Challenges
When I partnered with Aluminium Warehouse in early 2024, the account had structural problems that no amount of optimization could fix.
The technical foundation was fragmented: 46 overlapping campaigns across Google Shopping and Search. Conversion tracking was unreliable—accurate in some channels, uncertain in others. The product feed lacked critical optimization signals. Everything lived on Google; there was no platform diversification.
The business leadership understood the implications: wasted budget, lost insights, fragile growth. We aligned on Year 1 strategy: fix the foundation first.
Strategies Year 1: Rebuilding the Foundation
I consolidated 46 scattered campaigns into 6 tightly focused ones, rebuilt conversion tracking from scratch, and optimized the product feed with critical data signals. While Google was being refined, we launched on Bing Ads as a diversification hedge.
Within three months, revenue growth accelerated. High-margin products showed even stronger performance. The efficiency metrics were solid (blended ROAS improved to approximately 9.1x). By year-end, the account ran on a clean structure with reliable data and optimized feeds.
The foundation was built.
Strategies Year 2: Data-Driven Portfolio Focus
In early 2025, new management arrived with a different question: "Before we scale aggressively, which channels and products deliver the most value per pound spent?"
This constraint became the insight generator. Instead of "How do we grow revenue?" we asked "Where is our true competitive advantage?"
Together with management, I proposed a conservative testing phase: start with careful budget allocation (approximately £500 per day, ramping gradually) and use it to answer which products were genuinely efficient.
Q1 and Q2 revealed the answer decisively.
Core product categories delivered 11.8x to 12.9x ROAS. Everything else: 2x to 4x ROAS.
The company's competitive advantage sat in one product category, not spread across many. Most portfolio managers would diversify for safety. New management asked: "What does the data tell us to do?"
Starting in Q2, we realigned budget dramatically. By Q3, approximately 80-85% of spend went to core products. Secondary categories received just enough budget to monitor and test. Monthly budgets increased from conservative early-year levels , reaching peak efficiency in October.
This wasn't aggression for aggression's sake. This was following the data to its natural conclusion within the framework the business set: prove efficiency first, scale confidently later.
Results: Efficiency Over Volume
Year 1 to Year 2 comparison:
- Annual PPC spend: Reduced by approximately 49%
- Annual revenue: Declined by approximately 7%
- Efficiency (ROAS): Improved by approximately 83%
On paper, this looks like a trade-off. But it misses what actually happened.
Three factors shaped these numbers simultaneously. First: market headwinds. UK construction materials faced slowdown in 2025. Second: AOV compression of approximately 3% suppressed revenue across all channels. Third—and critically—new management's strategic constraint meant we deliberately ran conservatively in H1 to validate the data before aggressive scaling.
Despite these headwinds, despite cutting spending in half, we only lost 7% of revenue. The focus strategy protected the business during market downturn.
The Strategic Insight
Year 1 removed structural noise—broken data, unfocused campaigns, unoptimized feeds. Once removed, the data became clear.
Year 2 found the signal within that clean data: your advantage isn't spread across many products. It's concentrated in one area, and it's much stronger than the alternatives.
The budget reallocation from "balanced across categories" to "focused on core products" proved that data-driven focus trumps traditional portfolio diversification—once you have reliable data to follow.
The business brought the strategic constraint. I provided the analytical framework. Together, we shaped an approach the data validated.
What This Means
If you're managing PPC for a DTC e-commerce business:
First, fix the foundation. Broken tracking, poor feed data, unfocused campaigns—these are structural problems that optimization can't solve.
Then follow the data ruthlessly. Once you have clean, comparable data, stop hedging. Find what works best. Build budget allocation around that finding.
Efficiency trumps revenue. Your job is delivering the best return on every pound spent. When efficiency and growth diverge, follow efficiency. Efficiency scales. Efficiency survives downturns.
Use constraints as insight engines. When a business says "we need stability before scaling," that's not a limitation. That's an invitation to analyze deeper and discover competitive advantages hiding in plain sight.
Aluminium Warehouse's two-year journey shows what's possible when the business brings strategic goals and you bring analytical frameworks, working together to shape an approach the data validates.
Efficiency rose 83%. Revenue fell 7%. The first number is the one that scales.
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