Traditional MMM takes months and costs six figures. Most of that time is spent preparing data. We automated that part.
Traditional MMM engagements take 3–12 months. Most of that is data prep. We compress it.
You stop paying agency day-rates for someone to clean spreadsheets. That's where the savings come from.
Every mapping decision, transformation, and assumption is logged and reviewable. You can see exactly how we got from raw data to model input.
The modelling libraries behind MMM are open-source and well understood. Meta's Robyn and Google's Meridian are freely available. The maths isn't the expensive part. The data preparation is.
Every MMM project starts the same way: collecting exports from ad platforms, cleaning inconsistent date formats, aligning naming conventions across sources, handling missing data, and reconciling everything against finance records. This is where most projects stall or die.
We automated the data preparation layer. Our pipeline infers the schema from raw exports and maps them to MMM-ready formats. When channel names don't match across sources — and they never do — it resolves them automatically. It reconciles totals against finance data before anything reaches the model. And it logs every decision it makes, so you can review the lot.
The statistical modelling still needs experienced analysts. We're just removing the months of grunt work that sits in front of it.
Send us your data as-is. We handle the rest.
Platform exports, spreadsheets, CSV dumps. We take it as-is. No need to pre-clean or standardise anything.
Schema inference maps your data to MMM requirements and resolves naming conflicts across sources. Gaps get flagged early, not discovered three months in.
Nothing goes into the model unchecked. Every mapping decision and assumption is presented for review before it's accepted.
Clean, validated data goes straight into the model. Outputs arrive in time to influence planning, not post-rationalise it.
The return on every pound spent, broken down by marketing channel.
Where to increase spend, where to pull back, and the expected revenue impact.
Where additional spend stops generating proportional returns.
Your marketing effect separated from seasonal trends and external factors.
How long each channel's impact persists after spend stops.
Reusable dataset and process. Your next refresh takes days, not months.
We've sat through the procurement process, signed off on six-figure scoping documents, and then watched the first three months disappear into data cleaning. Good measurement projects shouldn't die because the data prep took longer than the planning cycle.
So we built tooling to fix that. The modelling still needs skilled analysts. But the months of manual data work before it? That can be automated, and it should be.
Get in touch to discuss your needs with our measurement experts.
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