Marketing Measurement And Attribution In 2023

Among the advertising shifts seen in the past few years, including across social and search and display marketing, the farthest-reaching may be those due to changes in attribution and measurement. And as sustainable paid media scale has become harder to achieve in certain channels, brands must improve their ability to identify the advertising vehicles that drive contribution, not just attribution. This means they need increased visibility into the drivers of their revenue that’s not only incremental but also maintains profit margins. But this likely will happen on an aggregated basis, rather at an individual-user level.

“With less visibility into attributable conversions, brands are moving away from looking at platform attribution as their true north and instead focusing on more holistic metrics like customer acquisition cost (CAC) and incrementality,” says Megan Conahan, EVP at Direct Agents, known for its digital marketing work with clients such as Sony and Walmart
WMT
. This is a complex undertaking given the ever-increasing number of platforms where consumers and brands spend their time and money, but moving beyond an expectation that every dollar will be attributable toward more holistic performance modeling is important for its feasibility and accuracy, she adds.

Ben Dutter, SVP of Strategy at Power Digital, which has worked with clients ranging from Uniqlo to Dropbox, talks about the four levers proven to drive first-time buyers – not just attributed revenue – as being creative, frequency, audience penetration, and duration. How can a brand find this type of incremental contribution? “The barrier to entry for media mix modeling (MMM) has lowered enough to make this form of measurement common and necessary… MMM is the best option you have when you lose tech-driven measurement, such as cookies.” MMM is especially useful for digital out-of-home (DOOH) advertising and television, as this top-of-funnel marketing can be challenging to track. But Dutter also provides an example of a smaller brand that may be working with influencers or on TikTok and lacking detailed visibility into user click data: By using statistical modeling to see how much first-time revenue came from that ad, decision-makers can better allocate budget to ensure the relationship between customer acquisition costs and the long-term value of those customers is positive on an aggregate basis.

The use of MMM to predict the performance impacts of budget or platform changes, and the deployment of machine learning to apply those model outputs to media strategies to achieve and maintain optimal spend distribution, is a tactic Conahan sees gaining interest from brands. “Overall, when you look at attribution and ad targeting, we’re getting away from the hyper-targeted approach where everything is trackable and attributable. Brands can no longer expect to target a niche within Meta that converts within the previously defined attribution window,” she says. Conahan goes on to explain that while specific media may have provided awareness, conversion, and attribution in the past, brands no longer should expect that and must look elsewhere to get everything they need.

On a platform-by-platform basis, the loss of signaling data due to changes in privacy legislation and Apple’s
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own privacy policies has helped catalyze a shift among digital marketers to MMM when they need to measure outcomes. Part of the value of MMM is its ability to expand beyond digital to all paid media, including trade promotions and retail deals. But while both Google
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and Meta have increased their MMM ability, brands may not want to share all of their external media spend data with these platforms in an effort to optimize results.

Affiliate networks, too, have improved their tracking and reporting capabilities, including real-time monitoring and analytics, to offer better insights into the performance of affiliate marketing campaigns. Ricci Massero, Marketing Manager at Intellek, says this allows brands to make real-time adjustments and that new technologies, such as cross-device tracking and cookieless tracking, have improved the accuracy of tracking while reducing the incidence of fraudulent activity.

Overall, Paul DeJarnatt, Digital Vice President of NOVUS, a media planning and buying agency that’s worked with Dollar Tree
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and LIDL, believes audience insights and understanding will overtake targeting and technology as third-party (3P) cookies depreciate and it becomes more challenging to conduct 3P lookalike targeting based on first-party (1P) customer data. While he acknowledges media buying systems are built to enable marketers to leverage data to drive targeting and personalization, he advises that using data exclusively is no longer efficient and that it’s crucial to analyze, and value, qualitative customer insights to to build ad strategy. DeJarnatt further argues the new way to find audiences will be through various companies’ device and audience graphs, which is one reason publishing companies are racing to build privacy-compliant, non-cookie-dependent data that can then be matched to advertisers’ 1P data in order to replicate that 3P scale and lookalike capability, globally and locally — but in a way where the consumer has authorized the use of their data.

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