Best Incrementality Testing Platforms for True ROI
Measured and Haus run geo holdout tests and Recast models your spend history; Causality Engine estimates what each channel caused from a GA4 export, for €99. A well-powered test remains the stronger evidence.
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By Joris van Huët, Founder & CEOUpdated 3 min read
Run the numbers for your store: the free holdout test planner.
Best Incrementality Testing Platforms for True ROI
If you can hold spend back in some regions for a few weeks, a geo holdout test is the strongest evidence of incrementality you can get: Measured and Haus run them, each test taking 4–8 weeks. Recast models it from a long spend history instead, and a causal attribution tool like Causality Engine estimates what each channel caused from a GA4 export, without pausing anything. Before you pay for any of them, run a rough incrementality test yourself: cut one channel's spend in one region for two weeks and compare revenue with the regions that kept running.
Why Incrementality Testing is Essential
Cookie loss and consent banners leave gaps in user-level tracking, and attribution models built on it inherit them. A test doesn't need user-level tracking: it compares regions or groups, so it measures what the ads added even when tracking is patchy. The price is time and paused spend.
Comparison of Incrementality Testing Platforms
| Tool | Methodology | Key Features | Pricing | Verdict |
|---|---|---|---|---|
| Causality Engine | causal inference | Per-channel credit with Direct split back to the channels that sent those buyers, last-click rebuilt for comparison, a data-health score and a recommended next step per channel | €99 one-time analysis or €299/month | A causal read of every channel from your GA4 export; an estimate, not an experiment. |
| Measured | Geo-based incrementality testing | Geo holdout experiments, 4–8 weeks per test | No public price: enterprise, quoted via sales (measured.com) | Built for enterprise brands running formal incrementality programs; each test pauses spend in the holdout regions. |
| Haus | Geo-lift experimental design | Geo-based incrementality experiments, 4–8 weeks per experiment | No public price: quoted via sales (haus.io/pricing, as read on 2026-09-08) | Built for brands wanting gold-standard incrementality proof; experiments take weeks and pause spend. |
| Recast | Bayesian Marketing Mix Modeling | Bayesian marketing mix modeling with longer historical analysis | No public price: quote-based (getrecast.com) | A good option for brands that want to use marketing mix modeling to measure incrementality; it requires significant historical data. |
What Causality Engine Does for Incrementality
Causality Engine doesn't run experiments. It uses causal inference on a GA4 export to estimate what each channel caused, with Direct split back to the channels that sent those buyers and last-click rebuilt from the same export next to it. Every channel gets a data-health score and a next step with how to test it, such as raising or cutting spend a little in one region and comparing revenue.
A read is faster and needs no paused spend, but a well-powered experiment remains the stronger evidence. Every number in the read checks against your export. What it doesn't do: it has no ad-platform connections today, it doesn't read campaigns or creatives, and it takes no spend data, so it doesn't report ROAS or marketing ROI per channel.
Every ad platform grades its own homework, so run the test on your side: your regions, your revenue. Rule of thumb: spend a test on the channel you're about to cut or double, and use cheaper estimates to decide where the next test goes. More on incrementality testing and marketing attribution.
Related Resources
Beta user case study: OFFFTRACK
Upper Funnel Attribution Problem: Measuring Awareness Campaign ROI
Frequently asked questions
What is the difference between attribution and incrementality?
Attribution splits the credit for a sale among the touchpoints before it; incrementality measures whether the sale would have happened without them. One tells you who was there, the other what they added.How do I get started with incrementality testing?
Start with one channel and one region: cut or pause that channel's spend in the region for two weeks, keep everything else the same, and compare revenue with the regions that kept running. Pick the channel you're closest to cutting or doubling, because that's where the answer is worth the most.What are the benefits of incrementality testing?
It tells you what a channel added, not what it was credited with, so you cut or scale on evidence instead of a platform's own report. The cost is time and paused spend: a geo test runs for weeks and holds budget back in the test regions.
Go deeper: Causal attribution, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Terms in this article
- Attribution ModelAn Attribution Model defines how credit for conversions is assigned to marketing touchpoints. It dictates how marketing channels receive credit for sales.
- Causal AttributionCausal Attribution uses causal inference to determine which marketing touchpoints genuinely cause conversions, not just correlate with them.
- Causal InferenceCausal inference is the set of methods for estimating how much an outcome changes because of a cause, rather than alongside it. It works by comparing what happened with an estimate of what would have happened without the cause.
- IncrementalityIncrementality is the part of an outcome, such as orders or revenue, that a marketing activity caused: the extra results compared with what would have happened without it. It is a causal effect, not the number of conversions credited to a channel.
- Incrementality TestingIncrementality Testing measures the additional impact of a marketing campaign. It compares exposed and control groups to determine causal effect.
- Marketing AttributionMarketing attribution assigns credit to marketing touchpoints that contribute to a conversion or sale. Causal inference enhances attribution models by identifying true cause-effect relationships.
- Marketing MixThe marketing mix is the set of actions a company uses to promote its brand or product. It traditionally includes product, price, place, and promotion.
- Marketing Mix ModelingMarketing Mix Modeling (MMM) is a statistical analysis that estimates the impact of marketing and advertising campaigns on sales. It quantifies each channel's contribution to sales.