"Best" is doing a lot of work in that question.

On a feature grid, incrementality platforms look nearly identical: geo experiments, some flavor of media mix modeling, a dashboard, a few integrations. They look very different the moment you're in a room with your CFO explaining why you moved $2 million out of a channel.

So it helps to flip the question. Instead of asking which platform is best in the abstract, ask which decisions you need to make over the next six to 12 months — then work backward to the capabilities that support them. Here's how that maps, use case by use case.

Four things that separate platforms

Before the use cases, four dimensions worth comparing directly. Most feature lists blur them together.

Method. How are test and holdout groups built? Some approaches test a handful of large, high-signal markets and extrapolate to the country. Haus uses random stratified sampling to assign test and control groups across a country or region, so results are representative of the market rather than a few big cities.

Precision. Ask whether you get power and precision calculations before a test runs, and confidence intervals after. A lift estimate with an interval running from 0% to 20% is technically a result. It's not something you can move budget on.

Coverage. Which channels, which outcomes, which geographies. Digital-only coverage is common. Offline, retail, marketplace, and international coverage is less common.

Support. Whether a person helps you decide what to test, interpret an ambiguous readout, and defend it internally. This is the dimension buyers most often discover they needed after they've signed.

You want to know whether a channel is driving sales

This is the entry point for most teams, and it's the use case where the widest range of options will technically work.

Incrementality experiments identify the marketing activities causing business outcomes by comparing groups exposed to your marketing against groups who weren't. If your question is "is Pinterest incremental or is it taking credit for sales I'd have gotten anyway," almost any geo experimentation approach will produce a number.

What varies is whether you can trust the number, and how fast the next one arrives. One test gives you one data point about one channel at one spend level in one season. A program gives you a compounding picture. Haus customers often run several tests a month, and the platform runs roughly 4,000 experiments a year across its customer base — which also means benchmarks and channel meta-analyses that put your own result in context.

Cody Plofker, CEO of Jones Road Beauty, describes the pattern: "We theorized that YouTube was driving more value than could be measured by clicks, and we tried triangulating a bunch of sources. We even tried our own internal lift testing, but didn't have the resources to get there."

Look for: Transparent methodology, upfront power analysis, and enough velocity to build a roadmap rather than a one-off.

You're testing upper funnel or hard-to-click channels

Out-of-home, linear TV, sponsorships, influencer pushes, product drops, regional radio, direct mail. These are the channels where click-based reporting has the least to say, which is exactly why they tend to be under-invested.

Not every platform handles them. Many open source packages were designed with US digital channels in mind, and extending them to offline or international activity is possible but requires heavy customization.

Haus supports three experiment types here. GeoLift covers channels where you can vary spend by geography. Fixed Geo Tests are for campaigns where you pick the regions, which suits OOH, retail activations, and events. Time Tests handle big time-bound moments by evaluating that window against a high-precision daily forecast of what would have happened otherwise.

Look for: Named support for offline and fixed-region designs, and international capability if you need it.

Your sales happen across DTC, Amazon, and retail

If a meaningful share of revenue lands somewhere other than your own site, single-KPI testing will systematically undercount your marketing.

Many platforms analyze one KPI at a time, with each test designed independently. That leaves you comparing results that were never balanced against each other. Haus measures incremental impact across DTC, Amazon and marketplaces, and offline retail, and balances multiple KPIs within a single test design.

Melissa Reisor, Director of Growth at OSEA, puts it this way: "Haus has been essential in helping us move beyond optimizing solely for DTC. It gave us the visibility we needed to set smarter media budgets and understand how our upper funnel campaigns can drive omnichannel sales growth across key partners."

Look for: Halo effect measurement across channels, and multi-KPI test design rather than sequential single-KPI tests.

You need to allocate the whole budget, not just evaluate one channel

At some point the question stops being "does this channel work" and becomes "where should the next $10 million go." That's a marketing mix modeling (MMM) question.

The trade-off in this category is real. Traditional MMMs are built on historical correlations, which is why two models trained on similar data can disagree, and why a model can contradict an experiment you just ran. Onboarding measured in quarters is common.

Haus' Causal MMM takes a different route: it uses experiments as ground truth rather than adding them after the fact as calibration. Every new test feeds back into the model. Recommendations are transparent about how your data drove them, first readouts come in weeks rather than months, and models refresh weekly.

Look for: How experiments enter the model — built in from the start, or bolted on afterward. Ask to see the priors and assumptions.

You're evaluating open source or a DIY build

AI coding assistants have made it easy to stand up a working geo test prototype in an afternoon. That's genuinely new, and it's worth taking seriously rather than dismissing.

Running one test has always been possible. The harder parts are what to test next, how to sequence a roadmap, how to reconcile a result against what Meta reports and what your post-purchase survey says, and who defends the methodology when leadership doesn't like the answer. Open source packages like Meta's GeoLift and Google's GeoX were built as research toolkits, not enterprise platforms — vendors across the space, including some historically pro-open source ones, have publicly acknowledged adapting them because out-of-the-box confidence intervals were too wide to act on.

There's also a subtler risk. When a result comes back that leadership dislikes, the temptation is to add a variable or shift the window, and a model will comply every time. Pre-committing to methodology before data comes back is less a technical feature than organizational protection.

Look for: Pre-committed methodology, placebo and leave-one-out validation, and a clear owner when the analyst who built it leaves.

Five questions to ask any vendor

1. How wide are the confidence intervals on a real test you've run — and can you show me?

2. Do you run power and precision calculations before I commit budget?

3. How many tests does a typical customer run per quarter?

4. What happens after the first test? Walk me through the second, third, and tenth.

5. Who sits with my team when the CFO pushes back on a result?

Where Haus fits — and where it might not

If you want a single all-in-one suite where incrementality is one tab among many, or you're primarily shopping for benchmarks rather than tests on your own business, other options may suit you better. Haus takes the position that benchmarks can mislead: similar businesses running similarly designed tests get different results because creative, targeting, and mix differ.

For most teams, though, the through line is the same. The platform question was never really "can I run a geo test." It's whether you can build a program that makes your business smarter over time.

See how Haus approaches incrementality →