Practical guides on marketing effectiveness, measurement frameworks, and Marketing Mix Modelling. Written by practitioners, not salespeople.
What MMM is, how it works, which industries use it, and why it matters more than ever in a post-cookie world. The only guide you need to get started.
Read the guide →The real cost breakdown of traditional MMM, why 80% goes on data prep, and how AI is bringing the price down from six figures to something usable.
The data prep bottleneck has always been the blocker. Here's how AI automation is removing it, and what that means for speed, cost, and accuracy.
An honest comparison of Meta's Robyn, Google's Meridian, Analytic Partners, Nielsen, and the newer AI-powered alternatives. Free and paid.
Two approaches to measuring marketing effectiveness. When to use which, why they're complementary, and what cookie deprecation changes.
The exact data inputs required, common gaps and how to work around them, and how AI handles messy, inconsistent files.
What effectiveness actually means, which metrics matter by channel, and how top UK companies connect spend to outcomes.
How to build a measurement plan, track ROI across digital and offline channels, and know when you've outgrown dashboards.
From web analytics to MMM platforms. What each layer of the measurement stack does and what to buy first.
Can DTC and ecommerce brands use Marketing Mix Modelling? What's different, what data you need, and where it falls short.
Both measure marketing effectiveness, but they work differently. When to use each and how they complement each other.
What to look for, questions to ask, red flags to watch for, and how to evaluate proposals from MMM consultancies.
The measurement questions that actually matter for budget decisions — backed by industry data — and the specific MMM outputs that answer them.
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