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Metrics type: Key Metrics • Category: Ad Platform

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At a glance

Cross-platform alert for spend on a Microsoft Shopping campaign whose product feed has live rejections. When offers in a feed are disapproved, the campaign serves fewer products with a degraded bid signal, so every pound you spend competes on a thinner, weaker catalogue. The card joins your Microsoft Shopping spend against the rejection status of the linked feed, including feeds managed through a feed tool such as Feedonomics, so you can see the cost of leaving rejections unfixed. The fix is to clear the rejections, after which the full catalogue serves and the bid signal recovers.

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Calculation

Calculated automatically from your Microsoft Ads (Bing) Shopping data joined to your product feed’s rejection status. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading.

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Worked example

A UK outdoor-gear brand runs three Microsoft Shopping campaigns fed by a Feedonomics catalogue. The card reads on 16 Jun 26, trailing 30 days. The card flags £2,740 of spend running against feeds with open rejections. Reading it:
  1. The footwear feed is the urgent one. With 140 of 620 offers rejected, nearly a quarter of the catalogue is dark, yet the campaign still spent £2,100. That budget concentrated on the offers that survived, which are not necessarily the best sellers.
  2. GTIN errors are a fixable, high-impact rejection type. Missing or invalid product identifiers are a common Feedonomics-stage fix; once corrected and re-reviewed, the rejected offers return and the campaign serves the full range again.
  3. The clearance price mismatch is a classic feed-vs-store drift. The feed price and the landing-page price disagree, so Microsoft disapproves the offer to protect shoppers. Sync the feed to live pricing and the rejections clear.
  4. The bid signal is degraded, not just the coverage. Because Smart Shopping optimises across whatever serves, a quarter of the footwear catalogue being dark means the algorithm has been learning on a skewed subset. Expect a short re-stabilisation once the rejections clear.
The recoverable value here is twofold: the spend stops being wasted on a thin catalogue, and the campaign’s optimisation gets the full product set back. Feed rejections are one of the highest-leverage Shopping fixes because clearing them restores both coverage and signal at once.

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Sibling cards merchants should reference together

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Reconciling against Microsoft Advertising

Where to look in Microsoft Advertising: Microsoft Merchant Center → Catalogs, where the offer status column lists approved, pending, and disapproved offers with the rejection reason. Cross-reference the disapproved count against the Shopping campaign spend under Microsoft Advertising → Campaigns. The campaign keeps spending on its approved offers even while others are rejected. Why Microsoft cannot show this on its own:
  • Merchant Center shows rejection status; the campaign report shows spend; the two are not joined into a single “spend at risk” figure inside the Microsoft UI.
  • When the feed is managed by a tool such as Feedonomics, the rejection often originates upstream and is best fixed there, but Microsoft only shows the downstream disapproval.
Why our number may legitimately differ from a manual check: Cross-connector reconciliation:

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Known limitations / merchant FAQs

My campaign is still spending, so are the rejections really a problem? Yes. The campaign spends on whatever offers survived review, but a meaningful share of your catalogue is dark, so the budget concentrates on a subset that may not be your best sellers. Worse, Smart Shopping optimises across the live catalogue, so a quarter of your products being rejected skews the algorithm’s learning. You are spending fully into a partially broken catalogue. The rejections are managed in Feedonomics, not Microsoft. Where do I fix them? Usually upstream in the feed tool, because that is where the offer data is assembled. Common fixes are correcting GTINs and product identifiers, aligning feed prices with live store prices, and supplying required attributes. Once corrected, the feed pushes to Microsoft and the offers re-review. Microsoft only shows the downstream disapproval, so fixing it in the Microsoft UI alone often does not stick. How fast do offers come back after I fix a rejection? After the corrected feed reaches Microsoft, offers re-review on Microsoft’s schedule rather than instantly. Expect a short delay, then the offers return to serving. Because Smart Shopping was optimising on a reduced catalogue, allow a brief re-stabilisation period once the full range is live again. Why does ROAS sometimes dip right after I fix the rejections? Because the campaign suddenly has its full catalogue back and the bidding algorithm has to relearn across the restored offers. This is a short, expected adjustment, not a sign the fix was wrong. ROAS should recover and usually improve once optimisation re-stabilises on the complete product set. Are all rejection types equally urgent? No. Identifier errors such as missing GTINs and price mismatches against the landing page are high-impact and usually quick to fix, so they are top priority. Policy rejections may need a product or content change and take longer. Sort by spend and rejection share to fix the costliest, most-fixable feeds first. The same rejections appear on Google Shopping. Is that the same issue? Almost always, because both platforms typically read the same source feed. The exact rejected set can differ because Google and Microsoft apply slightly different policies, but the root cause, the feed data, is shared. Fix it once at the feed level and both platforms benefit.

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Tracked live in Vortex IQ Nerve Centre

Spend on Campaigns with Active Feed Rejections is one of hundreds of KPI pulses Vortex IQ tracks across Microsoft Ads (Bing) and 70+ other ecommerce connectors. Nerve Centre runs the detection layer; Vortex Mind investigates the cause when something moves; Ask Viq lets you interrogate any number in plain English. Start for free or book a demo to see this metric running on your own data.