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7-figure Performance Max + Search growth, with control

$5.47M revenue on $1.12M ad spend (ROAS 4.89)

This case study shows the structure, scripts, and CRO fixes we shipped to scale a US ecommerce account without turning Performance Max into a black box.

Just Free Audit

Proof is shown via video walkthrough and on-screen Google Ads reporting.

Last updated: Changelog: 2026-02-10: Initial publish
Google Ads overview showing $5.47M revenue, $1.12M spend, and 4.89 ROAS for the year.
Year view proof: $5.47M revenue on $1.12M spend (ROAS 4.89).

Dec 2023 to Dec 2024 performance with peak Nov-Dec proof. Every number below is supported by screenshots and walkthrough video.

Results snapshot

Client: Client name withheld | Industry: Ecommerce...
ClientClient name withheld
IndustryEcommerce
MarketUnited States
ChannelsPerformance Max, Search
Catalog size5,000+ SKUs
Timeframe shownDec 2023 to Dec 2024 (plus Nov-Dec 2024 highlights)
Revenue$5.47M
Ad spend$1.12M
ROAS4.89
ProofFull video walkthrough + screenshots
Last updated2026-02-10
Google Ads performance snapshot highlighting $1.2M conversion value, $195K cost, and 6.16 ROAS.
Peak months proof: Nov-Dec performance shows $1.2M sales on $195K spend (ROAS 6.16).

The starting point

This was a US-focused ecommerce account scaling with two core engines: Performance Max and Search.

  • Keep scale profitable with a clear ROAS target
  • Avoid wasted spend from irrelevant inventory and placements
  • Stop SKU-level winners being diluted by the full catalog
  • Fix conversion leaks so paid traffic did not bleed margin

What we changed

Budget roles

Spend concentration matched the best performers.

  • 83% PMax
  • 15% non-brand Search
  • 2% brand Search
Campaign budget split showing most spend allocated to Performance Max with smaller Search and Brand budgets.
Budget roles proof: 83% PMax, 15% non-brand Search, 2% brand Search.

PMax structure by category and margin

We separated high-margin categories from the rest while keeping a controlled catch-all for discovery.

Google Ads campaign list showing multiple Performance Max campaigns segmented by category plus a catch-all.
Structure proof: PMax split by category and margin, with a controlled catch-all.

Search structure and budget role

Search captured explicit demand and defended branded coverage without starving PMax of learning data.

Audience signals

We kept signals tight: past purchasers, high-intent site users, and top product categories only.

Exclusions that stop obvious waste

Negative themes and placement exclusions prevented spend on irrelevant traffic.

Google Ads content exclusions screen showing negative keyword lists and placement exclusions applied.
Waste control proof: exclusions applied to reduce irrelevant placements and themes.

Targeting settings we do not skip at scale

Performance Max settings showing Target ROAS bidding, English language, and United States targeting.
Settings proof: Target ROAS with US-only targeting and presence-based location option.
Performance Max settings showing feeds set to all products and store locations set to all locations.
Coverage proof: feed and store location scope confirmed for the campaign.

SKU-level control using scripts

Performance buckets

We split inventory by performance so winners stay protected while laggards get controlled testing.

SKU performance dashboard grouping products into Elites, Regulars, Climbers, Strugglers, Ghosts, and Sleepers.
SKU control proof: product bucketing used to protect winners and limit wasted spend.

Product-type reporting

Category-level ROAS shaped merchandising focus and budget caps.

Product type report table showing clicks, cost, conversion value, and ROAS by product group.
Merchandising proof: ROAS by product type used to guide budget and inventory focus.

Channel spend tracking

We used scripts to estimate PMax channel mix and catch drift away from Shopping intent.

Chart estimating Performance Max spend share across Shopping, Video, Display, and other channels over time.
Visibility proof: script estimates channel-level spend to detect drift and protect Shopping intent.

CRO checklist rollout

We used a checklist cadence to spot friction and align the site with paid traffic intent.

  • Pricing format tests
  • Option overload control
  • Product page and checkout friction checks
Product page CRO checklist with tasks, status columns, and priority indicators for conversion improvements.
CRO proof: checklist-based rollout used to find and fix conversion friction.

Governance

  • Approvals before major budget or structure changes
  • Brand safety checks on placements and query themes
  • Rollback plan for every experiment or structural change

How we use AI

Do

  • Scan reports for anomalies and opportunity clusters
  • Draft hypotheses, QA checklists, and rollout steps
  • Summarize Action Logs for faster client updates

Don't

  • Auto-apply bid, budget, or feed changes
  • Publish creative or landing changes without review
  • Trust outputs without validating against real data

Positioning line: Agentic speed, human QA.

Full walkthrough video

Watch the full walkthrough to see the live account, the performance proof, and the structural changes that made the results repeatable.

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