How to choose a marketing attribution platform that identifies true incrementality
Three method families produce incrementality, and most tools run none of them. Geo experiments hold spend back, so Haus and Measured give the cleanest evidence and cost the most. Marketing mix models infer it from history, which is Recast and Prescient AI. Causal inference on an existing export withholds no spend, which is Causality Engine. Multi touch tools such as Northbeam allocate rather than measure.
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 |
| Northbeam | Method: Machine-learning multi-touch attribution | Pixel: Yes | Starting price: $1,500/monorthbeam.io/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. Causal inference on a GA4 export you already have, per channel, priced per read rather than per month.
- Haus. Geo lift experiments. The counterfactual is observed, not modelled, which is the strongest evidence available.
- Measured. Holdout based incrementality testing across channels, enterprise quoted.
- Recast. Bayesian marketing mix modelling that infers incrementality from spend history.
- Prescient AI. Marketing mix modelling aimed at mid market ecommerce.
- Northbeam. Multi touch attribution. Allocates an observed total by a rule, which is a different question from incrementality.
How to choose between them.
- Observed or inferred counterfactual
- A geo holdout observes what happens without the spend. Everything else infers it. Observed beats inferred whenever you can afford to withhold budget for long enough, and that condition is the whole trade.
- What it costs you to run
- The real price of a geo test is the withheld spend, not the software. For a brand at five figures a month that can be the most expensive line in the exercise, which is why modelled approaches exist.
- How much history it needs
- Marketing mix models want years and enough variation in spend to separate channels. Causal reads on an export want months. A tool that promises incrementality on a thin history is promising precision it cannot have.
- Does it cover organic
- Incrementality is usually sold as a paid media question, but organic search, organic social and email all have an incremental component. A platform that only models the paid slice answers a narrower question than the one being asked.
Questions people ask next.
How to choose a marketing attribution platform that identifies true incrementality
Decide which method family fits first. Geo experiments observe the counterfactual and cost withheld spend. Marketing mix models infer it and need years of history. Causal inference on an existing export withholds no spend and needs months. Multi touch attribution allocates a known total and does not measure incrementality at all.Is multi touch attribution the same as incrementality?
No. Multi touch divides revenue you already have across touchpoints using a rule chosen in advance. Incrementality asks what would not have happened without the spend. The two can disagree completely and both be computed correctly.Can I get incrementality without pausing any ads?
Yes, by inference rather than experiment. A marketing mix model or a causal read on historical data estimates the counterfactual instead of observing it. The estimate is weaker than a holdout and costs no withheld budget.
Related reading
More questions answered on the answers index, including choosing a tool to cut wasted spend, vendors that publish their method, attribution tools for GA4 and Shopify.
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