Unraveling granular insights of Advantage+ shopping campaigns through Marketing Mix Modeling (MMM)

MMM can provide granular insights

One of the biggest hurdles in adoption of MMM was that 'it does not provide granular insights'.

Historically it was one reason why Multi Touch Attribution (MTA) outscored MMM despite it being relatively complex, compute intensive and not providing a holistic Marketing Effectiveness picture.

It is another matter that MTA itself was never performed accurately by marketers. As the joke goes, people thought first touch and last touch = MTA. 😅

We at Aryma Labs have been leveraging more accurate Monte Carlo Markov Chain (MCMC) approach for MTA

With complete 3rd party cookie deprecation by Google in September/October this year, MTA's utility will be severely limited. Perhaps MTA will be continued to be used organizations that are walled gardens.

But that will leave the rest of the large majority with no adequate marketing measurement and attribution solution.

MMM was touted to fill this void. However, MMM not able to provide granular insights always remained a chink in its armour.

Well not anymore.

The Game Changing GTA-F Solution

Through our innovative approach, we cracked this granularity problem.

With our Game theoretic based approach which we call GTA-F, clients can now get answers to:

✅ Which Campaigns worked.
✅ Which TV channels worked.
✅ Which YouTube ads worked.
and more..

How do we know GTA-F really works?

One of our client Siluet magnanimously allowed us to publish the case study with real numbers (no masking).

This case study has extensive and detailed explanation of the whole process. You don’t normally see 10 page case studies :) but that is Aryma Labs for you. We believe in providing complete information to the client.

In a nutshell, Our GTA Rank (depicting order of campaign importance) not only matched the spends proportion from the in-platform metrics but they also matched the sales proportion numbers of a sub MMM model built specifically to test the efficacy of the granular solution !!

So through a triangulation approach of GTA Rank, In-platform spends proportion and Sub MMM models sales proportion, we honed in on the granular truth.

Through this case study we got accurate insights on which Advantage+ campaigns worked !!

What about PMAX campaigns?

Our GTA-F algorithm works on PMAX campaigns too. We can easily cull out granular insights for PMAX campaigns.

In summary:

Aryma Labs' innovative granular solution for MMM not only is fully geared to replace MTA but also enhance the very offering of MMM.

If you think granular insights of Advantage+ campaigns or PMAX campaigns can't be achieved with MMM, we have one thing to say - Try us !😎

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Thanks for reading.

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Stay tuned for more articles on MMM.