Original LinkedIn post
📈 Search volume for "Marketing Mix Modeling" (MMM) has increased 3x since 2021.
This is just one datapoint capturing the recently increased interest towards MMM-based marketing ROI measurement. Why is this happening? My view:
1️⃣ Google and Meta have started driving MMM's adoption
Visible examples:
- Launch of open-source modeling libraries (Meta's Robyn, Google's Meridian).
- Creation of measurement guides featuring MMM (e.g., Google's Modern Measurement Playbook by Brenton Lewis & team).
- MMM on stage in conferences (e.g., recent Meta Measurement Forum by David Choe & team).
2️⃣ Incrementality has become a popular and broadly accepted concept
By using incrementality tests and/or MMM, more and more marketers are questioning whether their current KPIs reflect incrementality or not. For example, Ad platform -reported conversions do not reflect true incrementality. They include conversions that would occur anyway, and conversions that are driven by other media.
3️⃣ Privacy trend is negatively affecting attribution-based measurement solutions
The rise of website consent forms has been decreasing Google Analytics 4's ability to track online users' behaviour. Multi-touch attribution solutions, on the other hand, are adversely affected by shorter cookie expiration times and the upcoming deprecation of 3rd party cookies in Chrome.
4️⃣ MMM can be used in more tactical use-cases due to more granular results
Examples: Performance marketing teams at ecom companies can use MMM to measure ROI of Ad platform-level campaigns. Omnichannel retailers can use MMM to plan cross-channel marketing campaigns.
5️⃣ It's easier than ever to take MMM into use due to data connectors
Data connectors from companies like Supermetrics have made MMM onboarding fast and easy, especially for ecoms and other digital-heavy advertisers. Digital data can be onboarded within hours.