The Ad Measurement Trends That Reshaped Online Advertising This Year

2024 was a year of hectic change for ad measurement.

Chrome may have deferred third-party cookies, maybe even a vague lifeline. However, user-level data is still drying up, to the point that last-click and multi-touch attribution are completely lost their sharpness.

Instead, data-driven advertisers have turned to familiar stalwarts like media mix modeling (MMM), as well as new techniques like incremental testing.

These are some of the new ad attribution trends and techniques that programmatic advertisers should know about in 2025.

Measurement of incrementality

This year, media buyers were gripped by the “curation” mania. But for ad measurement, 2024 was the year of the transition to “incrementality,” a difficult term to define.

“I really need a better answer to this question,” Olivia Kory, head of strategy at incrementality measurement startup Haus in AdExchanger conversations podcast this month, to the question of what incrementality measurement actually is.

She sums it up as a marketing measurement model that focuses on establishing causation rather than correlation.

Incremental measurement achieves this through the sophisticated use of retention groups and geotesting. One way to compare the incremental contribution of, say, Instagram or a large DOOH campaign is to run that campaign in some markets while those ads aren’t running at all in other, similar markets.

Earlier this year, a shoe salesman ASICS detailed for AdExchanger how it uses the Hub and Google’s data cleaning room, the Ads Data Hub, to create similar cities to test incremental ROI.

The advantage of geo-testing is that evaluates large cohorts people (the city of Chicago, say), rather than tracking known users for conversions or changes over time, said Habu’s head of customer success, Avanti Gade. This makes measuring incrementality a permanent strategy, while most user-level attributions disappear with new privacy standards.

But there are also downsides. First, large platforms don’t necessarily allow for incremental testing. Meta and Google do this, but Amazon doesn’t have a ready way for advertisers to test in one city while creating an opt-out group in another.

It’s also expensive: there are suppliers, testing costs and abandonment costs.

As ASICS global marketing manager Devin McGuire said, New York and Los Angeles were blocked from incremental testing because those cities are too important to the bottom line. I can’t spoil the core business with some advertising test.

But the costs might be negated if incrementality testing reveals a the company’s overall brand search budget it can be better spent elsewhere.

There’s only one way to find out, and that’s to test the idea.

Mix modeling

Call it vintage chic, because MMM is back.

Not so long ago, data-driven marketers would have scoffed at the idea of ​​a return to MMM. It’s an old-fashioned campaign measurement method built for TV, radio and print that requires month determine the results.

But with user-level data working on dry and walled gardens covering all ad demand, MMM becomes a viable way to attribute platforms as a whole, without insight into the platform itself.

The platforms heard the MMM requests and responded in 2024.

This year, Google launched Meridian, its open source MMM service. Meta already had one, her name was Robyn.

The next step for the category is to use data science and new first-party data tools to reduce the timeline for MMM from months to weeks, and hopefully even days, so results can feed into campaigns in real-time.

Another challenge will be to open MMM to more advertisers. It requires big budgets and data analysts, which used to be the responsibility of big TV advertisers. Ad tech vendors are trying to bring MMM to smaller digital or regional brands.

Tons of eCommerce metrics

The rise of digital-native brands and e-commerce shopping in general has brought created new metrics to measure ads which is likely to catch on with other advertisers, especially in retail media.

One big advantage is ACOS (“advertising as cost of sale”), which started as a way to attribute ads on Amazon.

The mathematical difference between ACOS and ROAS is negligible. However, ACOS requires a direct link to purchase data, as revenue generated by paid media is part of the equation.

But the main difference between ACOS and ROAS is philosophical. ACOS considers ad spending as a contribution to total sales. To measure ROAS, ads are the whole point.

Another boutique e-commerce metric could emerge.

At AdExchanger’s Programmatic IO this year in Las Vegas, Home Depot’s head of marketing measurement Zach Darkow said he wants internal metrics what the retailer calls “return on marketing objectives (or ROMO)” would catch on.

While measuring ROAS is a way to justify and stretch a specific ad budget, ROMO broadens the measurement lens to include factors like brand awareness or category share in stores. He also cited WISS, or “web-influenced store sales,” a short-lived metric in the Target Roundel.

Those informal metrics wouldn’t work with walled gardens that just report ROAS, Darkow acknowledged.

A common thread throughout the development of new attribution models and metrics is the challenge of educating and inspiring brand marketers to actually change their behavior, as one retailer told AdExchangerwhen the built-in ROAS platform “sits there like a feather bed.”