Imagine you’re shopping for laundry detergent. You click to buy your preferred brand, but when your order is fulfilled, the product is out of stock. What do you do?
Recent DoorDash data shows that shoppers in this scenario buy a competing brand nearly 12% of the time, while more than 40% of the time they request a refund instead. In either case, the preferred brand just lost an easy opportunity to convert on strong demand.
This isn’t a niche problem. Inventory distortion from out-of-stocks and overstocks costs the retail industry an estimated $1.7 trillion each year, according to IHL Group. That makes capturing that demand a huge growth opportunity for brands.
The challenge here is that most commerce media platforms are great at measuring advertising performance but don’t fully understand the conditions under which advertising is trying to convert. That missing context is becoming increasingly consequential as brands invest more heavily in commerce media.
Conversion isn’t just a media outcome
Commerce media’s greatest strength is proximity to the transaction. Advertisers can connect media exposure to purchases and measure impact against verified sales.
But many other factors can derail conversion. A product might be missing from the retailer’s digital assortment, or the listing might lack a title, image or product detail to give the shopper confidence. In each case, the brand doesn’t have a full view of the lost opportunity.
Conventional advertising metrics can tell us what happened (click/no click, sale/no sale, etc.), but often lack the context to explain why or why not, as the case may be. And if marketers don’t understand why something happened, it’s easy to optimize for the wrong variable.
Bad diagnosis leads to bad optimization
When a campaign delivers strong engagement but disappointing sales or category share, the natural response is to adjust the media strategy by changing the targeting or creative or shifting spending toward other products or retailers. But what if the campaign was reaching the right consumers and generating genuine purchase intent only to encounter one of the availability problems discussed above?
In that case, shifting budget away from the “underperforming” product or retailer actually means walking away from demonstrated consumer demand.
The same applies to substitution behavior. When shoppers can’t get their preferred product, do they choose another product within the brand’s portfolio, switch to a competitor or abandon the purchase altogether?
Each of those actions merits a distinct strategic response. A product that consistently loses shoppers to competitors during stockouts might warrant greater attention from sales and supply-chain teams. A product with strong demand but poor digital visibility might need better catalog execution to maximize advertising outcomes.
The takeaway is that advertising amplifies demand and content drives conversion – but growth is delivered on the shelf. And without visibility into that full order journey, it’s easy to pour money into fixing a problem that doesn’t exist.
Breaking down data silos to drive growth
These gaps in contextual understanding stem from a familiar problem in ad tech: fragmented workflows and siloed data. Marketing teams manage campaign strategy, delivery and performance. Ecommerce oversees product listings and content. Sales and category teams manage retailer relationships and assortment. Supply-chain teams focus on inventory and availability.
Organizations haven’t connected the data in a way that helps them understand if and where demand hits a snag along the buying journey. For decades, point of sale and syndicated sales data has helped CPGs understand what sold in stores. But sales data alone rarely explains why a product didn’t sell. Was it out of stock? Was the shopper’s preferred item or size unavailable? Did they choose another product or switch to a competitor when at the shelf?
But even when brands have inventory data, they can’t always keep up with what shoppers encounter. Shelves change by the hour and any data source – whether it’s a brand’s, a retailer’s or a periodic audit – is just a snapshot.
Newer approaches are helping close that gap by connecting advertising data with real-world commerce signals. At DoorDash, for example, we’re evolving Ads Manager into Brand Center, bringing advertising performance together with on-shelf availability, assortment insights and consumer purchasing insights. By combining order data with in-store signals captured by Dashers, brands can see not only where sales are happening, but where demand may be going unfulfilled due to out-of-stocks and what shoppers choose instead when a product isn’t available.
This integrated visibility is helping different teams within an organization work from a shared understanding of what’s constraining growth so they can figure out who can actually do something about it.
Commerce media needs to explain why, not just what
Commerce media has rapidly become a core marketing channel because of how it’s made advertising more measurable and more optimizable. But to mature further, commerce media performance needs to be connected with the broader commercial context that determines whether demand becomes revenue.
That deeper contextual visibility brings a new set of questions that lead to critical insights, including, “Which products are generating demand that isn’t being fulfilled?” and “Where are availability or assortment gaps suppressing sales?”
Marketers don’t have to become inventory managers or supply-chain experts. But they should have enough visibility to recognize when a disappointing campaign result isn’t actually an advertising problem. Because a commerce media program with impressive advertising metrics isn’t worth much if it’s not driving real revenue.
Sometimes, the best way to improve advertising performance has nothing to do with the ad itself.
