Marketing Mix Modeling (MMM) has some history. It’s been used by companies in CPG and consumer goods verticals for decades and was, before the digital age, one of the few tools available for holistic marketing measurement with financial guidance. Attribution modeling, on the other hand, sprang up with the advent of the internet and digital marketing. It has been the preferred technique for digital-first verticals such as e-commerce where most or all of the customer journey takes place online.  Through cookies, the IDFA, pixel tracking and other user-level tools, it was able to provide a very granular view of which marketing channels delivered the most conversion, across the final steps of the purchase journey anyways. MMM and attribution have co-existed, though the choice of which tool to use for marketing measurement was primarily a question of the industry you were in, which drove the data you had to work with and most likely your firm’s familiarity and acceptance for either MMM or MTA tools.

But things are changing. Although data sources and marketing channels are only proliferating, access to the granular user-level data required by MTA is being eliminated. Changes in privacy regulations, corporate policies and other factors will force brands that have relied on MTA to do something different:


GDPR and Privacy Regulations

GDPR (General Data Protection Regulation), came into effect in the EU a few years ago and similar regulations also exist in parts of the US and the rest of the world. These have impacted data privacy practices globally, with companies significantly tightening their rules around ad serving and user-level tracking. Large digital platforms, through which a majority of the world’s digital marketing spends are channelled, have increasingly adopted a “walled garden” approach that only supports analysis within a specific platform. Cross-platform and channel tracking is no longer possible or supported.


Google Phasing Out Third-Party Cookies on Chrome

Following similar moves by Safari and Firefox, Google announced in early 2020 that it will end support for third-party cookies, which fuel much of the digital advertising ecosystem, in its Chrome browser by early 2022. The company also recently clarified that once it ends support for cookies, it will not use other ways to track users around the internet, and would use only privacy-preserving technologies that rely on methods like anonymization or aggregation of data. This will be a further impediment for advertisers to deliver user-level targeted ads.


Apple Restrictions on IDFA on iOS Devices

Perhaps the most noteworthy recent change was Apple’s Ad Tracking Transparency policy (ATT) which cut off access to the Identifier for Advertisers (IDFA) system. IDFA allowed apps and advertisers to track activity across different internet domains, back to a specific user’s iOS device. With the release of iOS 14.5 that system has now been turned off, with Apple offering a lower resolution attribution system called SKAdNetwork. The final impact of these changes remains to be seen, but its already clear that user-level tracking and ID-driven attribution analysis will be severely restricted versus the past. Advertisers will need another way to measure their marketing effectiveness in the Apple ecosystem.


The core value proposition of attribution has always been its ability to link individual ad serving with individual conversions. With sharing of user-level data becoming more constrained and regulated, attribution simply won’t be able to offer the same coverage, or the same strategic cross-channel view of marketing spend effectiveness.

Marketing Mix Modeling (MMM) has traditionally been seen as a technique for more traditional brands, with a higher proportion of offline spend. But even digital advertisers can significantly benefit from the wider view that MMM delivers, especially now that the data sources underpinning MTA are disappearing and having some kind of alternative approach is essentially mandatory. What it lacks in terms of a granular, microscope view of user-level insights vis-à-vis attribution is more than compensated by the holistic, telescope view it offers advertisers, allowing them to optimize marketing allocations across various digital and offline channels and maximize marketing ROI.

With solutions now available to run MMM in-house and on-demand, it has the additional advantage of being privacy-friendly for brands that prefer to avoid sharing sensitive data externally with third-party analytics providers. It also avoids the time and expense required to work with third parties on MMM. These limits have traditionally kept MMM confined to annual updates, for only the largest brands. But having the option of running one’s own MMM analyses in-house, using existing systems, data, and data science resources, and on your planning timeline, removes those hurdles and adds strategically essential analytic agility.


Demand DriversTM from Analytic Edge is a future-ready solution that gives brands the ability to run MMM and other measurement analytics in-house, using an automated and integrated process. It offers marketers the cost, scale and speed advantages they need, and provides an alternative to both attribution modeling and traditional MMM approaches. Demand Drivers is already used by a number of global brands across traditional and digital-first verticals to get a holistic view of the effectiveness of their marketing investments and make faster and more responsive marketing decisions.

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