Today, most companies rely on a correlative approach to measurement called “post-exposure attribution”: someone sees an ad and then they do something like make a purchase, and the company assumes they did that action because they saw that ad. That’s not always the case.

Despite its inaccuracies, the ad-tech industry is deeply entrenched. Marketers have grown accustomed to its self-fulfilling prophecy, wherein conversion-optimized ads are tailored to individuals predisposed to certain actions, leading to inflated campaign performance metrics. This cycle is exceedingly beneficial to ad platforms as it obfuscates the true performance realized by advertisers.

Recent privacy changes have complicated ad measurement by limiting access to granular user-level data, which makes post-exposure even worse. Platforms now struggle to accurately connect conversions to ad exposures, and have resorted to modeling and estimating such links, bringing additional uncertainties into the measurement process.

Why This Matters

Inaccurate measurement has significant economic ramifications, with at least 23% of the $88 billion spent annually on automated digital ad buying wasted due to misallocated budgets, though this is likely just the tip of the iceberg. Some campaigns are allocated excessive budgets, falsely believed to be performing well, while others are underfunded despite stellar performance that is not recognized. This disparity in resource allocation can result in missed opportunities, potential revenue losses and a skewed perspective on campaign effectiveness.

The Path Forward

Businesses need to navigate the flawed landscape of ad measurement by exploring alternative approaches and embracing best practices like incrementality testing.Some businesses are reverting to traditional measurement methods like media or marketing mix modeling. Others are experimenting with aggregate-style measurements such as geotesting which also have more sophisticated, modern manifestations. Sadly such alternatives aren’t widely enough adopted yet.

Marketers must relinquish their fixation on granular data and embrace the randomly determined nature of causal, scientific ad measurement. They must also approach platform-powered ad optimization and measurement with healthy skepticism. By deploying learning agendas powered by scientific experimentation, advertisers can discover the combination of campaign parameters that yield true incrementality.

The need for accurate ad measurement cannot be overstated. From misallocated budgets to potential revenue loss, the economic consequences of flawed measurement are significant, impacting businesses of all sizes. Most notably, small and medium-sized businesses lacking the financial buffer of larger enterprises bear the brunt of these inefficiencies, risking financial setbacks and hindering their growth potential.

When you get the measurement of ads right, everything flows more efficiently, people buy more goods and companies spend more on advertising because they are more confident in their decision-making and ROI. It’s a virtuous cycle all based on measurement.