Resources · Case studies

Real client work. Real before-after numbers.

21ecom and B2B accounts scaled across Google Search, Shopping, Performance Max, YouTube, and Demand Gen. Pulled straight from Patrick's daily newsletter, with a link back to the original write-up on every detail page.

$520K to $1.06M a month by adding YouTube on top of a clean Search, Shopping, and PMax stack - case study cover
EcommerceOne quarterIllustrative

$520K to $1.06M a month by adding YouTube on top of a clean Search, Shopping, and PMax stack

This account was already good. Search, Shopping, and Performance Max were clean and profitable at a 3.0x blended ROAS. The problem with good accounts is they run out of demand to harvest. Push more budget into Search and you just pay more for the same clicks. We added YouTube and Demand Gen as a demand-creation layer, sequenced so it fed the harvest layers, and doubled the account in a quarter. Here's the full campaign map, every campaign and what it feeds.

Monthly revenue: $520K → $1.06MBlended ROAS: 3.0x → 2.8xBranded search demand: +44%
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The Performance Max structure we put on every account over $50K a month - case study cover
Ecommerce8 weeksIllustrative

The Performance Max structure we put on every account over $50K a month

Most accounts run one Performance Max campaign with every product in it and wonder why it plateaus. The bidder can't tell a 12% margin clearance mug from a 60% margin hero sofa, so it averages. We rebuilt this account into a margin-tiered, feed-segmented PMax structure with brand excluded. Same budget, 38% more revenue, and the profitable SKUs finally got the spend.

PMax + Shopping ROAS: 2.1x → 2.9xRevenue: +38%Ad spend: Flat
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A 4.0x blended ROAS was hiding a non-brand account barely breaking even - case study cover
DTC ecommerce90 daysIllustrative

A 4.0x blended ROAS was hiding a non-brand account barely breaking even

The account reported a 4.0x blended ROAS and everyone was happy. Then we separated branded search from real acquisition. Brand was running at 14x and carrying the whole number. Non-brand, the part that actually grows the business, sat at 2.4x and capped. Here's how we split them and doubled new-customer revenue.

Blended ROAS (before): 4.0xTrue non-brand ROAS: 2.4x → 2.5xNon-brand spend: $45K → $92K
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A 3.2x ROAS account that was losing money on its bestsellers - case study cover
Ecommerce60 daysIllustrative

A 3.2x ROAS account that was losing money on its bestsellers

The account looked like a 3.2x ROAS winner. The problem: its bestsellers were its lowest-margin products, and the bidder was pouring budget into them because they converted. Revenue looked great. The bank account didn't. We fed profit into Google instead of revenue, let the bidder optimise for margin, and grew gross profit 36% without selling a dollar more.

Gross profit: +36%POAS: 1.0 → 1.4ROAS: 3.2x → 3.1x
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Shopping revenue +41% from feed engineering, with bids untouched - case study cover
Ecommerce6 weeksIllustrative

Shopping revenue +41% from feed engineering, with bids untouched

The account was bidding fine and still invisible on half its catalogue. Shopping doesn't reward bids, it rewards relevance and coverage. We rebuilt the product feed (titles, attributes, GTINs, a supplemental feed) and layered a three-tier priority structure to funnel queries. Same budget, 41% more Shopping revenue, and products that had never shown up started winning.

Shopping revenue: +41%Absolute top impression share: 38% → 61%Clicks: +33%
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A 4.5x tROAS target was throttling a profitable account. Lowering it added 71% revenue - case study cover
Ecommerce75 daysIllustrative

A 4.5x tROAS target was throttling a profitable account. Lowering it added 71% revenue

The account ran a 4.5x tROAS target and everyone felt good about it. Trouble is, break-even was 2.2x, so a 4.5x target left a mountain of profitable volume on the table. We laddered the target down in controlled steps, watched the marginal ROAS at each one, and uncapped the winners. Spend more than doubled, ROAS settled at a healthy 3.4x, and revenue grew 71%. Every extra dollar still cleared break-even.

Revenue: +71%Monthly spend: $30K → $68KROAS: 4.5x → 3.4x
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+18% revenue in 7 days. Without increasing spend - case study cover
Ecommerce7 days, week-over-week

+18% revenue in 7 days. Without increasing spend

Most accounts chase growth by increasing budgets. This account did the opposite: structural tightening at the margins, no creative refresh, no budget changes. 18% more revenue in 7 days at the same spend.

Revenue lift: +18%Conversions lift: +20%Spend change: Flat
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From €0 to €31K/month with no efficiency decay - case study cover
Ecommerce (EU)Sept 2025 – Dec 2025

From €0 to €31K/month with no efficiency decay

Most case studies start after the hard part is done. This one starts on day one of a brand-new Google Ads account with no historical data. As spend increased, performance didn't deteriorate. It improved.

Sept ROAS: 2.29xOct ROAS: 2.16xNov ROAS: 2.69x
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+112% revenue in November with ROAS holding above target - case study cover
EcommerceNovember (BFCM)

+112% revenue in November with ROAS holding above target

Most accounts lose discipline in November. Budgets surge, promos stack, ROAS gets sacrificed. This account took a different path: revenue +112%, spend +134%, ROAS softened only 4% and stayed above 2x target.

Revenue: +112%Spend: +134%ROAS: -4% (above 2x target)
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Shopping coverage 49% → 57% in one month - case study cover
EcommerceNovember → December

Shopping coverage 49% → 57% in one month

Shopping performance jumps when coverage improves, not when bids increase. We didn't raise bids or loosen targets. We implemented feed duplication. Impression share went from 49% to 57% against the same competitors.

November IS: 49.04%December IS: 57.06%Net gain: +8 pp
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YouTube conversions +100% with no spend increase - case study cover
Ecommerce7 days post-change

YouTube conversions +100% with no spend increase

Most YouTube performance issues aren't creative problems. They're signal problems. We changed three things: excluded app inventory, built a conversion-led video system, layered in a purchaser-based first-party audience. Conversions doubled.

Conversions lift: +100%Spend change: ControlledConversion quality: Materially improved
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$106K revenue from YouTube + Search in 28 days - case study cover
B2B services28 days (Jul 7 – Aug 4, 2025)

$106K revenue from YouTube + Search in 28 days

B2B client in a competitive niche. We ran YouTube + Google Search only. Steady climb from $0 to $106K of attributed revenue in 28 days. The system was the win, not the budget.

Revenue: $106KAd spend: $24.8KConversions: 920
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Casa Beyond scaled to $560K/month without raising CAC - case study cover
Home decorJune 2025

Casa Beyond scaled to $560K/month without raising CAC

Casa Beyond came in with broad campaigns, no SKU prioritisation, and ROAS pressure. We segmented by margin, built tight campaign clusters, and optimised for conversion-value-over-cost at the SKU level. Same brand, same products, same ad spend appetite. Different system.

Revenue: $560KAd spend: $208KConversions: 3,920
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Crossed $500K/month in Google Ads revenue - case study cover
Ecommerce3 months since partnership

Crossed $500K/month in Google Ads revenue

One ecom client just crossed half a million in monthly Google Ads revenue. 2× growth since we partnered three months ago. Founder reaction: "Let's gooo." Q4 target is $1M/month.

Monthly revenue: $500K+Growth since partnership: 2x+Time to milestone: 3 months
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3.47x ROAS on a fresh campaign in a struggling account - case study cover
EcommerceFresh campaign launch

3.47x ROAS on a fresh campaign in a struggling account

Client had historically struggled to break performance ceilings. Fresh campaign, right system. 3.47x ROAS on $1.83K of spend. Quote from the client: "I have never seen this type of ROAS on this account."

ROAS: 3.47xConversion value: $6,360Ad spend: $1,830
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Niche ecom: $34.7K in 42 days at 3.05x ROAS - case study cover
Niche ecom6 weeks

Niche ecom: $34.7K in 42 days at 3.05x ROAS

Fast-growing niche ecom brand. Six weeks. $8.7K of spend, $34.7K of sales at 3.05x ROAS. Engineered, not lucky. Funnel-stage campaign architecture + feed work + conversion-first landing pages.

Revenue: $34.7KConversions: 1,230Ad spend: $8.7K
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17.97x ROAS on a freshly built ecom account - case study cover
EcommerceJan 1 – Feb 17, 2025

17.97x ROAS on a freshly built ecom account

$9.9K spend → $178K revenue → 17.97x ROAS. No viral spike, no seasonal promo, no discount. Built from scratch with profitable scale in mind. Systems, not luck.

Revenue: $178KConversions: 1,520Ad spend: $9.9K
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€104K in revenue with 70%+ from cold traffic - case study cover
Ecommerce (EU)Single campaign month

€104K in revenue with 70%+ from cold traffic

Most brands treat Google as a harvest channel for branded clicks. Built right, it becomes an acquisition engine. Here's how we drove €104K in revenue with 70%+ from cold traffic.

Revenue: €104KPurchases: 1,490Ad spend: €47.2K
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$1.18M in sales on $469K ad spend across 90 days - case study cover
EcommerceMar – Jun 2025

$1.18M in sales on $469K ad spend across 90 days

Once you cross $100K+ in monthly ad spend, your biggest risk isn't the platform. It's operational chaos. This account ran $469K of spend over 90 days with $1.18M in tracked revenue, blended 2.52x. Sustained, not a flash.

Revenue: $1.18MPurchases: 8,580Ad spend: $469K
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+$248K in 90 days without killing ROAS - case study cover
EcommerceFebruary → March 2025

+$248K in 90 days without killing ROAS

February: $172K revenue, $62.7K spend, inconsistent daily performance. March: $421K revenue, $178K spend, stable optimised performance. The difference? A growth system instead of a gamble.

Feb revenue: $172KMarch revenue: $421KSpend lift: 2.8x
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Conversion rate doubled by fixing the shipping schema - case study cover
Ecommerce7 days

Conversion rate doubled by fixing the shipping schema

Ads dialled. CTR healthy. CPC low. Conversions embarrassing. We went deeper than the campaign and looked at the tech layer. Google was showing a 4-day shipping estimate; the brand fulfilled in 2. Fixed the schema, conversion rate doubled in 7 days.

Conversion rate: 2xTime to lift: 7 daysChange: Schema (no campaign change)
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