6 Minutes Read By Felix Gerlsbeck

Decoding Data & AI: Why is everyone talking about MMMs (Marketing/Media Mix Models)?

#Advanced Data Analytics#Artificial Intelligence#Decoding Data & AI#Digital Strategy#Digital Transformation#Marketing#Tech

OMMAX holds in-depth expertise in data strategy, data engineering, and advanced data analytics. We have a proven track record of successful projects implementing process automation with AI, data warehouse setup, or optimized resource allocation for clients of all industries from healthcare to manufacturing.

In our Decoding Data & AI series, we provide you with key insights for successful data & AI projects in a clear and easily understandable format, empowering your business to thrive by facilitating integration into your corporate strategy. As part three of our “Decoding Data & AI” series, we are having a look at Marketing Mix Models (MMMs): what they are, why everyone is talking about them, and what value they bring.

If you want to get the highest return for your marketing investment, you need to know how much effect a given Euro spent has on your top line. Of course we have crude measures like overall ROI, but what we actually need to know is what this effect is for each channel separately and in combination. This is more difficult than one may think, because the different marketing channels interact and constantly cross over into each other’s paths. I see your ad on Instagram, then on TV, then I watch an influencer video with your product in it, and then I finally get pushed over the edge and whip out my credit card. And on top, for every potential customer out there, this works differently. This is a bit too complex to measure directly.


What we need here is what economists call a model - a description of how people will react to different marketing choices, but slightly simplified. Much like an architectural model will show me the general outline of the building but not include every bathroom installation, my Marketing Mix Model will show me in general terms how quickly the credit cards are coming out of the wallets when I change my marketing mix.

In the end, this is a mathematical formula that will predict my revenue based on the money spent on different media and marketing channels. Since this model can get a bit complicated, like in most complex data analysis, it is useful to let AI do the heavy lifting. This is a typical machine learning problem, where the idea is to feed lots of data to a self-learning algorithm, which will construct our model by finding patterns in the data that a human analyst won't be able to see.

Luckily, both Google (LightweightMMM) and Meta (RobynMMM) believe this problem is important enough that they have recently created Open Source versions of this self-learning algorithm that everyone can download, use, and adapt to their own purposes.

You "only" have to bring your data to the table. However, as always, more and higher quality data is a lot better than less. Data quality and proper data management are absolutely essential to leverage the power of such AI-driven solutions. The more data we can throw into those models - timing, size, and location of your campaigns, as well as revenue data on the most granular level - are crucial. The cherry on top would be any data about competitor promotions you may have collected.

Feeding that input data into a marketing mix model allows the latter to address the million-dollar question of marketing: how should ad spend be distributed across the different marketing channels, in order to maximize conversions and therefore revenue?
 


Let's take a look at the output from Robyn - the MMM package by Meta: It tells you both what you are currently spending in your marketing mix and how much the different channels contribute to your ROAS - already quite helpful. This is shown on the left. However, since this is a full model, it will also show you how much more you could make with the same spending level, just by optimizing your channel mix.
 


What you can see in this example is that just by optimizing allocation, the client could generate an additional 560,000 conversions, an increase of 22.6% while keeping total spending the same. Robyn suggests to substantially reduce investment in TV and Out-of-home (billboards) advertising, and increase search and print media.

You can see that this is a powerful way to supercharge your marketing decisions by moving beyond simple measures to really leverage the value of your data.

Want to learn more about OMMAX's expertise in data & AI? Get in touch with our experts through the form below and sign up for our Decoding Data & AI series!

By Felix Gerlsbeck

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