Find out which channels are actually earning their budget

Introduction

Last-click attribution only credits whatever happened right before a conversion. That means channels like TV, out-of-home, brand search, or upper-funnel display, the ones that build demand long before anyone clicks "buy", get almost no credit at all. If you're allocating budget off last-click numbers, you're probably over-investing in the channels at the end of the journey and under-investing in the ones that created it in the first place.

Marketing Mix Modelling, or MMM, gives you a statistical view of what each channel is actually contributing to your results across the whole mix, not just the parts a click can track.

What is Marketing Mix Modelling?

MMM estimates how much each marketing channel contributes to a business outcome, like sales or leads, using aggregate historical data rather than tracking individual users.

It takes your spend by channel, your sales or conversions, seasonality, promotions, and outside factors like weather or the economy, and works out statistically how much each channel is contributing to the outcome, independent of the others. This matters because offline channels were never trackable at the individual level anyway, and cookie and privacy restrictions make that harder for digital channels too.

Because of that, MMM can do a few things last-click simply can't. It gives you a defensible view of contribution across TV, radio, out-of-home, sponsorship and brand search, not just the digitally-trackable slice. It lets you model what happens if you shift 10% of TV budget into paid social before you actually spend the money finding out. And it gives you a way to justify offline and brand spend, which is usually the hardest budget to defend when only last-click numbers are on the table.

MMM vs last-click and multi-touch attribution

Last-click credits the final touchpoint only, so it misses almost everything that built the demand in the first place. Multi-touch attribution spreads credit across several tracked touchpoints in a journey, which is an improvement, but it still can't see anything untracked, so TV, print and offline stay invisible. MMM works at the aggregate level across the whole mix, tracked or not, so it's the only one of the three that can fairly weigh a TV campaign against a paid social campaign in the same model.

These methods aren't really competing with each other. A lot of mature measurement setups run MTA and MMM side by side, using MTA to optimise individual channels and MMM for the bigger budget calls.

Why it needs a solid foundation underneath it

MMM is only as good as the data going into it. If your existing attribution isn't reliable, an MMM built on top of it will still spit out confident-looking numbers that don't actually mean much. That's why it tends to suit organisations that already have solid digital measurement in place and are ready to extend that rigour across the rest of the mix, rather than being a starting point for someone with no attribution foundation at all.

Before starting, it's worth having clean, consistent spend and outcome data by channel, a clear sense of what's already trackable and what isn't, and at least a couple of years of history for the model to learn from.

Who this actually pays off for

MMM suits larger advertisers with meaningful spend across multiple channels, including offline ones, where budget decisions are big enough to justify a proper statistical model rather than a gut call. If you're a CMO or Head of Media making six or seven-figure calls about where next year's budget goes, this is built for that conversation.

If you're earlier in your measurement journey, still building out solid attribution or without much offline spend, MMM probably isn't the next move. Getting the foundation right first will do more for your budget decisions than jumping straight to the top of the ladder.

Get in touch with Fresh Egg's data engineering team if you want an honest read on whether your setup is ready for MMM, or what needs shoring up first.

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