GeoX got a lot of fanfare this year. It soft launched in May and went globally available in September, giving marketers a new way to use geo incrementality testing alongside Meridian. As a Meridian-certified Google partner, we’ve had access, and we’ve been using GeoX to calibrate and fine-tune our MMM results.
That combination matters because MMM and incrementality testing answer different parts of the measurement problem. MMM helps explain how the wider media mix is contributing to business outcomes, while incrementality tests give us a controlled way to test whether a particular intervention actually caused a lift. Bringing the two together makes the overall model stronger, and Google has leaned into that by allowing incrementality tests to directly calibrate MMMs.
But GeoX was only one part of the same Meridian update. Full-Funnel MMM arrived much more quietly, and it tackles another measurement problem that marketers have been dealing with for years: brand spend often doesn’t get the attribution credit it deserves.
Brand media tends to build interest over weeks or months, while performance media often captures that interest much closer to conversion. Someone might see a YouTube campaign, become more interested in the brand, search for it later and eventually convert through paid search. The revenue shows up wherever the customer happened to convert, even though some of that demand may have been created much earlier in the journey.
Full-Funnel MMM is designed to make that earlier contribution easier to measure.
Following the path from brand to sale
The model does this in two stages. Stage one measures how brand media builds a brand equity metric. Stage two then measures how that equity, alongside everything else running, contributes to the final business outcome.
Separating those stages gives the model a better chance of reflecting the different jobs that brand and performance media are doing. Brand activity can build equity over a longer period, while performance media can continue to work over much shorter timelines. Connecting the two means the model can trace more of the path between the brand activity that helped create demand and the channel that eventually converted it.
The quality of that model then depends heavily on the signal being used to represent brand equity.
One accessible proxy is branded Google Query Volume, or bGQV. If brand activity is working, more people will search for the brand. That makes branded search volume a practical signal of growing interest or consideration that can feed into the first stage of the model.
Generic Google Query Volume can play a different role. It can help control for wider shifts in market demand, which makes it easier to distinguish between growth caused by marketing and growth caused by the category itself becoming more popular. That matters because a model that cannot see underlying market demand can end up handing media credit for demand it never created.
Branded search volume is only one option for the brand equity measure. Website visits or brand tracking survey data could work too, depending on the business and the hypothesis about how brand equity feeds into sales. The important part is choosing a signal that genuinely reflects the way brand activity is expected to influence future demand.
More detail only helps if the model holds up
That extra stage naturally adds more complexity. The specification has to be thought through carefully, the data has to be reliable, and the relationship between the two models has to make sense. If the equity signal is weak or the inputs are poor, the additional complexity is unlikely to improve the result.
For marketers with meaningful brand budgets, though, the potential value is significant. Full-Funnel MMM gives teams a structured way to understand what brand spend is doing before the conversion happens, and how that contribution feeds into the performance channels that ultimately capture demand.
That creates a stronger basis for budget decisions. If you can see more clearly where demand is being created and where it is being converted, you can make a better judgement about the role each part of the media mix is playing and where the next pound or dollar should go.
Making the added complexity practical
The challenge is that a more sophisticated model also takes more work to build and manage. That is where InsightMix comes in.
We built InsightMix to automate the process of building MMMs, and it has brought our time to deliver client results down to four to six weeks. Full-Funnel MMM can slot into that same pipeline, giving the team another modelling option where the data, media mix and business question support it.
That becomes more valuable as the model gets harder to build. The aim is to give clients the benefit of a more complete view of how brand and performance work together without requiring them to carry the full complexity of the modelling process themselves.
GeoX and Full-Funnel MMM are solving different problems, but they point in the same direction. Measurement is getting better at connecting what happens across the full journey: testing whether media caused an effect, understanding how brand builds demand over time, and tracing how that demand eventually turns into a business outcome. For marketers trying to understand what their brand spend is really doing, that is a meaningful step forward.
