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· What was my last campaign’s return on marketing investment?

· How did the different channels perform individually and as a whole campaign?

· What action does the results imply me to take?

These are some of the key questions marketers have and should be able to answer in a post-campaign analysis. Looking at one campaign’s data isn’t, however, a feasible way to get accurate & reliable results, especially if there’s multiple overlapping offline and online media channels at play (which usually is the case with larger advertisers). In order to measure individual media channel’s sales & profit uplift, you need to look at marketing effectiveness on a much longer time period.

World of Marketing Mix Modeling Experts

In our series of ”World of Marketing Mix Modeling” we’re entering the Matrix and diving into sea of data to bring you the real life superstars of Marketing Mix Modeling.

Who:

Joonas Imeläinen

What data do I need for MMM and where to get it from?

Neil Borden has been identified as one the first persons to use the term marketing mix in his famous phrase from 1949: “An executive is a mixer of ingredients, who sometimes follows a recipe as he goes along, sometimes adapts a recipe to the ingredients immediately available, and sometimes experiments with or invents ingredients no one else has tried." The mix (which consisted of 4Ps at first, then 7Ps, and eventually 7Cs) has been somewhat clear for the marketers throughout the years, the modeling on the other hand not so much. We’ve previously covered what is Marketing Mix Modeling and what ROMI stands for , but in this article we’re diving into the ingredients – data inputs – you need for Marketing Mix Modeling.

There has been a lot of discussion around the Bayesian approach in Marketing Mix Modeling, with some of the most data-savvy companies and agencies doubling up their investments in this methodology. Today we'll break down what it is, where it came from and what is the benefit of it.

In our series of ”World of Marketing Mix Modeling”

we’re entering the Matrix and diving into sea of data to bring you the real life superstars of marketing mix modeling.

How unbelievably crazy it may sound, Marketing Mix Modeling (MMM) solutions and cars have more in common than you might think. They both take you from point A to point B by utilizing fuel and turning it into velocity. And like with different car manufacturers, some MMM solutions take you to your destination faster than the other ones.

How unbelievably crazy it may sound, Marketing Mix Modeling (MMM) solutions and cars have more in common than you might think. They both take you from point A to point B by utilizing fuel and turning it into velocity. And like with different car manufacturers, some MMM solutions take you to your destination faster than the other ones.

As we go into the 2020s, traditional Marketing Mix Modeling (MMM) just won’t cut it anymore.

‍ A recent study by Google (2017) reveals that the older models and techniques seem to fall especially short in three areas: data limitations, selection bias, and model validation.

Statistical models used by marketers to measure the effectiveness of their advertising spend have been around in various forms since the 1960s. The natural selection has favored regression analysis above other lifeforms, and not least because it’s somewhat easily grasped and implemented regardless of the company size or marketing environment. But as time goes by, older versions become obsolete and new, smarter ways of life ascend the food chain (as homo sapiens we ought to know).

What data do I need for MMM and where to get it from?

Neil Borden has been identified as one the first persons to use the term marketing mix in his famous phrase from 1949: “An executive is a mixer of ingredients, who sometimes follows a recipe as he goes along, sometimes adapts a recipe to the ingredients immediately available, and sometimes experiments with or invents ingredients no one else has tried." The mix (which consisted of 4Ps at first, then 7Ps, and eventually 7Cs) has been somewhat clear for the marketers throughout the years, the modeling on the other hand not so much. We’ve previously covered what is Marketing Mix Modeling and what ROMI stands for , but in this article we’re diving into the ingredients – data inputs – you need for Marketing Mix Modeling.