Client name withheld under NDA.
Fashion is one of the hardest ecommerce categories to make profitable on Google Shopping in Singapore. Shoppers browse many products before buying, compare against marketplaces and fast-fashion giants, and often leave without a purchase. Margins after returns and discounts leave little room, so every dollar of ad spend has to work hard.
This label sold its own womenswear designs through an online store and spent S$8,000 a month on one automated Shopping campaign. It earned S$2.00 for every dollar spent, below the roughly 2.8× it needed to break even after returns. Nearly 40% of its products were disapproved or limited in Merchant Center because of missing sizes, colours and product identifiers, and visitors who browsed without buying never saw the brand again.
We filled in size, colour, material and age group for every product, set product identifiers correctly for the label's own designs and rewrote titles to lead with garment type, fabric and fit.
Shoppers filter by size and colour. A product without those details simply does not show for the searches that matter. Clear titles also improved click-through, because shoppers could see the fabric and fit before they clicked.
We split campaigns by category (dresses, workwear and basics) and by margin tier using custom labels, with a separate campaign for bestsellers.
A bestselling dress and a slow-moving basic deserve different budgets. The structure let the account spend where products actually sold. Custom labels also let the team push new arrivals for their first few weeks, when interest in a new collection is highest.
We set up dynamic remarketing showing the exact items people had viewed, with a seven-day window for cart abandoners, exclusions for recent buyers and frequency caps.
Fashion shoppers rarely buy on their first visit. Showing them the piece they liked brought many of them back to finish the purchase. Frequency caps and buyer exclusions kept the ads from following people around after they had already purchased.
We moved to target ROAS bidding by margin tier and tested on-model main images against flat lays, using lifestyle images in remarketing.
On-model images showed fit and drape, which mattered more to shoppers than a clean flat lay. Seasonal edits got their own image sets, so shoppers saw pieces styled for what they were actually shopping for.
Once ROAS was well above break-even, we raised budgets around new collection drops, paused sizes that were out of stock and reviewed search terms weekly.
Scaling only made sense once each dollar was profitable. Pausing out-of-stock sizes stopped the account paying for clicks it could not convert. Weekly search term reviews also excluded searches for other brands and marketplaces that brought clicks but rarely sales.
By month six, Google Ads returned S$4.10 for every dollar, up from S$2.00. With returns well above break-even, monthly spend grew to S$11,000 and revenue from Google Ads reached S$45,100 a month, 182% more than when the work started. Remarketing brought back shoppers who had left, and nearly every product was eligible to show. Because the account was now profitable at a higher spend, the label could plan larger collection launches with paid support built in from the start, instead of treating Google Ads as a cost to cut whenever sales slowed.
Approved products passed 90% and ROAS moved above break-even.
Dynamic remarketing live and ROAS around 3.3×.
Spend raised to S$11,000 a month at a 4.1× ROAS.
Shopping filters on size, colour and fit. Filling in those details for every product was the cheapest gain in the whole project, because products that had been invisible started to show.
Most people browse several times before they buy clothes online. Dynamic remarketing that showed the exact piece they viewed turned many of those browsers into buyers.
This label needed about 2.8× to cover costs and returns. Raising spend at 2.0× would have lost money faster, so the budget only grew once results were comfortably above that line.
Feeds, Shopping campaigns and Performance Max managed for profit.
Results across SEO, paid media, content, web and more.
How a home and lifestyle retailer went from 1.2× to 4.9× ROAS.
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