A cosmetics brand wanted to sell more through Google Shopping. But the problem wasn't the campaigns, it was the store behind them: slow, technically outdated, product data full of gaps. We migrated and rebuilt the store first, and only then set up Shopping ads and the product feed. Return on ad spend multiplied afterward.
The case
Google Shopping looks like an advertising topic: budget, bids, campaigns. In reality, it's a chain. The feed shows the product, the ad gets the click, the product page decides the purchase. If one link is weak, the budget goes up in smoke. For this brand, several were weak at once: load time, product data and checkout.
The order: store first, then ads
- Migration and rebuild. New store on Shopify, faster, with a clean checkout and connected ERP.
- Get product data in order. Titles, descriptions, images, SKUs. What's clean in the store is clean in the feed.
- Trust on the product page. Reviews, shipping cost info, matching add-ons. People coming from an ad often don't know the brand.
- Only then Shopping and the feed. Product feed from Shopify, campaigns built on top of it.
What ads can't make up for
| Problem in the store | Consequence in the campaign |
|---|---|
| Slow pages | Expensive clicks that bounce |
| Missing product data | Poor placement or rejected products |
| No trust on the product page | Clicks without purchases |
| Glitchy checkout | Abandoned carts despite paid clicks |
Your takeaways
- Return on ad spend is a result of the whole system, not just the campaign settings.
- More budget on a weak store only amplifies the losses. Foundation first, then scale.
- Data quality is marketing quality. What's poorly maintained in Shopify and your ERP won't get better in the feed.
- Plan relaunch and advertising in sequence. Sending campaigns to a store that's being rebuilt burns money.
Are your ads running but the revenue isn't coming? We first check whether your store can actually convert the visitors.