Scaling LA Design Concepts From Five-Figure to Mid Six-Figure Monthly Revenue On Google Ads
How we rebuilt the PMax shopping engine for a US luxury fabrics & wallpaper retailer — and roughly doubled return-on-ad-spend at more than ten times the previous monthly revenue.
Revenue growth
in 7 months
ROAS lift
efficiency at scale
Spend grown
ROAS still doubled
Engagement window
Companies we've worked with
The 60-second version
LA Design Concepts is a US luxury fabrics & wallpaper e-commerce retailer run by Frank K. We took over Google Ads management when the account was generating five-figure monthly revenue on a default PMax setup. Over seven months, we rebuilt the shopping engine across four pillars — feed segmentation, campaign architecture, audience signal layering, and category-economics bidding. Revenue grew roughly eleven-fold while ROAS approximately doubled. Efficiency improved as scale grew, not despite it.
Where they were
A premium shopping account stuck on default PMax
Frank had run LA Design Concepts' Google Ads in-house and via a previous freelancer for around two years before bringing us in. The account was producing five-figure monthly revenue at a return-on-ad-spend in the high-twos — competent, but well below the ceiling for a brand selling luxury fabrics and wallpaper at four- and five-figure AOVs.
The diagnosis was a textbook 'outgrown the setup' pattern. One PMax campaign with a single all-products asset group. No customer-list audience signals. No segmentation by AOV band or category. A single account-wide ROAS target applied uniformly to a $200 hardware sale and a $5,000 drapery order — two completely different conversion economies sharing one bid strategy.
Spend was modest because the account couldn't be trusted to scale. Pushing more budget through a default Smart Shopping setup at a luxury price point usually punishes ROAS faster than it grows revenue. Frank knew the headroom was there. He needed someone to architect the structure that could spend into it.
What we did
The four-pillar PMax rebuild
The work was structural, not tactical. We treated Google's Performance Max as a shopping engine that needs the right inputs to scale — a clean feed, segmented campaigns, real audience signals, and category-aware bidding.
Feed restructure
Segmented the product feed by AOV band (sub-$200 / $200–$1,000 / $1,000–$5,000 / $5,000+) and by category (drapery, wallcovering, hardware, trim). Custom labels on every product. The feed went from a single bucket to a sixteen-cell matrix that PMax could actually act on.
Campaign segmentation
Moved off 'one PMax to rule them all' into segmented PMax campaigns aligned to each AOV band and category. Each campaign got its own asset group, its own creative, and — crucially — its own bid strategy reflecting the realistic conversion economics of that segment.
Audience signal layering
Uploaded customer-match lists for existing buyers, abandoned-cart non-buyers, and high-AOV historical purchasers. Layered them into PMax as audience signals so the algorithm had a real model of who converts at which price point — not a guess built from broad-match query data.
Bid strategy by category economics
Replaced the account-wide ROAS target with per-segment targets. A $5,000 drapery sale carries different attribution and margin profile to a $200 hardware order; bidding them identically wastes spend on the cheap stuff and underbids on the expensive stuff. The new structure forces PMax to respect those economics.
The numbers
What changed
Revenue growth
in 7 months
ROAS lift
efficiency at scale
Spend grown
ROAS still doubled
Engagement window
FAQ
Common questions
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