How much is your service?

Our pricing model is based on the performance we achieve, ensuring our goals align with yours. We do not charge retainers.

What level of communication and reporting can I expect?

Weekly Loom walkthroughs with a written summary, plus a dedicated Slack channel with the team available seven days a week. No portal you have to log into to find your numbers.

How do you stay updated on industry trends and best practices?

Through our close relations with Google, we receive ongoing training from high-level representatives on new features often before public release. Our scale across high-spending accounts gives us real-time data on what's actually working.

What sets your agency apart from the competition?

Our specialists are among the top professionals globally. At Ad-Lab we enforce stringent conditions to ensure each team member handles a minimal number of accounts, allowing unparalleled focus on each client.

Do you offer free account audits?

Yes. We conduct in-depth audits and provide strategic recommendations. We identify areas where your previous or current agency is missing optimization opportunities.

How long are your contracts?

Month-to-month. We are confident that within the first month we can demonstrate the Ad-Lab difference, eliminating any need for long-term commitment.

How does the pricing work?

Performance-priced. Month-to-month. No retainer, no annual lock-in, no exit fee.

What's a typical Ad-Lab client size?

$30,000+ in monthly Google + YouTube ad spend, or $100,000+ in monthly revenue. Most are doing both. Below that range the audit doesn't surface enough leverage to justify our model. Above $1M/month is where the system around the ad starts compounding hardest. If you're not sure where you fit, apply for the free audit anyway and we'll tell you straight.

Why aren't you on Meta or TikTok?

Because the work that compounds Google + YouTube spend at scale (landing pages, creative, feed engineering, tracking, AI ops, weekly audits) is full-time work. Spreading thin across Meta and TikTok would mean less depth in the channels where we already have $150M+ of pattern recognition. We partner with Meta and TikTok specialists for clients who need both, but we won't pretend to be the best at every channel ourselves. That said, the Google + YouTube work we ship affects your Meta and TikTok performance every week (branded search volume, AOV across the shop, blended CPA), and we report that cross-channel context as part of the standard cadence.

All case studies

Ecommerce (EU)Sept 2025 – Dec 2025

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

A clean launch, disciplined scaling, ROAS that improved as spend grew

Sept ROAS

2.29x

Oct ROAS

2.16x

Nov ROAS

2.69x

Dec ROAS

2.86x

Chapter 01

The build (before the numbers)

Five campaigns, each with a specific role. No overlap.

On September 8 we launched a completely new Google Ads account: no historical data, no legacy structure, no prior learnings to lean on. The goal wasn't aggressive scale. It was to build a system that could scale without breaking.

We didn't launch everything at once. The account was rolled out in phases, with each campaign serving a specific role.

Five campaigns, five distinct roles

Brand Search to capture existing demand cleanly
Search Prospecting to expand high-intent non-brand queries
Performance Max (Full Build) for scalable demand capture
PMax Retargeting to support return traffic
PMax Competitor to test conquesting without polluting core learning

Chapter 02

What the data showed month over month

Spend scaled every month. ROAS improved every month.

Monthly performance progression

Month	Spend	ROAS	Note
September (launch)	€664.67	~2.29x	Baseline learning and signal collection
October	€1,589.27	2.16x	Budget increased only after early signals stabilised
November	€3,995.49	2.69x	Search prospecting + PMax improved as learning consolidated
December (Dec 1 – 24)	€10,846.87	2.86x	Spend crossed five figures, efficiency higher than launch

December is where most accounts see ROAS drop as spend scales. Here it improved because structure was clean from day one.

Originally published on the daily newsletter

Patrick wrote this case study live the week the numbers landed. Read the original on Beehiiv for the full context.

Read the original write-up

Feb 11, 2026

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