For years, marketers and advertisers alike have been trying to perfect the attribution model. For years, marketers and advertisers have argued that first touch is more important… no, last touch is more important. All the while, we’ve added more layers to the marketing pile that has only made it more confusing when trying to figure out what piece of advertising impacted a single sale (little secret, it all works together).
Fast forward to the very recent past, and we’ve added layer upon layer of data on top of the layers of marketing piles. We have tried to make more sense of the attribution models by showing the different levels of consumer data, including trends, before’s and after’s, and everything else we can think of.
All of this has us still guessing whether first touch or last touch are the most important touch to a marketer.
Now we have a new flavor, and that flavor is marketing mix modeling. According to martech.org, MMM is a large regression model that utilizes different variables in order to help marketers measure impact of advertising and marketing dollars on KPIs and results.
While this concept isn’t entirely new, it’s new in the sense that Apple changed a lot of the ways that we collect data. Due to this change, we analysts had to reimagine how we look at the impact of media spend.
Because data sets all seem to return different results (think GA compared to your API upload every morning that never matches), MMM brings in competitive landscape, total sales, economic data and a slew of other variables that can impact why someone would buy a specific brand of shampoo.
All these levels of data and different variables impact how each consumer interacts with the marketing. Smartly placed product integrations or perfectly timed creative can have a higher impact on marketing than advertising dollars and GRP levels. We live in a society where there are too many points of stimulation to continue to rely on the attribution models of yesteryear.
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