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Chris Kervinen

Sellforte | Five things to learn from your ROMI data

Five things you should look from your ROMI data

Ok, so you put in the time and effort (and, let’s face it, blood, sweat and tears) into a kick-ass marketing plan, executed on it, sending multiple, beautifully crafted, media types out into the world across multiple media channels — all in the hope that it would win customer’s hearts and (hopefully) wallets.

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Sellforte | Methodological pitfalls of traditional Marketing Mix Modeling

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).

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Sellforte | Five things to learn from your ROMI data

Five things you should look from your ROMI data

Ok, so you put in the time and effort (and, let’s face it, blood, sweat and tears) into a kick-ass marketing plan, executed on it, sending multiple, beautifully crafted, media types out into the world across multiple media channels — all in the hope that it would win customer’s hearts and (hopefully) wallets.

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Sellforte | What data do I need for Marketing Mix Modeling?

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.

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