Why DIY MMM Rarely Makes a Splash
Building your own marketing mix model (MMM) can seem enticing, but in reality, it can be more than you've bargained for.
Building your own marketing mix model (MMM) can seem enticing, but in reality, it can be more than you've bargained for.
The rise of AI and open-source models has commoditized marketing measurement, but do they truly deliver—or just distract?
Are the best solutions bought, or built? Every organization looking to elevate their MMM program to a Commercial Analytics solution will grapple with this question at some point.
Organizations looking to elevate their MMM program to a Commercial Analytics solution have a very weighty question awaiting them.
Like the boys from “Weird Science” and Dr. Frankenstein, in the world of Marketing Measurement, there are well-intentioned but needy parties trying to compensate for the limits of the real world by creating a strange new alchemy: Experimentation + Bayesian MMM.
Marketing leadership likes (and frequently trusts) real-time reports including ROI-like metrics such as ROAS. These constantly updated reports always provide something new to look at and promise opportunities for advertising tweaks. The real-time nature of these types of reports can be compelling
The Winterberry Group forecasts that marketing analytics and data infrastructure spend is expected to grow from $22 billion in 2022 to $32 billion in 2026.
From harnessing innovation to mastering organizational adoption and beyond, read on to find out how a holistic measurement framework is playing a key role in driving success.
Brands are getting hit hard by inflation and other macroeconomic factors, and marketing teams are under increasing pressure to cut costs.
Can attribution still deliver granular, actionable results? Or is now the time for forward-thinking leaders to adopt to a more unified view?