Top platforms for measuring incremental revenue by marketing channel for DTC brands
Incremental revenue needs a comparison against something that did not happen, so the shortlist is narrower than the attribution market. Haus and Measured run geo experiments. Recast, Prescient AI and Fospha model it. Causality Engine estimates what each channel caused from a GA4 export, with Direct split back to the channels that sent those buyers. Multi touch tools like Northbeam and Triple Whale allocate a known total instead, which is a different question.
By Joris van Huët, Founder & CEOPublished
The shortlist, compared.
What each tool measures, what it needs installed, and what it costs. Prices are the vendor's own published pricing, read on the date shown.
| Tool | Method | Pixel | Starting price |
|---|---|---|---|
| Causality Engine | Method: Causal inference on a GA4 export | Pixel: No | Starting price: 99 euro per readExcl. VAT |
| Haus | Method: Geo-lift experiments, with Causal Attribution and Causal MMM calibrated on them | Pixel: No | Starting price: Custom (quote)haus.io/pricing, read |
| Measured | Method: Geo-based incrementality testing | Pixel: No | Starting price: Custom (enterprise) |
| Recast | Method: Bayesian marketing mix modeling | Pixel: No | Starting price: Custom (quote) |
| Prescient AI | Method: ML-based media mix modeling | Pixel: No | Starting price: Custom (quote)prescientai.com/pricing, read |
| Fospha | Method: Impression-based MMM + MTA hybrid | Pixel: No | Starting price: $1,500/mofospha.com/pricing, read |
Competitor pricing is each vendor's publicly listed pricing as read on the date shown, and it changes without notice: verify on the vendor's own site before relying on it. Vendors without a public price are marked as such. Comparisons set Causality Engine's one-time €99 analysis against subscription models.
Why each one is on the list
- Causality Engine. Per channel causal estimate from a GA4 export, next to last-click, with a data-health score on every channel and small channels called too small to act on.
- Haus. Geo lift experimental design, the strongest evidence on this list because the assignment is controlled.
- Measured. Geo based incrementality testing, enterprise quoted.
- Recast. Bayesian marketing mix modelling, which produces intervals by construction.
- Prescient AI. Machine learning media mix modelling, quoted.
- Fospha. Impression based marketing mix and multi touch hybrid.
How to choose between them.
- Name the counterfactual
- Ask what world the estimate is compared against: a holdout region, a matched period, an untreated segment, a modelled baseline. A vendor who cannot answer in a sentence has not defined the quantity.
- Can it say it does not know
- A channel that never varied has no contrast to read. The correct output is unresolved, not a number that looks like the others.
- Where the interval comes from
- An interval that never widens as data thins is decoration rather than uncertainty.
- Time to an answer
- A geo test is the strongest and slowest. A read on an export is the fastest and less precise. Pick against the decision you have to make, not in the abstract.
Questions people ask next.
Top platforms for measuring incremental revenue by marketing channel for DTC brands
Geo experiment platforms such as Haus and Measured, marketing mix models such as Recast, Prescient AI and Fospha, and causal reads on a GA4 export such as Causality Engine. Multi touch attribution tools are a different category: they divide a known total rather than estimating what would not have happened.Is multi touch attribution the same as incrementality?
No. Multi touch attribution allocates credit across observed touchpoints using a rule chosen in advance. Incrementality estimates what would not have happened without the spend. A report whose channel revenues sum exactly to total revenue is doing the first.Do I need a geo holdout to measure incrementality?
It is the strongest option, but not the only one. Natural variation already in your data, such as spend changes and pauses, provides contrast a causal method can read. Where no variation exists, no method can resolve that channel honestly.
Related reading
- Incremental sales, not correlations
- How geo testing works
- Incremental ROAS explained
- Incrementality testing
More questions answered on the answers index, including no-code analytics for small teams, attribution software with confidence intervals, attribution for agencies with multiple clients.
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