From £300k to £1m:
Scaling a National Retailer on Google Ads

Profit-First Shopping Campaigns

The existing shopping campaigns were bidding uniformly across the entire product catalogue, regardless of margin or conversion performance. Spend was being diluted across thousands of SKUs with highly variable ROAS.

The restructure separated the catalogue into performance tiers. Products not generating sufficient ROAS were moved to lower-bid campaigns, ring-fencing budget for higher-performing lines. This allowed for more aggressive bidding on products that were already converting well, while keeping lower performers in play at a reduced cost.

Shopping Feed Optimisation

For many retailers, the product feed is an afterthought — auto-generated titles pulled directly from a CMS, with little thought given to how they appear in the SERP. This was the case here.

Approximately 200 priority SKUs were identified across the most commercially important product lines. Each was given a manually constructed product title, structured to lead with the most search-relevant attributes: material type, format, colour, finish, and application.

The impact was threefold. Click-through rates improved as ads became more relevant to the query. Ad rank increased, as Google's relevance scoring rewarded better title alignment — which in turn lowered average CPC. And with more qualified traffic arriving at more relevant product pages, conversion rates followed.

Performance Max: Layered on Top

Performance Max campaigns were introduced across all major product categories: ceramic, porcelain, marble, stone, and outdoor. Crucially, these were added in addition to the existing shopping campaigns — not as a replacement — ensuring maximum SERP coverage for the brand.

Each campaign was built with category-specific asset groups and target ROAS goals ranging from 600% to 1,300%, calibrated to the margin profile and demand characteristics of each product type.

The dual-campaign approach created dominance in both standard shopping results and the broader Performance Max inventory, including Display, YouTube, and Discovery, without cannibalising existing performance.

Brand Campaigns: Built for Every Product

Brand campaigns represented approximately 5% of total monthly spend — but generated returns of over £350 for every £1 invested. An open budget was applied, with the only constraint being the volume of search demand.

What made this work was scope. Rather than simply bidding on the brand name, campaigns were built out across every exclusive product line the retailer stocked — capturing high-intent searches for products that could only be bought through them.

Achieving this required extensive negative keyword work to maintain precision. With hundreds of product variations across multiple ranges, separating branded intent from generic search required a rigorous and ongoing approach — but the efficiency it unlocked made it one of the highest-value activities in the account.

Cross-Platform Expansion

Google Ads was the primary channel, but growth at scale required a broader platform mix. Microsoft Ads was implemented across all Search campaigns, capturing an audience that skewed older and more affluent — well-matched to a premium tile brand.

On the social side, Meta (Instagram and Facebook) and Pinterest ran as upper and mid-funnel channels, targeting home renovation and interior design audiences with visual product creative. TikTok was in the process of being introduced at the close of the engagement, targeting a younger homeowner demographic.

Rather than treating each platform in isolation, channel roles were defined clearly: Search for intent capture, social for discovery and retargeting, with attribution structured to avoid double-counting across the funnel.

Local Campaigns: Rethinking In-Store Traffic

With 20+ showrooms across the UK, store visits were a meaningful part of the business. The inherited approach used standard Search campaigns targeting local queries — "tile shops near me", "tile showrooms Portsmouth" — at a cost per store visit between £20 and £40, with the London stores sitting at the top of that range.

These campaigns were paused. In their place, Performance Max campaigns with local objectives were built for each region, using store visit goals rather than online conversion targets. Google's machine learning, fed with accurate location data and optimised asset groups, handled distribution across Search, Maps, Display, and YouTube.