Mature marketing teams use all three, an approach often called the measurement triangle.
Method 1: Attribution
The question it answers: Which touchpoints did customers interact with before they converted?
Attribution follows individual users through their journey and assigns credit for each conversion. It ranges from simple rules, like last-click, to algorithmic approaches such as the data-driven attribution in Google Analytics 4. Ad platforms also report their own attributed conversions.
Where it shines: Attribution is fast and granular. You can see results daily, down to the campaign, ad group or keyword. That makes it the right tool for in-flight optimization: pausing a weak ad, shifting bids, or spotting a broken landing page.
Where it falls short: Attribution measures association, not causation. A customer who was already planning to buy might search your brand name and click an ad on the way, and attribution gives that ad credit for a sale it didn't cause. It also only sees what it can track, so privacy restrictions, ad blockers and offline channels create large blind spots.
Method 2: Incrementality Testing
The question it answers: Did this specific marketing activity cause additional sales, and how many?
Incrementality tests are controlled experiments. You show ads to one group and withhold them from a comparable group, then compare outcomes. The difference is the true incremental effect. Common formats include:
- Conversion lift studies run inside ad platforms, which randomly hold out a share of the target audience.
- Geo experiments, which turn advertising on or off in selected regions and compare them with similar regions.
- Holdout tests, such as pausing a channel entirely for a period and measuring what happens.
Where it shines: A well-designed experiment is the closest thing marketing has to ground truth. It cuts through correlation and tells you what actually happened because of your ads.
Where it falls short: Experiments are slow, can be expensive (holding out an audience means giving up some sales), and test one thing at a time. You can't run a perfect experiment on every channel every quarter. Results also reflect the conditions at the time of the test and may not hold at different spend levels.
Method 3: Marketing Mix Modeling
The question it answers: How much did each channel contribute to total sales, and how should we allocate next period's budget?
MMM uses aggregated historical data, typically weekly spend and sales over two to three years, to estimate each channel's incremental contribution while controlling for seasonality, pricing, promotions and other factors.
Where it shines: MMM gives the full picture. It covers every channel at once, including offline media, needs no user-level data, and produces response curves that show where each channel hits diminishing returns. That makes it the best tool for strategic budget allocation and scenario planning.
Where it falls short: MMM is slow to build and refresh, works at a coarse level, and depends on modeling assumptions. When two channels always rise and fall together, the model can struggle to tell them apart. Different reasonable model choices can yield different answers.
Side by Side
| Attribution | Incrementality testing | Marketing mix modeling | |
|---|---|---|---|
| Core question | Who touched the conversion? | Did this activity cause lift? | What did each channel contribute? |
| Data used | User-level journeys | Test vs. control groups | Aggregated weekly totals |
| Speed | Daily | Weeks per test | Monthly or quarterly refresh |
| Granularity | Ad, keyword, campaign | One test at a time | Channel or major campaign |
| Offline channels | No | Some (via geo tests) | Yes |
| Measures causation | No | Yes | Estimates it |
| Best used for | Day-to-day optimization | Validating key assumptions | Budget allocation and planning |
How the Three Work Together
The real value comes from connecting the methods so each one improves the others.
1. Experiments calibrate the MMM. MMM's biggest weakness is uncertainty about which of several plausible answers is right. Experiment results fix that. If a geo test shows that paid social drives a return of $2.10 per dollar, you can feed that result into the model as prior knowledge. Modern Bayesian tools such as Google's Meridian and PyMC-Marketing are designed to accept this kind of input.
2. MMM points to where experiments are needed. When the model is uncertain about a channel, or when its results disagree sharply with attribution, that channel becomes the next candidate for a test. MMM turns experimentation from guesswork into a prioritized roadmap.
3. MMM sets the budget; attribution spends it well. MMM decides how much goes to each channel for the quarter. Within each channel, attribution and platform data guide which campaigns, audiences and creatives get the money day to day.
4. Disagreements are signals, not problems. A classic example is branded search. Attribution often shows it as a top performer, because people who already intend to buy click branded ads. MMM and holdout tests frequently show its incremental value is much lower. When methods disagree, dig in. That's usually where the biggest budget savings hide.
The Bottom Line
No single method tells the whole truth. Attribution is fast but biased toward what it can track. Experiments are accurate but narrow. MMM is comprehensive but relies on assumptions. Used together, each one checks the others, and you get measurement you can actually defend in a budget meeting.
All three methods ultimately rest on the same foundation: clean conversion data and consistent channel definitions in GA4. If your UTMs are inconsistent or your channel groupings are broken, attribution, incrementality tests and MMM will all disagree for the wrong reasons. UTM governance is the unglamorous work that makes all three methods trustworthy.
Next up: Google is bringing its Meridian MMM into Google Analytics 360. We'll look at what that means for analytics teams.
Building and connecting all three methods is exactly the kind of work we take on. See our Attribution & Measurement Strategy service if you want help setting up the triangle for your business.
