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By Jessika Ödling

For more than a decade, attribution has served as digital marketing’s north star. It told us where to spend, what was working, and who deserved the credit. Clicks became conversions. Conversions became KPIs. And for a while, that was enough.

But somewhere along the way, something shifted. Campaigns started performing better on paper than they did in terms of actual business outcomes. Performance marketers have been underserved, forced to use systems that measure what's easy, instead of what actually matters.

Attribution has long been the default for measuring marketing impact. But most models still rely almost entirely on clicks, ignoring view-throughs, post-purchase behavior, or upper-funnel influence. That means brands end up optimizing for the small percentage of users who actually click, while overlooking the broader drivers of demand. As tracking restrictions tighten and session data becomes increasingly unreliable, it’s getting harder to trust what those models are really telling us.

Most attribution frameworks today offer an oversimplified, often skewed version of the story based on incomplete signals. In a reality where budgets are under pressure and every channel, now more than ever, has the incentive to prove results, that’s no longer enough. To understand what actually drives value, brands need more than an oversimplification of interactions. They need a way to isolate effect. That’s where incrementality comes in.

Attribution systems are designed to track interactions and assign credit. That makes sense in theory, and for a long time, it worked well enough. But as customer journeys became more fragmented, and as platforms began to report their own versions of the truth, the cracks started to show.

If a customer saw three ads, opened an email, visited your site twice, and finally purchased through a branded search, which touchpoint(s) should get credit for it? Ask your attribution model, and it will answer. Ask your finance team, and they’ll likely raise an eyebrow.

Incrementality testing offers something attribution can’t: a causal framework. Rather than tracking paths and assigning credit, incrementality isolates the true lift of a marketing activity. It does this by comparing a group of users exposed to a campaign with a statistically similar group that wasn’t. The difference in outcomes, whether that be conversions, revenue, or the number of new customers, is the incremental effect.

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Learn more.

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