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Betting on Commerce Media? Then Don’t Model Your Data

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I’m a lawyer, and I’m also a doctor. I’m in Gen Z, yet I’m 45. I like dirt bikes and also the opera. This sounds like the setup to some riddle. In fact, it’s what appeared when I searched myself in an online registry of the audience segments data providers thought I was part of. For the record, I am none of these things, nor am I in many of the 400 other segments I was seemingly randomly assigned to.  

That was several years ago, but things haven’t improved much. If anything, dubious inflation of datasets using lookalikes and modeling has become more normalized in our industry, corrupting the quality and accuracy of much of the data sold. 

The results of this are clear. According to Truthset.io, 39% of gender targeting budget is misspent and 73% of ads targeting African Americans are wasted. That’s a colossal failure.

Bad Data Persists Because it’s Incentivized

My team and I have consistently found that there are very few data providers that actually have accurate deterministic data. Techniques like modeling persist because, when it comes to marketing datasets, a simple axiom still holds sway: bigger is better. There are incentives on all sides to inflate the size of audience datasets and achieve the biggest reach, despite this obviously weakening ROAS. 

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This presents both a challenge and an opportunity for commerce media programs. The superior performance of commerce media inventory has propelled it into the fastest-growing advertising segment, with McKinsey predicting it will surpass $100 billion in revenue as soon as 2026. Just a glance at the rise of retail media, for example, proves how this branch of ad inventory sets new standards for performance. 

Unfortunately, old mindsets are hard to shake. As more companies look to launch commerce media networks, a new player to the space might be inclined to mix third-party and first-party data to achieve the traditional aim of extended reach.  

But that line should not be crossed, because it is contradictory to the entire proposition of commerce media – and sacrificing quality for quantity will eventually be detrimental to the entire industry’s growth. 

Commerce Media Data is Like Fine Wine – Don’t Dilute It

Commerce media’s value proposition rests on its strong first-party data and proximity to the user’s point of purchase. The data pools may seem comparatively limited, but their high-quality data ensures greater accuracy and effectiveness, especially in a closed-loop system. This means campaigns perform so well that marketers don’t need to spray and pray using big, dubious datasets. 

Commerce media networks should focus on delivering tools that enhance the experience for advertisers and consumers — for instance, by making it easy for buyers to access this data in demand-side platforms, where advertisers can use it along with other commerce media data to achieve the scale they need.

Now that advertisers have discovered the performance and attribution benefits of commerce media, as an industry we need to ensure we protect what brought them to commerce media in the first place: strong first-party data coupled with purchasing intent. There is room enough in the industry for players to develop unique data monetization strategies without resorting to dubious modeling practices that will ultimately sabotage the momentum commerce media has fought hard to build.


Harsh Jiandani is the Chief Commercial Officer at Koddi, a retail and commerce media technology for enterprises. Jiandani has more than a decade of industry leadership experience, including roles at Microsoft’s PromoteIQ, and Xandr (formally AppNexus), where he successfully navigated one of AT&T’s largest Ad Tech acquisitions. He holds an MBA from the NYU Stern School of Business.

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