--- source_url: "https://www.ad-lab.io/resources/case-studies" title: Case Studies. 11 ecom brands scaled with Google + YouTube Ads. Ad-Lab mirrored_at: 2026-08-10T01:32:24.504Z host: www.ad-lab.io cited_in_42a: true mirror_canonical: "https://index.42a.ai/www.ad-lab.io/resources/case-studies" --- > **Original source:** https://www.ad-lab.io/resources/case-studies 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. [Book a free audit](https://www.ad-lab.io/book-a-call)[Read the daily newsletter](https://adlab.beehiiv.com/) [ ![$520K to $1.06M a month by adding YouTube on top of a clean Search, Shopping, and PMax stack - case study cover](https://www.ad-lab.io/api/case-studies/cover/full-funnel-youtube-scale-1m) 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% Read the breakdown ](https://www.ad-lab.io/resources/case-studies/full-funnel-youtube-scale-1m)[ ![The Performance Max structure we put on every account over $50K a month - case study cover](https://www.ad-lab.io/api/case-studies/cover/pmax-structure-asset-group-split) 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 Read the breakdown ](https://www.ad-lab.io/resources/case-studies/pmax-structure-asset-group-split)[ ![A 4.0x blended ROAS was hiding a non-brand account barely breaking even - case study cover](https://www.ad-lab.io/api/case-studies/cover/brand-nonbrand-split-true-cac) 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 Read the breakdown ](https://www.ad-lab.io/resources/case-studies/brand-nonbrand-split-true-cac)[ ![A 3.2x ROAS account that was losing money on its bestsellers - case study cover](https://www.ad-lab.io/api/case-studies/cover/poas-profit-bidding-rebuild) 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 Read the breakdown ](https://www.ad-lab.io/resources/case-studies/poas-profit-bidding-rebuild)[ ![Shopping revenue +41% from feed engineering, with bids untouched - case study cover](https://www.ad-lab.io/api/case-studies/cover/feed-engineered-shopping-moat) 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% Read the breakdown ](https://www.ad-lab.io/resources/case-studies/feed-engineered-shopping-moat)[ ![A 4.5x tROAS target was throttling a profitable account. Lowering it added 71% revenue - case study cover](https://www.ad-lab.io/api/case-studies/cover/troas-target-unlocked-scale) 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 Read the breakdown ](https://www.ad-lab.io/resources/case-studies/troas-target-unlocked-scale)[ ![+18% revenue in 7 days. Without increasing spend - case study cover](https://www.ad-lab.io/api/case-studies/cover/7-day-18-percent-lift) 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 Read the breakdown ](https://www.ad-lab.io/resources/case-studies/7-day-18-percent-lift)[ ![From €0 to €31K/month with no efficiency decay - case study cover](https://www.ad-lab.io/api/case-studies/cover/controlled-ramp-31k) 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 Read the breakdown ](https://www.ad-lab.io/resources/case-studies/controlled-ramp-31k)[ ![+112% revenue in November with ROAS holding above target - case study cover](https://www.ad-lab.io/api/case-studies/cover/bfcm-revenue-doubled) 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) Read the breakdown ](https://www.ad-lab.io/resources/case-studies/bfcm-revenue-doubled)[ ![Shopping coverage 49% → 57% in one month - case study cover](https://www.ad-lab.io/api/case-studies/cover/shopping-coverage-49-to-57) 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 Read the breakdown ](https://www.ad-lab.io/resources/case-studies/shopping-coverage-49-to-57)[ ![YouTube conversions +100% with no spend increase - case study cover](https://www.ad-lab.io/api/case-studies/cover/youtube-conversions-doubled) 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 Read the breakdown ](https://www.ad-lab.io/resources/case-studies/youtube-conversions-doubled)[ ![$106K revenue from YouTube + Search in 28 days - case study cover](https://www.ad-lab.io/api/case-studies/cover/b2b-youtube-search-106k) 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 Read the breakdown ](https://www.ad-lab.io/resources/case-studies/b2b-youtube-search-106k)[ ![Casa Beyond scaled to $560K/month without raising CAC - case study cover](https://www.ad-lab.io/api/case-studies/cover/casa-beyond-home-decor) 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 Read the breakdown ](https://www.ad-lab.io/resources/case-studies/casa-beyond-home-decor)[ ![Crossed $500K/month in Google Ads revenue - case study cover](https://www.ad-lab.io/api/case-studies/cover/500k-monthly-club) 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 Read the breakdown ](https://www.ad-lab.io/resources/case-studies/500k-monthly-club)[ ![3.47x ROAS on a fresh campaign in a struggling account - case study cover](https://www.ad-lab.io/api/case-studies/cover/fresh-campaign-3-47x) 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 Read the breakdown ](https://www.ad-lab.io/resources/case-studies/fresh-campaign-3-47x)[ ![Niche ecom: $34.7K in 42 days at 3.05x ROAS - case study cover](https://www.ad-lab.io/api/case-studies/cover/niche-ecom-42-days) 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 Read the breakdown ](https://www.ad-lab.io/resources/case-studies/niche-ecom-42-days)[ ![17.97x ROAS on a freshly built ecom account - case study cover](https://www.ad-lab.io/api/case-studies/cover/17x-roas-from-scratch) 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 Read the breakdown ](https://www.ad-lab.io/resources/case-studies/17x-roas-from-scratch)[ ![€104K in revenue with 70%+ from cold traffic - case study cover](https://www.ad-lab.io/api/case-studies/cover/eu-cold-traffic-104k) 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 Read the breakdown ](https://www.ad-lab.io/resources/case-studies/eu-cold-traffic-104k)[ ![$1.18M in sales on $469K ad spend across 90 days - case study cover](https://www.ad-lab.io/api/case-studies/cover/million-dollar-90-days) 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 Read the breakdown ](https://www.ad-lab.io/resources/case-studies/million-dollar-90-days)[ ![+$248K in 90 days without killing ROAS - case study cover](https://www.ad-lab.io/api/case-studies/cover/90-day-248k-lift) 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 Read the breakdown ](https://www.ad-lab.io/resources/case-studies/90-day-248k-lift)[ ![Conversion rate doubled by fixing the shipping schema - case study cover](https://www.ad-lab.io/api/case-studies/cover/schema-fix-conversions-doubled) 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) Read the breakdown ](https://www.ad-lab.io/resources/case-studies/schema-fix-conversions-doubled) Want this on your account? ## Free 15-minute audit. No deck. No pitch. We screenshare your Google Ads, GA4, and Shopify, run the same audit playbook these case studies came out of, and walk away leaving you the doc whether you become a client or not. [Book the call](https://www.ad-lab.io/book-a-call)