most likely influenced the conversion and redistribute branded traffic back to its original sources. It also adds Word of Mouth — a channel that is always happening for every brand at some percentage, but is otherwise impossible to quantify.
When should I use MMM?
Use case | Recommended model |
Cross-channel budget allocation | MMM — 2–3 conversion cycles |
Day-to-day channel comparison | Data-Driven |
Creative-level ad optimization | Any Click |
MMM is valuable for any business running multi-channel marketing. If a large share of your new-customer conversions sit in Direct or Branded Search, that can indicate demand is being created by channels that don't get the last click. But before drawing strategic conclusions, rule out tracking issues first — a high Direct share can also mean missing UTMs or a broken pixel. Make sure your tracking setup is solid; MMM works best on top of clean data.
Where do I find MMM in Klar?
Go to Tracking → Attribution → Attribution Deep Dive. Use the model selector at the top of the report and choose Marketing Mix.
Attribution Models in Comparison
Comparing static last-click attribution with Klar's dynamic MMM attribution, MMM gives you a holistic and more realistic picture of which channel is actually driving revenue — going far beyond click-based data.
If you want to know more about the different static and dynamic attribution models in Klar, how they differ, and when to use which one, check out this video: