Measured incrementality alternatives for Amazon brands

Spillmark runs geo holdout tests for Meta ads and measures Amazon orders by ship-to state. It plans the test from your own Amazon order history, picks a group of US states whose past sales track the rest, runs a placebo check, tells you how long the test needs and shows the expected cost before you pause anything.

$19 a month gets the dashboard. Holdout tests start on Starter at $49 a month. Cancel anytime. Read-only, and nothing changes without your approval. Use the Get started button under the introduction.

Against Measured, you give up an enterprise program: geo-matched tests across channels, a media mix model, an optimizer and an analytics expert on every account. You keep one holdout on Meta ads and Amazon orders. Measured's own description is in the sections after this one.

The method is on how lift is calculated. Size a test with the holdout test calculator before you pause ads.

Measured's homepage, as of October 2026, describes an AI-powered marketing effectiveness platform that combines causal experiments with media mix modeling, under the line "Built for enterprise." It lists five products:

• Incrementality Testing: real-world experiments to find the impact of each channel.
• Media Mix Modeling: a model calibrated by those tests.
• Media Plan Optimizer: where and how much to spend to hit a business goal.
• Cross-Channel Reporting: one view of performance across your media.
• Benchmarks: spend, ROAS and efficiency compared with peer brands.

Its enterprise page lists 300+ integrations and support for global, multi-brand portfolios, and says every customer is paired with an analytics expert trained in incrementality, MMM and omnichannel optimization.

Measured's incrementality page says its tests are geo-matched or first-party split tests. A geo-matched test changes media in some markets and compares them with similar markets where nothing changed. A split test divides an audience you own, such as a direct mail list, into a group that gets the media and one that doesn't. Measured says machine learning selects the markets and the platform handles the splitting, test design and calibration, so a test launches "in days, not quarters."

Matching matters because the two groups should sell in step without the test, so any gap that opens up while it runs is the media's effect. Matched market testing covers how to pick the groups, and difference-in-differences covers the comparison.

A made-up example shows the arithmetic. Before a test, the ads-on states sold 3,000 Amazon orders a month to the holdout states' 1,000. During the test, with Meta ads paused in the holdout states, the counts were 3,000 and 900:

lift = (ads-on orders ÷ holdout orders, during) ÷ (ads-on orders ÷ holdout orders, before) − 1
     = (3,000 ÷ 900) ÷ (3,000 ÷ 1,000) − 1 ≈ 11%

In this example, Meta ads were adding about 11% to Amazon orders where they ran. A real test also reports the range around that number, since a few weeks of orders are noisy.

Measured's triangulated measurement page puts three signals together. Here's what it says each one does, next to a version a small brand can run without a platform:

The same page lists the weak spot of each signal alone: a model infers from history without proving cause, a lift test is a snapshot of one moment, and platforms grade their own homework. That matches the case for pairing them in marketing mix modeling vs. incrementality testing. For a brand on one or two channels, the experiment comes first, since a model needs a long history of spend across channels to learn from.

Measured's omnichannel retail page says it measures lift across DTC, Amazon and physical stores, separates media-driven sales from baseline traffic, and tracks performance by region, brand and retailer, with geo and audience-level lift tests as part of the same offer. So Measured's site does describe the halo from ads onto Amazon, which is the effect a brand selling through Meta ads and Amazon needs measured.

Measured's pages describe a platform for enterprise, omnichannel and multinational companies. If your main question is whether Meta ads pay off on Amazon, there are lighter routes to the same experiment:

• Run a geo holdout yourself. Get orders by ship-to state, pick two groups of states that sell in step, pause Meta ads in one group with location targeting for several weeks, and use the formula above. Size it first with the holdout test calculator.
• Use open-source tools. GeoLift, from Meta Open Source, is an R package for geo experiments. Robyn and Meridian are open-source media mix models if you later want one.
• Use a self-serve testing platform. Haus, for example, lists a self-serve Basics plan for DTC brands selling via Shopify and Amazon, as of October 2026. See the Haus incrementality guide.
• Use a tool built for Meta and Amazon. That's the first section on this page: one holdout, with the price in the shortlist.

Stay with Measured if you want experiments, a media mix model and an expert across DTC, Amazon and stores, and you can ask for a price. Use the holdout when Meta ads and Amazon orders are the whole question. Haus is the self-serve geo testing vendor in the shortlist. Recast is the Bayesian MMM vendor.