Today’s marketers are under immense pressure to understand the true business impact of every marketing dollar across their entire media portfolio, all while navigating outdated measurement models in a privacy-first world.
56% of marketers report that they struggle with measuring the ROI of their marketing campaigns due to the limitations of traditional measurement approaches (Harvard Business Review) – and a surprising 72% of marketers say their inability to connect marketing activities to results is one of their biggest roadblocks (Forrester).
The challenge is that most measurement solutions were built on pre-privacy, user-level attribution models. But as click-based systems have fallen apart in recent years, Media Mix Modeling (MMM) has seen a renaissance among marketers as a valuable – albeit antiquated – tool for understanding media performance.
While traditional MMM is a powerful method for measuring media performance and companies that use advanced analytics such as MMM are 33% more likely to outperform their peers in terms of revenue growth and profitability – MMM has significant limitations.
Old-School Media Mix Modeling Isn’t Built for the Future
MMM’s main benefit is its ability to measure all business drivers for which historical data is available, including offline channels and non-addressable media, in addition to non-media factors.
It also can account for longer-term impacts of media on sales as well as the diminishing relationship between spend and ROI for a given media tactic. But MMM is an incomplete measurement solution in isolation.
MMM’s most obvious drawback is inefficient data collection across many disparate data sources. This is often a time and labor intensive process that can take months (even years in some cases) to complete before the first version of a model can be released.
Traditional MMM also leverages a correlation-based approach, making it impossible to differentiate between mere correlation and actual causation. This limitation can lead to inaccurate conclusions about which marketing activities truly drive sales in today’s fast-moving, multi-channel landscape.
A New Role for Media Mix Modeling
While MMM on its own isn’t enough to deliver reliable portfolio optimization, by integrating it with incrementality testing and platform data, this triangulated media measurement approach transforms traditional MMM from a limited, correlation-driven tool into a powerful, actionable solution.
Incrementality testing delivers causal insights, platform data provides granular reporting, and MMM ties it all together to reveal the full impact of a brand’s media mix.
This approach empowers marketing teams to automatically calibrate MMM results with incrementality test results and platform attribution data. It eliminates the need to stitch results together manually and makes it easy to holistically assess impact all in one place.
By triangulating these methods, marketers gain access to accurate, timely, and actionable insights that drive better business performance, boost sales, and ensure every dollar is working as hard as it should.
These insights allow marketers to prove the value of their investments and confidently reallocate spend to maximize ROI. Without this capability, brands miss the opportunity to optimize inefficient ad dollars and build a more profitable, revenue-driving funnel.
For a deeper dive into MMM of the past and its new role as a key driver of calibrated media measurement and optimization, check out our guide: The Future of Media Mix Modeling: How incrementality testing is redefining MMM for a new generation of marketers.