One of the hottest trends in advertising effectiveness measurement, especially with privacy concerns killing user-level online tracking, is geographic incrementality experiments. These experiments are cost-effective, straightforward and reliable, if done right.
Geo media experiments typically use large marketing areas, such as Nielsen’s Designated Market Areas (DMAs). Unlike traditional matched market testing, this modern approach involves randomizing DMAs, ideally all 210, into test and control groups. This way, advertisers with first-party data can measure true sales lift in house without external services. For those lacking in-house sales data, third-party panels, such as those from Circana and NielsenIQ, offer alternatives compatible with this kind of test design.
High-quality, randomized controlled trials (RCTs) – akin to clinical trials in medicine – are the best source of evidence of cause-and-effect relationships, including advertising’s impact on sales.
Statistical models, including synthetic users, artificial intelligence, machine learning, attribution, all manner of quasi-experiments and other observational methods are faster, more expensive and less transparent forms of correlation – not measurement of causation. They may be effective for audience targeting, but they are not for quantifying ROI.
Imagine, however, the potential for conducting geo experiments using ZIP codes instead of DMAs.
Targeting with ZIP codes
An advantage to DMAs is that they are universally compatible with all media types. ZIP codes, on the other hand, pose challenges to experiments in digital media. Targeting with ZIP codes online often relies on inference from IP addresses, which is unreliable and increasingly privacy-challenged. Geo-location signals from mobile devices also contribute ZIP codes to user profiles, which is bad for experiments, as a single device/account can be tagged with multiple ZIP codes based on where the user has recently visited.
A key to the reliability of this kind of geo experiment is ensuring that the ZIP codes used for randomized media exposures match the ZIP codes where audience members receive their bills, as recorded in company CRM databases. Each device and user should be targeted by only one ZIP code: their residential one.
To adopt this technique, media companies can take two transformative steps:
- Use primary ZIP code targeting:
Major players like Google and Meta already collect extensive user data, often appending multiple ZIP codes to a single device. For experiments, these companies should offer a “primary” zip code targeting option, based on the user’s profile or most frequently observed ZIP code for their devices. - Implement anonymous registration with ZIP codes:
Publishers should require registration to access most free content, offering an “anonymous” account type that doesn’t require an email address. Users would provide a username, password and home ZIP code, enabling publishers to enhance audience profiles while maintaining user anonymity.
These strategies would significantly improve ROI measurement, offering a more powerful and simpler mechanism than cookies or other current alternatives. Unlike cookies, which were always unreliable for measuring ROI, these methods provide a privacy-centric, fraud-resistant solution that doesn’t require complex data exchanges, clean rooms, tracking pixels or user IDs.
Industry bodies like the IAB, IAB Tech Lab, MMA, ANA, MSI and CIMM should advocate for this approach, which would revolutionize advertising incrementality measurement.
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With over 30,000 addressable ZIP codes compared to 210 DMAs, the potential for greater statistical power and more reliable ROI measurement is immense. As Randall Lewis, Senior Principal Economist at Amazon, told me, “the statistical power difference between user IDs and ZIP codes in intent-to-treat experiments can be small, with the right analysis methods.”
Adopting this approach would mark a significant leap forward, making high-quality experiments more accessible and reliable than ever before, ensuring a privacy-pure and fraud-proof approach to measuring advertising effectiveness.
“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
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