The future of cross-media measurement takes shape with AI and industrywide efforts

In a little-publicized announcement, Google said it plans to roll out its Meridian marketing mix modeling (MMM) solution globally in early 2025 — with access to Search and YouTube data. In a highly competitive media landscape where walled gardens typically guard their data, even Google seems willing to play ball and cater to advertisers’ need for holistic and transparent measurement.

Advancements brought on by more capable AI technology and machine learning models are already bringing MMM back in vogue for marketers, some experts tell The Current. The channels that prove their worth in this MMM-led future will likely thrive more than others, they say.

But it isn’t just Google who’s jumping on the cross-media measurement bandwagon.

Last month, a report revealed a proposal to incorporate data from the U.K.’s Barb, which tracks TV and video audiences, into Origin, the cross-media measurement tool created by the Incorporated Society of British Advertisers (ISBA).

The news follows rollouts of cross-media measurement platforms by some of the world’s leading industry trade bodies, which are attempting to deliver on advertisers’ need for unduplicated, holistic and unified measurement in a fragmented media landscape.

The future of measurement is changing thanks to two major drivers. First, technology is reviving traditional measurement techniques (like MMM) for the modern age. Second, industry trade bodies are on a mission to corral industry players into opening up their data and delivering holistic measurement solutions.

AI is giving new life to MMM

Google’s Meridian and Meta’s Robyn may be high-profile examples of MMM’s revival, but some firms are working on making traditionally resource- and cost-intensive MMMs accessible to advertisers beyond just the world’s top brands.

With AI improving MMM’s handling of complex data sources and its speed of modeling, MMM has become more attractive. “AI tools which unify brand equity, creative quality, and sales data mean marketers can balance the immediate and long-term impacts of their investments, linking spend with sales KPIs at the most granular level,” says Matt Dodd, head of analytics at Kantar.

Historically, MMM gave marketers channel-level insights more suited to the annual planning process. That lack of granularity came with limitations, especially as audiences fragmented and the speed of marketing planning and execution tried to keep up with social media.

So marketers turned to multi-touch attribution (MTA). “MTA was tied to cookies that you could use to stitch together user paths for how they’ve interacted with media and whether they bought. There were a lot of problems with that, because cookies just break down,” says Russell Nuzzo, managing partner and head of new client solutions and partnerships at Gain Theory.

Now the digitalization of marketing channels — whether it’s connected TV, digital out-of-home or audio — is giving marketers large amounts of granular data from previously scant sources.

MMM is thus seeing a resurgence, says Nuzzo: “You get a very, very large data set, and it still lets you mine all the dimensions that you would want from MTA. And it’s just more robust.” MMM today leans on these much larger sets of data together with tried-and-true “econometric-style techniques,” he adds.

With AI making its way across marketing organizations, new algorithms are also helping marketers make sense of all this data. Michael Kaminsky, co-founder at Recast, says Markov Chain Monte Carlo sampling algorithms, for example, “allow modelers to specify and train much more complex models than what was possible in the past, going from models with hundreds of parameters to tens of thousands.”

As AI enables MMM to ingest a lot more data, it is also making the analysis and modeling process faster. “AI is going to help a lot with making the process more ‘read and react,’” says Nuzzo. “If you have data with constant flows, instead of modeling periodically, maybe you’re always modeling, and you’re always moving little bits of dollars here and there, and trying to optimize daily.”

Joint efforts

Technology isn’t the only dimension of measurement that is evolving. In recent months, industry trade bodies have introduced cross-media measurement solutions like Aquila, Halo, and Origin, spearheaded by the Association of National Advertisers (ANA), the World Federation of Advertisers, and ISBA, respectively.

These solutions point to industrywide collaboration as a key element in giving marketers confidence in cross-media measurement solutions. Even tech giants Google, Meta, Amazon and TikTok are all backing ANA’s Aquila, for example.

“The issue is not necessarily linked to a lack of signal but more to a lack of unified signals,” Sue Haas, interim president and CEO at Numeris, tells The Current. “Understanding one’s true use and movement between all media, platforms and devices can be extremely challenging in a highly fragmented media ecosystem.”

Still, since these efforts can take time to develop and gain adoption, some players in the advertising ecosystem aren’t waiting around.

Disney+ recently signed up for independent audience measurement in Europe through AudienceProject, a measurement company. Marketers will be able to measure ad campaigns on the streamer against those they are running on linear TV and connected TV, social media and the open internet.

“As streamers join third-party measurement companies, it helps unify online video behaviors and consumption to linear television viewing, which in turn provides the media industry a reliable, consistent, standardized, and neutral source of video audience data and metrics,” says Haas.