Case Study
Google Ads Performance

1.21x → 10.22x
ROAS

How margin-led campaign restructuring, smarter bidding signals, and algorithmic patience turned a burning budget into a compounding revenue engine.

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ROAS Improvement
+ 0 %
Peak Monthly Revenue
€ 0 K
Cost Per Order
- 0 %
More Orders / Month
0 ×

01 The Core Story

In November, the account was bleeding. A ROAS of 1.21x meant nearly every euro spent returned almost nothing above cost. The Marge_Low campaign was operating at a catastrophic 0.27x ROAS, and the Demand Gen Remarketing campaign was spending at €2,413 per conversion while generating just €4 in revenue. The account wasn’t underperforming — it was structurally broken.

The transformation from November to the January–March steady state represents a +481% improvement in ROAS and a +796% increase in monthly revenue — from approximately the same, or lower, monthly spend. This wasn’t a budget story. It was a structure, signal, and discipline story.

Monthly Revenue vs. Ad Spend — Nov 2024 to Mar 2025
November
€45.8K
December
~€118K
January
€281.6K
Feb–Mar
Steady State 7x+
Peak month
Transition / Steady state

02 Five Factors That Drove the Turnaround

01
Margin-Based Restructuring Introduced Control

In November, campaigns lacked margin logic, optimising blindly across all products. Segmenting into Marge_Hoch and Marge_Low with tailored ROAS targets introduced control and intent. This ensured high-margin products received stronger bidding signals, shifting the account from volume-driven to profitability-focused optimisation within clear, effective guardrails.

Marge_Low: 0.27x → profitable tier
02
Cost per Order Reduced Through Smarter Allocation

Cost per order dropped from €1,321 to €89—a 93% reduction—without increasing spend. Inefficient campaigns like Demand Gen Remarketing were restructured, freeing budget for high-performing segments. Combined with improved bidding signals and audience targeting, this reallocation significantly increased efficiency and ensured spend drove meaningful conversions.

€1,321 → €89 CPO (−93%)
03
Conversion Volume Scaled Significantly
Conversions grew from 28.6 to 310.5—nearly 11× higher—on similar or lower spend. Improved structure allowed the algorithm to better identify and prioritise high-intent users.
Previously underutilised demand was captured more effectively, unlocking scalable growth and dramatically increasing the account’s ability to convert relevant traffic.
28.6 → 310.5 orders / month
04
ROAS Targets Enforced Better Efficiency
With no constraints, the algorithm previously wasted spend on low-quality traffic. Introducing margin-aligned ROAS targets forced more selective bidding. As stronger conversion signals accumulated, machine learning improved, compounding efficiency gains and ensuring that scaling occurred without sacrificing profitability or overall performance quality.
Discipline → compounding gains
05
Higher Revenue with Lower Spend
Spend decreased from ~€37,900 to €27,551, while revenue increased nearly sixfold. By eliminating inefficiencies and prioritising high-value segments, the account achieved more with less. This demonstrates that structure—not budget—was the key constraint, and that optimisation can unlock significant growth without increasing investment.
Lower spend. 6× revenue.

03 Before & After — Headline Numbers

Metric November (Before) January Peak (After) Change
ROAS 1.21× 10.22× +745%
Monthly Revenue €45,836 €281,573 +514%
Cost Per Order €1,321 €89 −93%
Conversions / Month 28.6 310.5 +985%
Monthly Ad Spend €37,900 €27,551 −27%

04 Optimisation Tactics Deployed

A
Optimised asset groups using appropriate search themes, interests, and demographic targeting to ensure ads reached the highest-intent audiences for each product margin tier.
B
Improved headlines, descriptions, and all creative assets to align with audience intent signals and maximise click-to-conversion rates across campaign types.
C
Adjusted bidding strategies and target ROAS settings per margin segment — moving away from blanket targets toward tier-specific guardrails that gave the algorithm meaningful direction.
D
Paused frequent target ROAS adjustments and allowed each restructured campaign to stabilise for a minimum of 15 days before making further significant changes — giving the algorithm time to learn.
E
Carefully monitored performance across channels to ensure effective budget allocation, identifying and eliminating wasted spend (e.g. the €2,413-CPO Demand Gen campaign) in favour of high-performing campaigns.
F
Gradually scaled campaigns in line with available budget while maintaining efficiency — avoiding aggressive spend increases that would disrupt algorithmic learning curves.
G
Launched retargeting campaigns specifically designed to recover revenue from abandoned checkouts, capturing high-intent users who had already demonstrated purchase intent.

The result speaks for itself.

All achieved with lower monthly ad spend than the November baseline.
ROAS Improvement Nov → Steady State
+ 0 %
Revenue Growth Same or Lower Spend
+ 0 %

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