Media Measurement in an Evolving Advertising Ecosystem

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A new article on ADWEEK says that if you’re currently concerned about impending browser policies that could limit cookies, you’re not alone.

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As businesses evolve to meet consumer demands for more privacy, the fundamentals of media measurement are shifting, too. Marketers are still coming to terms with how the potential for cookie loss will affect their businesses in the coming years. In search of solutions, global-reaching brands such as ASUS and Resident have added a time-tested strategy to their measurement toolkits: marketing mix modeling (MMM). Thought to be too involved, too slow or matter only for TV advertisers, MMM has proved highly resilient in the modern era due to its reliance on privacy-friendly, aggregate data to measure sales impact and marketing ROI.

MMM can be used to inform cross-channel budgeting decisions, measure the holistic impact of media and support strategic and tactical optimization across platforms. Before diving into implementation, let’s debunk common misconceptions about MMM that hold some businesses back:

  • Myth: MMM is only for brand advertisers who want to measure TV: In the past, MMM was mostly used by businesses that didn’t have granular data. Now, it’s gaining traction with direct-response advertisers because it helps bridge gaps in existing measurement systems. Today’s MMM is more accessible. It’s most often used by companies who have a diverse media mix (with no one media channel over-indexed on spend), and up to a year of performance data to draw from.
  • Myth: We don’t need MMM because we already use multi-touch or last-click attribution: Most attribution models will be impacted by less access to third-party data due to browser policy changes and other shifts. It’s important to use multiple measurement systems so you get more reliable results. That’s where MMM comes in. It can be used to complement other attribution learnings, bring you closer to a true understanding of performance, support key budgeting and optimization decisions, and bolster scenario planning.
  • Myth: MMM is resource-intensive, and it takes too long: The research and methodology behind MMM have evolved significantly. New innovations have dramatically reduced resourcing and time requirements. Modeling insights can now be generated monthly, or more often if needed, based on just six-to-12 months of granular historical data.

How to implement MMM

There are two ways to go about implementing MMM: Do it yourself or work with a partner. Many businesses start with a DIY approach to MMM, but knowing where to start can be challenging. There’s also a world of partner options available to meet your business’ modeling needs–from self-serve SaaS platforms to full-service models that support data collection, interpretation and recommendations.

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