The Incrementality Blog

Stanford Professor Brett Gordon sat down with Haus' Joe Wyer, Olivia Kory, and Phil Erickson to discuss his influential research around marketing measurement and incrementality.

See how Haus ColdStart uses comparable KPIs to test the incrementality of new products, markets, and channels that lack historical data.

MMM alone can't beat multicollinearity and noise. See why incrementality testing should anchor your model, with real results from StockX and OluKai.

Causal Attribution uses experiments to debias attribution, giving marketers a reliable read on daily performance that isn’t based solely on clicks and views.

We simulated a year of marketing decisions 36 million times. Accuracy protected the business. Volume didn't.

A year ago, Haus data showed Meta’s standard attribution performing better than their Incremental Attribution setting. Fresh analysis tells a new story.

The model should adapt to the way your business actually decides — which goes as far as your experiments allow

In this guide, we outline a better way to plan, test, and measure marketing during peak demand periods.

If you’re gearing up to propose a Causal MMM, then that probably means your current MMM isn’t cutting it, and you’re ready to make the case for something better. 

GeoLift answers whether a channel worked. Causal MMM answers what to spend on each channel.

Four years ago, we were a small team with a big point of view. Today, we're trusted by the most sophisticated marketing organizations in the world.

We analyzed dozens of incrementality experiments to learn how leading studios and streaming platforms use testing to answer the questions that matter most.

AppLovin is proving it can drive efficient DTC growth — now the question is how durable that performance is as the channel matures.

You're likely ready for an MMM when you’re no longer relying on just one or two channels and instead have activated a mix of channels. Let's dig deeper into what that looks like.

Marketers have long made billion-dollar budget calls on incomplete data. Architect is built to change that.

Most enterprise teams know their marketing data isn't good enough. Haus Enterprise Measurement Strategist Kevin Keeley knows a thing or two about where the best ones start.

For many enterprise businesses, their traditional MMM is packed with data but not actionable. Here's how Haus' Hannah Perez helps them operationalize MMM and experiments.

Every marketing team is running the same race: spend is growing, channels are multiplying, and the finance team wants to know whether any of it is actually working.

Media and entertainment companies don't have a measurement problem; they have several, stacked on top of each other in ways that make standard measurement tools look inadequate before you've even started.

Most marketing teams are spending money on measurement. The harder question is whether they're measuring the right things, with the right tools, in a way that actually leads to better decisions.

Incrementality is the marketing term for causality. An incremental conversion is one that resulted specifically from ad exposure, meaning it wouldn't have happened without the marketing.

Meta is overhauling its attribution settings, including a new Incremental Attribution option. Here's what's changing and how to test what works for your brand.

An effective channel diversification strategy starts with grounding your thinking in marginal efficiency — not average efficiency. Here's how to do that.

What Tottenham’s collapse reveals about the right — and wrong — way to use advanced analytics.

When brands cut prospecting to chase ROAS, they stop replenishing future demand – and history illustrates what happens next.

Meta’s attribution changes could be affecting more than your reporting. Here's why you should retest your setup and how to use lift tests to settle the debate.

Learn how Haus’ Tom O’Bara helps global brands and studios run impactful incrementality tests and answer their hardest measurement questions.

We unpack how Haus uses covariate stratification to help enterprise streaming platforms, broadcasters, and sportsbooks accurately measure the impact of ads run during live sports.

We offer heuristics for determining test duration based on data from thousands of Haus tests and advice from Haus Measurement Strategists.

Whether you’re actively evaluating incrementality platforms or simply curious to learn more, consider this checklist your first stop.

How automating hundreds of causal models a week – and grading them with a blind exam – yields better outcomes for businesses.

The Haus playbook for measuring tentpole brand campaigns

Haus’ Feliks Malts has partnered with hundreds of teams on their incrementality testing programs. Here's how he ensures they're set up to drive real business impact.

Haus is built on AI and machine learning that strengthens the speed, accuracy, and reliability of incrementality tests.

Let's unpack the differences between multi-touch attribution (MTA) and marketing mix modeling (MMM) and when each approach comes in handy for marketers.

Now live in the Haus app, Time Tests estimate the incremental impact of a significant change to your business when test and control groups aren’t feasible.

We analyzed over a hundred incrementality tests before, during, and after BFCM 2025 and uncovered dramatic delayed conversion effects.

We discuss some of the key questions to ask a potential MMM provider — and the importance of prioritizing causality.

Can TikTok scale? How much omnichannel impact does TikTok have? Haus’ analyses of hundreds of TikTok incrementality tests answers these questions and more.

Haus' AI-driven platform unleashes scientific rigor, reduces manual errors, and makes complex analytics accessible and actionable for every marketer.

Identification is all about teasing out real causal relationships between tactics and outcomes — and it's the backbone of Haus’ Causal MMM.

Use A/B testing to optimize and incrementality testing to prove impact. Dive into differences, use cases, and how to pick the right test.

Traditional marketing mix modeling (MMM) often relies on linear regression to illustrate correlation, not causation — but that's changing.

Haus Principal Measurement Strategy Specialist Arthur Anglade has seen a lot of MMMs in his day. He sits down to explain why Causal MMM is different.

Learn the fundamentals of marketing measurement — from ROAS and MMM to incrementality testing — and discover how to measure true campaign impact.

Meet Causal MMM: A marketing mix model that inspires trust, provides clarity, and drives smart marketing investments.

Let's explore incrementality from every angle — what it is, what you can test, and what you need to get started.

In this Q&A, John Bates Clark Medal-winning economist Susan Athey discusses why Haus is a company she just had to be a part of.

Discover what marketing attribution is, how it works, its pros and cons in 2025, and why incrementality testing provides a more precise measure of ROI.

Our large-scale analysis of real-world, branded search incrementality experiments unpacks when branded search is most effective and how best to leverage it.

Experiments reveal causality and establish the ground truth for cross-channel decision-making — even when it comes to OOH campaigns.

Learn how Haus Measurement Strategist Hoon Hong uses incrementality tests to sharpen brands' storytelling and connect with key audiences.

Traditional MMM fails on bad data. Learn how Haus treats experiments as ground truth, taming noise and multicollinearity for trustworthy guidance.

Haus experts offer four useful, non-obvious incrementality tests that can prepare you for the busiest period of the marketing calendar.

Explore key concepts, benefits, and practical steps around MMMs — helping you measure and optimize marketing effectiveness.

We explore incrementality experiments from all angles — what they are, why they matter, and how they translate into business impact.

From balancing the funnel to optimizing creative, our new guide has data-backed tips for improving performance on Meta.

We analyzed 640 Meta experiments on the Haus platform, revealing key insights into Meta's incrementality.

Explore geo experiments from all angles — what they are, why they matter, and how you can use them to measure incremental impact.

GeoFences enable you to exclude markets from your test that aren’t relevant to your business — helping you focus more deeply on the ones that are.

An exclusive Haus analysis show Meta is incremental in most cases — but is the platform's move toward automation improving incremental efficiency?

At our Open Haus AMA, a customer asked us: “How do you know when a brand is at the scale where investing in an incrementality tool makes sense?”

In our recent Open Haus AMA, we were asked why incrementality has become such a buzzword in marketing. Let's dive in.

Based on open source mobility data collected from mobile phone GPS data, Haus Commuting Zones minimize spillover and improve test accuracy.

Sometimes used interchangeably, it’s worth clearly distinguishing between these two approaches and what they accomplish.

Incrementality testing requires proper design and intentional analysis. Let's walk through some best practices (and potential mistakes).

We talked to Senior Technical Fellow Vanja Dukic about her journey to Haus and how Haus lets academics translate their knowledge into action. 

Haus’ exclusive Fixed Geo Tests enable marketers to rigorously measure campaigns that are traditionally difficult to quantify.

Dive into the key differences between attribution and incrementality — how each approach works, when they're useful, and key pros and cons.

Building an incrementality practice is vital for any brand that's trying to improve decision making. Let's explore how to get started.

Measurement Strategist Victoria Brandley walks us through her full-circle journey to Haus and how she became an incrementality believer.

Let’s walk through how to build a marketing measurement plan that puts experimental data at the center.

Our Measurement Strategists explain which tests can make a difference as you prep for Prime Day 2025.

Let's explore some of the differences between traditional media mix modeling and incrementality testing.

When macroeconomic conditions shift, marketers should proactively partner with finance, understand how budgets may change, and test for efficiency.

Incrementality testing isolates true campaign impact — giving you clarity, confidence, and a case your CFO will love.

This guide outlines the metrics, testing methods, and proven frameworks you can use to measure marketing effectiveness in 2025.

Senior Applied Scientist Ittai Shacham walks us through life on the Haus Science team and the diverse expertise needed to build causal models.

Platform creative tests may not meet the definition of a causal experiment, but they can be performance optimization tool within the bounds of the algorithm.

Brands need to have complete information to make responsible decisions like their company depends on it.

Let's unpack the pros and cons of building your own in-house marketing mix model versus working with a dedicated measurement partner.

Measurement Strategy Team Lead Alyssa Francis sat down with us to discuss how she pushes customers to challenge the testing status quo.

Fuzzy on some of the nuances around incrementality testing? This guide goes deep, unpacking detailed examples and step-by-step processes.

Plus: What to do when a test result is incremental but not profitable, and a framework for next steps after a test.

We sit down with Principal Economist Phil Erickson to talk about Haus’ “unhealthy obsession” with productizing causal inference.

When real-time retail data meets incrementality testing, CPG brands can finally measure what’s working and optimize ad spend with confidence.

In our first episode of Haus Spotlight, we speak to Measurement Strategist Chandler Dutton about the in-the-weeds approach Haus takes with customers.

An exclusive Haus analysis shows YouTube often delivers powerful new customer acquisition and retail halo effects that traditional metrics miss.

Causal MMM isn’t rooted in historical correlational data – it’s rooted in causal reality.

Causal MMM is a new product from Haus founded on incrementality experiments. Coming 2025.

Acknowledging uncertainty enables brands to make better, more profitable decisions.

As the customer journey grows more complex, incrementality testing helps you determine the true, causal impact of your marketing.

Haus’ synthetic control produces results that are 4x more precise than those produced by matched market tests.

Having the right measurement toolkit for your business is only meaningful insofar as your team’s ability to use that tool.

Vendor Central sellers – brands that sell *to* Amazon – can now use Haus to measure omnichannel incrementality.

Haus incrementality testing doesn’t rely on pixels, PII, or other data that may be vulnerable to privacy regulations.

Developing story: Starting in January 2025, some health and wellness brands may not be able to measure lower-funnel conversion events on Meta.

Sure, the title's a mouthful – but attributing changes in data (ex: ‘my KPI went up') to certain factors (ex: ‘we increased ad spend’) is hard to do well.

As brands' marketing strategies grow in complexity, incrementality testing becomes increasingly consequential.

Traditional MTAs and MMMs won't measure incrementality – but geo experiments reveal clear cause, effect, and value.