More revenue from the catalogue and budget already in place.
These cases show how large-catalogue ecommerce businesses generated more Google Ads revenue by aligning product opportunities, campaign decisions and budget:
- Capture customer demand the existing account was missing
- Give commercially valuable products stronger visibility and support
- Make more of the existing budget contribute to revenue growth
Case 01 · National irrigation retailer
Approximately $50,000 more Google Ads revenue a month, from a catalogue of more than 6,000 products.
The constraint
The account had accumulated hundreds of separate ads across individual product categories. It looked comprehensive, but the data was spread too thinly and campaign priorities were not consistently following the products creating the strongest commercial value.
What changed
The fragmented structure was consolidated so clearer product-performance patterns could emerge. Once the evidence was stronger, the best products and categories were separated into a small number of focused campaigns and given more deliberate support.
The result
Monthly Google Ads revenue increased from approximately $200,000 to $250,000 without increasing ad spend. That is approximately $50,000 more revenue a month, or approximately $600,000 a year at that monthly run rate.
Same catalogue. Same ad budget. Better structure and clearer product priorities.
Case 02 · National packing & industrial supplier
Approximately $200,000 more Google Ads revenue a month, after advertising decisions were connected to profitability.
The constraint
Google Ads revenue had stalled at approximately $400,000 a month. The account was managed against a blended return, even though different product categories contributed very different levels of profit to the business.
That made the range look more uniform than it was. Lower-margin categories could keep receiving budget because their revenue appeared acceptable inside the total account result.
What changed
Product and category performance was reviewed alongside the client's profit and loss information. The account was rebuilt around commercial contribution rather than revenue alone.
Budget moved toward the categories creating the strongest value for the business and away from areas absorbing spend without contributing enough.
The result
Monthly Google Ads revenue grew from approximately $400,000 to $600,000. That is approximately $200,000 more revenue a month, or approximately $2.4 million a year at that monthly run rate.
Budget followed commercial value, not the blended account average.
Case 03 · Automotive ecommerce retailer
Approximately $35,000 more Google Ads revenue a month, while return on ad spend improved from 13.5x to 14.3x.
The constraint
Google Ads was already profitable, but growth had stalled across roughly 20 campaigns using a mixture of older bidding approaches.
The ecommerce platform, pricing and promotions could not be changed. More revenue had to come from campaign structure, targeting, product coverage and budget decisions rather than a website rebuild or heavier discounting.
What changed
The account was consolidated around one Brand Search campaign and two Performance Max campaigns using value-based bidding and a consistent return target.
Product demand, search quality, feed quality and budget were managed together. Campaigns were tested and consolidated, then budgets increased in measured steps when the evidence supported it.
The result
Monthly Google Ads revenue grew from approximately $75,000 to $110,000. That is approximately $420,000 a year at that monthly run rate, with peak months exceeding $150,000.
Return on ad spend also improved from 13.5x to 14.3x as investment increased. The important result was that the business created substantially more revenue without changing its website, pricing or promotions.
More revenue and better returns, without touching the website, pricing or promotions.
Three businesses. Three different constraints.
One account needed less fragmentation.
One needed advertising decisions connected to product profitability.
One needed a leaner structure and more disciplined scaling.
StoreSmith does not begin with a predetermined list of fixes. It begins with the catalogue, the demand and the commercial evidence, then identifies the small number of decisions most likely to create more revenue.
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