[Updated March 2022]
As privacy laws change the way marketers can gather and leverage data, our team shares how brands can reliably gain insights and measure performance through Marketing Mix Modeling.
By now, most marketers are aware that open access to the cookies, mobile device IDs, and other data sources fueling advertising performance analytics is declining. As new privacy measures are rolled out across the web, marketers are now looking for new ways to responsibly measure campaign success and continue to deliver customized experiences for consumers online.
That’s where Marketing Mix Modeling comes in. Once thought to be slow and cumbersome, new, optimized forms of Marketing Mix Modeling have been designed to operate in today’s digital-heavy atmosphere, allowing for companies to reach detailed insights on online behaviors, even with limited access to the individualized data of the past.
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Meta (formerly Facebook) recently commissioned Accenture to demonstrate the effectiveness of Marketing Mix Modelling. Accenture found that Marketing Mix Modeling, when integrated with analytics models and techniques, provides nuanced, multi-channel impact analyses that modern marketers can use to drive success – without requiring the use of individually identifiable information.
Here’s why adopting Marketing Mix Modeling now will set brands up for future success in the privacy-first world:
Unlike identity-based measurement systems, Marketing Mix Modeling is not powered by user-level data. Instead, Marketing Mix Modeling uses aggregated data from a range of variables and channels to examine marketing effects without any user tracking.
What does this mean for marketers? While privacy regulations continue to change over time, Marketing Mix Modeling provides a stable measurement tool unchanged by fluctuations in data availability. In a series of interviews of measurement experts, Accenture found that this makes Marketing Mix Modeling the methodology most resilient to privacy changes.
Veteran marketers may shudder to think of using Marketing Mix Modeling due to its reputation as an expensive tool to build that is slow to show results. However, the setup and capabilities of Marketing Mix Modeling have changed significantly in recent years, rendering these concerns moot.
Today, marketers can partner with analytics experts or use open source tools like Meta’s Project Robyn to build models of different sizes and customize them based on specific KPIs. And, with modern computing power, models can run on a near-constant basis, producing fast, actionable results. As a result, Marketing Mix Modeling today is both reliable and attainable for brands of all sizes.
The advent of machine learning algorithms introduced new capabilities to Marketing Mix Modeling. For example, integrating the Bayesian Belief Network advanced learning technique into models produces insights into cross-channel synergies previously thought to be unattainable with Marketing Mix Modeling.
Likewise, using AI to automate data input not only frees up manpower, but also ensures models are constantly running the most refreshed data available. In a previous study, Accenture found that integrating AI to marketing practices increases ROI by 30%.
While there are endless possibilities, one thing is clear: Marketing Mix Modeling can continue to evolve to meet marketers’ measurement needs, even as those needs continue to change.
A Model for the Future
Marketing Mix Modeling is here to stay – and for brands who want continued access to marketing insights, it’s time to begin adoption. Setting up Marketing Mix Modeling now, before the doors to individual-level data close, will help prepare brands for the changes to online privacy ahead.
Read the reports to find out how all types of brands can leverage Marketing Mixed Modelling to drive marketing success.