Blog
Adstock and Carryover Effects in MMM: Why Marketing Has a Memory
May 6, 2026 · 11 min read
Adstock is the math that captures how marketing effects persist after the spend hits. Without it, your MMM systematically misattributes credit between channels. Here's what adstock is, why every serious MMM uses it, and how to think about it whether you're using a tool or building your own.
Saturation Curves in MMM: Why Doubling Your Spend Won't Double Your Sales
May 6, 2026 · 10 min read
Every marketing channel hits diminishing returns. Modeling that curve correctly is the difference between an MMM that recommends realistic budget shifts and one that tells you to spend infinity dollars on whatever channel looked best last quarter. Here's how saturation works in MMM and why it's non-negotiable.
Bayesian Priors in MMM: Why Pure Frequentist Models Fall Apart
May 6, 2026 · 11 min read
MMM is a small-data problem dressed up as a big-data one. With dozens of parameters and only a year or two of weekly data, pure frequentist estimation produces unstable, often nonsensical results. Bayesian priors are how serious MMM tools tame the chaos. Here's why they matter, what they actually do, and how to think about them.
Why Your MMM Needs Credible Intervals (and Point Estimates Will Mislead You)
May 6, 2026 · 10 min read
A single ROAS number for each channel is the most dangerous output your MMM can give you. Without uncertainty quantification, you can't tell signal from noise — and you'll make budget decisions on numbers the model isn't actually confident in. Here's why credible intervals matter, what they actually mean, and how to use them.
Control Variables in MMM: The Non-Marketing Stuff That Decides Whether Your Model Works
May 6, 2026 · 11 min read
Sales go up in November. If you don't tell your MMM that's because of holiday shopping, the model will attribute the lift to whatever channel happened to be active. Controls — seasonality, trend, holidays, day-of-week — are how you stop the model from confusing background noise with marketing impact. Here's how they work and why they often decide whether an MMM is useful or misleading.
Multicollinearity in MMM: When Your Channels Move Together (and the Model Can't Tell Them Apart)
May 6, 2026 · 10 min read
If you spent on Meta and Google in roughly the same amounts every week, no model in the world can confidently tell you which one drove the sale. Multicollinearity is the silent killer of MMM accuracy — and the reason your model gives you wildly different ROAS estimates each time you re-run it. Here's how to spot it, what to do about it, and when to give up and run an experiment instead.
How to Calculate Marketing ROI: A Practical Guide for 2026
April 13, 2026 · 10 min read
Marketing ROI sounds simple until you try to calculate it across multiple channels. This guide covers the basic formula, where it breaks down, how to handle attribution, and when you need more sophisticated methods like marketing mix modeling.
How to Measure the ROI of Google Ads Without Trusting Google
April 3, 2026 · 9 min read
Google Ads reports inflated ROAS for reasons most marketers never question. This guide explains the structural problems with Google's conversion tracking, the branded search trap, and how to independently measure whether your Google spend is working using MMM and incrementality testing.
The Best Free Marketing Mix Models in 2026 (Tested and Compared)
March 28, 2026 · 9 min read
A practitioner-tested comparison of every free marketing mix modeling option in 2026 — from browser-based tools to open-source frameworks to spreadsheet templates. What "free" actually means for each, what you get, and which one is right for you.
How to Measure the ROI of Meta Ads Without Trusting Meta
March 27, 2026 · 9 min read
Meta Ads Manager ROAS is structurally inflated. This guide explains why, what the numbers actually mean, and how to independently verify whether your Meta spend is working using marketing mix modeling and incrementality testing.
How to Prepare Your Data for Marketing Mix Modeling
March 26, 2026 · 9 min read
A practical guide to formatting your marketing data for MMM. Covers weekly vs. monthly granularity, handling zero-spend periods, channel naming, promo flags, common CSV mistakes, and a ready-to-use template.
MMM vs. Attribution vs. Incrementality Testing: Which Do You Actually Need?
March 14, 2026 · 10 min read
Marketing mix modeling, multi-touch attribution, and incrementality testing each answer different questions. This guide explains what each one does, where it breaks down, and how to decide which to use based on your team size, budget, and channels.
Marketing Mix Modeling for Ecommerce and DTC Brands
March 13, 2026 · 10 min read
How to use marketing mix modeling when you run a DTC or ecommerce business. Covers data prep from Shopify and ad platforms, why platform ROAS lies to you, handling promos and discounts, and what actionable MMM output looks like for online brands.
What Is Marketing Mix Modeling? The No-BS Guide for Marketers Who Actually Spend Money
March 12, 2026 · 12 min read
Marketing mix modeling explained for working marketers. How MMM works, what data you need, how it compares to attribution and incrementality testing, and how to get started without a data science team.
How to Interpret Marketing Mix Modeling Results
March 9, 2026 · 7 min read
MMM gives you ROAS by channel. Here is what to do with those numbers — how to pressure-test them, what to question, and how to turn model output into a budget decision.
How Much Data Do You Need for Marketing Mix Modeling?
March 9, 2026 · 6 min read
The honest answer to how much historical data MMM actually requires — and what happens to your results when your dataset is thin.
Free Alternatives to Meta Robyn and Google Meridian for Non-Technical Marketers
March 8, 2026 · 5 min read
Compare free MMM tools like Meta Robyn, Google Meridian, PyMC-Marketing, and CheapMMM. Find the best no-code alternative to Robyn for marketers without data science teams.