Education

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

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.

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.

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.

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.

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.

The Haus playbook for measuring tentpole brand campaigns

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

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

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.

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

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

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.

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

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.

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.

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).

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.

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

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.

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.

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

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.

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

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

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

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.

Haus’ Customer Marketing Lead Maddie Dault and Success Team Lead Nick Doren dive into what you can incrementality test – and why now's the time.

To kick off our new Incrementality School series, three Haus incrementality experts weigh in describing a very fundamental concept.

Think Amazon ads are the only effective marketing channel for Prime Day? Think again.

Not testing during your busy season may be costing you more than you think.

The media strategy that gets DTC brands from zero to one does not get them from one to ten.

5 common signs that indicate it is definitely time to start investing in incrementality.

Are you stuck wondering if you should be using 3rd party incrementality studies, ad platform lift studies, or trying to design your own? Find out here.

As platforms force more automation, the role of the media buyer is evolving. Read on to learn what to expect and what levers are left to pull.

While incrementality is better than relying on attribution alone, comparing them as-is is challenging. Thankfully, there’s a better way to get an unbiased data point regardless of the channel.

Learn which common marketing measurement tactics are accurate, precise, neither or both.

Leverage causal targeting to execute promotions that are actually incremental for your business.

Promotions, despite their potential power and ubiquity, are actually hard to execute well.

Geotesting is a near-universal approach for measuring the incremental effects of marketing across both upper and lower funnel tactics.