Google Ads · Case study
Restructuring Google Shopping for Revenue Growth at Target ROAS
Shopping campaign restructure with feed optimisation and margin-led bidding, driving revenue growth at target ROAS.
Retail, Shopping ads · run personally by Hasnain Jameel
The situation
What was actually going wrong
Shopping campaigns were running on a feed that had never been properly audited. Titles were manufacturer defaults, key attributes were missing, and every product sat in one campaign with a single ROAS target regardless of margin. High-margin products and loss leaders were being bid on identically.
The work
What I changed
Feed quality fixed first
Titles were rewritten to match how buyers actually search, missing attributes were completed and disapprovals cleared. Feed quality is the highest-leverage and least glamorous work in Shopping.
Campaigns split by margin and intent
Products were segmented so that high-margin lines could bid aggressively while thin-margin items were capped. A single blended ROAS target across a whole catalogue always overspends somewhere and underspends somewhere else.
Search terms controlled at query level
Negative structures were added so that generic browsing queries stopped absorbing budget that belonged to purchase-intent searches.
Performance Max given proper signals
Rather than running Performance Max as a black box, audience signals and asset groups were structured deliberately, with reporting configured to reveal what it was actually doing.
The outcome
What it produced
Most Shopping accounts do not have a bidding problem. They have a feed problem wearing a bidding problem’s clothes.
Straight answers
Questions about this work
It is the single biggest determinant of Shopping performance. The feed decides which searches you appear for, which no bid adjustment can override.
No. Margin varies across a catalogue, so a blended target guarantees you are either leaving money on high-margin products or losing it on thin ones.
Yes, when it is fed clean conversion data and structured deliberately. Run as an unstructured catch-all, it absorbs budget and tells you very little.
Measurement & limitations
Results are as reported by the ad platforms, analytics or the client at the time and have not been independently audited. They are attributed results, not controlled incrementality tests, unless stated otherwise. Outcomes depend on the business, market and budget, and past results do not guarantee future performance.
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