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CONTEXT

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Ergonomist is a DTC eCommerce company generating £6.2M in annual revenue with 462 SKUs across 6 product categories. They sell through DTC and operate in 1 market. Their Google Ads account had a monthly spend of £17K managed by a previous agency. 

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DIAGNOSIS

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When we audited the account, we identified several structural issues that were limiting performance and creating significant waste:‍

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1. Misaligned campaign structure - Shopping campaigns were segmented by product type rather than margin tiers, which meant budget allocation was not aligned with profitability. As a result, high-margin SKUs were competing directly with low-margin and even loss-leading products, reducing overall account efficiency and limiting scalable growth.

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2. Lack of query control and keyword isolation - There was no structured negative keyword strategy at the category level, causing significant overlap between Shopping, Search, and Performance Max campaigns. This led to internal competition, inefficient CPC inflation, and poor query prioritization across high-intent vs exploratory traffic.

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3. Inefficient feed structure and poor data quality - The product feed lacked a clear optimization framework, with inconsistent and under-optimised product titles, multiple missing or incomplete attributes, and no use of strategic product duplication. This limited query matching, reduced visibility across high-intent searches, and restricted our ability to segment products by performance or margin. As a result, the account was underutilizing Shopping and Performance Max potential and leaving scalable revenue on the table.

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APPROACH

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We restructured the account in three phases over 4 weeks:‍

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Phase 1: Core Focus: Active Campaigns Optimization & Scaling‍

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We started by restructuring active campaigns around profitability and control, rather than product catalog structure.

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Instead of grouping products by type, we segmented Shopping and Performance Max campaigns by CPA targets, calculated ROAS target for each product, confirmed it with the client and adjusted the structure accordingly.. This allowed us to align bidding strategy with actual business economics, ensuring that high-margin products received the majority of scalable investment, while lower-margin SKUs were constrained to efficiency-focused spend.

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At the same time, we implemented a query control framework to reduce internal competition. This included introducing category-level negative keywords and clearer separation between Search, Shopping, and Performance Max traffic. The goal was to prioritize high-intent queries and prevent overlapping campaigns from bidding against each other.

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We also began restructuring the product feed to support scale. Initial improvements focused on title optimization (based on search intent and keyword mapping) and fixing missing attributes to improve eligibility and coverage. In parallel, we introduced selective product duplication to create additional bidding and segmentation levers for top-performing SKUs.

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The overall logic behind Phase 1 was to:

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• Align spend with profitability
• Regain control over query allocation
• Improve product visibility and matching

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This created a stable and scalable foundation before increasing budgets.‍

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Phase 2: Controlled Scaling & Demand Capture (Weeks 5–8)‍

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With a stable and profitability-aligned structure in place, we shifted focus to controlled scaling and demand expansion.

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A key lever in this phase was the introduction of an aggressive product duplication strategy. We created multiple versions of top-performing SKUs using differentiated titles, images, and attribute combinations. This allowed us to expand query coverage, test alternative positioning angles, and increase impression share across high-intent searches without being limited by a single product configuration.

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At the same time, we leveraged the BFCM period as a scaling catalyst, but approached it with a structured framework rather than simply increasing spend. Budgets were scaled progressively across margin tiers, prioritizing high-margin products and campaigns with proven efficiency. Bidding strategies were adjusted to balance volume and profitability, ensuring that incremental spend translated into incremental revenue rather than diminishing returns.

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Crucially, we treated Black Friday not just as a short-term spike, but as a “trampoline” to reset the account to a higher baseline. By aggressively increasing daily spend, conversion volume, and data signals during this period, we were able to push the account into a new performance tier.

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Because the underlying structure, feed quality, and query control were already in place from Phase 1, this higher level of performance sustained even after the BFCM period ended. Rather than seeing the typical post-promo drop, the account maintained elevated daily revenue and volume, confirming that growth was driven by structural improvements, not just seasonal demand.

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As a result, we were able to increase revenue by +133% within the second month of engagement, while maintaining efficiency and avoiding the typical volatility associated with aggressive seasonal scaling.

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Phase 3: Price-Led Scaling & Demand Expansion (Weeks 9–Ongoing)‍

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This phase became the key driver of continued growth once initial scaling limits were reached.

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After Phase 2, the account approached a saturation point, with impression share exceeding 70% across most prospecting campaigns. While we knew additional demand existed, we were no longer able to access it efficiently through structure or bidding alone.

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At this stage, we identified price competitiveness as the core strategic advantage. The brand was positioned among the most affordable options in the market (excluding low-trust marketplaces like Temu or AliExpress), creating an opportunity to win more clicks and conversions within existing auction coverage.

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To capitalize on this, we introduced a sale price strategy across top-performing SKUs, enabling the price drop / sale badge in Shopping ads. The objective was not just conversion rate improvement, but CTR and auction competitiveness uplift. By making our listings visually and economically more attractive, we were able to improve click-through rates, strengthen ad rank, and extract more value from already high impression share.

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Over time, all hero products were consistently supported with sale pricing, which:

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• Improved CTR and traffic quality
• Strengthened competitive positioning in auctions
• Helped maintain performance during lower seasonality periods

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As a result, we generated over 350K of clicks using the promo strategy and the CTR of 1.3% for shopping, which is above the industry standard.

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Critically, this strategy compounded over time. By the time we approached BFCM 2025, the majority of products were already operating with optimized pricing and promotional signals.

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We once again used the promotional period as a scaling catalyst, doubling spend and revenue during November. However, as with Phase 2, the key outcome was not the spike itself, but the new baseline established afterward.

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Since November 2025, the account has been able to sustain significantly higher daily revenue and spend levels, confirming that growth was driven by structural and strategic improvements rather than short-term promotional effects.

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RESULTS

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Following the restructuring, we scaled prospecting campaigns aggressively without relying on branded traffic, achieving +300% revenue growth over 12 months. Early impact was visible within the first 8 weeks (+133% revenue), while improved query control and feed optimization significantly reduced inefficient spend and stabilized performance at scale.

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KEY TAKEAWAY

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Sustainable scale was achieved by aligning media structure with profitability, competitive positioning, and a price-led strategy tailored to the brand. Campaign architecture, feed strategy, and pricing proved far more impactful than incremental bid or budget adjustments. Growth was driven almost entirely by prospecting, with ~99% non-branded search traffic and zero reliance on Meta ads. Additionally, products featuring visible sale pricing consistently showed a strong correlation with higher click-through rates and increased traffic.

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