With winning the battle for consumer attention being more challenging than ever, it’s important for marketers to understand which of their advertising is succeeding, and why? Without those insights, ad buyers are doomed to settle for sub-standard business outcomes and hazy reporting on what drove their key performance indicators. A sharp focus on the right kind of measurement and attribution is fundamental to succeeding in the evolving landscape. Over the course of this three-part series, we will examine the most commonly-used measurement and attribution approaches, as well as newer models that are on the forefront of marketing science.
Today, we’ll begin with an exploration into incremental lift as a causal approach to measuring advertising effectiveness.
Let’s briefly look at a common attribution method in use today, known as “last touch” conversion tracking. In this modality, which is in wide use across digital advertising, the KPI is typically one of cost per acquisition (CPA), cost per site visit (CPSV) or return on ad spend (ROAS.) Indeed, these all seem like worthwhile things to measure.
The problem is that these metrics report on the correlation between media exposure and the marketer’s business goals, but provide little-to-no insight on causation. This means that buyers are optimizing their media based upon a metric that doesn’t separate “true” conversions from background noise: it’s not really useful to measure conversions that were going to happen regardless of the ad campaign.
Marketers should understand how many users saw the ad and converted because of it, and last-touch fails at doing this.
With last-touch, you’re really measuring targeting precision, not so much ad effectiveness. Because this method awards attribution “credit” to the media property that displayed the ad immediately prior to the user converting, you’re validating that you’ve reached the correct audience, but did that reach stimulate the conversion?
Let’s imagine that a consumer has decided to purchase a new pair of their favorite sneakers next weekend. On Wednesday, you serve them an ad for those sneakers, and on Friday the consumer makes the purchase. Sure, that impression succeeded in reaching the right segment of users, so the targeting was spot-on, but do we really think that the ad exposure drove the purchase? Of course not; the consumer had already made up their mind to purchase the sneakers before they saw the ad.
Measuring the actual, causal media benefits of a campaign using an incremental lift approach allows marketers to home in on the actual real-world impact that their marketing has had.
Fixing the correlation/causation problem that is inherent to most legacy forms of attribution, incrementality measurement helps marketers understand lift across tactics.
But how to actually measure that lift? There are many different approaches and they each have their pros and cons. Next time, we’ll look at several of these, and see if we can identify one that makes the most sense for your business goals.
It’s difficult to change well-worn habits. Indeed, it will take time for some marketers to migrate from older, imprecise measures to an always-on incremental lift model. The benefits far outweigh the temporary uncertainty that changing methodologies can introduce, and with targeting and attribution soon to be shaken up by industry-wide changes, now is the ideal time for ad buyers to build a firmer grasp on how their spending is driving results.
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