Why a made-to-order furniture catalog outgrew Shopify’s native search.

Summary

Sterling Home sells a catalog where a single sofa or dining table can carry hundreds of variant permutations, to shoppers spending upward of £600 a basket. When the retailer migrated to Shopify, it adopted the platform’s native search and merchandising tools. A review three months later found that high-intent queries like “green sofa” were failing to surface the right product.

Sterling brought in the Athos Commerce Intelligent Discovery Platform for site search, merchandising, and recommendations. Because the platform is fully API-powered, retrofitting it into the new Shopify build was straightforward.

Revenue per search session rose 34%. Searcher average order value rose 30%, against 16% for non-searchers over the same period. Search users make up roughly 12% of sessions and now generate 30% of online revenue.

The brand

For over 50 years, Sterling Home has been one of Scotland’s best-known names in furniture and home decor, built from a single flagship store into a multi-category destination for sofas, dining, bedroom, and homewares. Sterling has grown its reputation on breadth of range and a considered, big-ticket shopping experience, the kind where a customer is choosing a leather sofa or dining set they expect to live with for a decade.

Much of what Sterling sells is made-to-order: a single sofa or dining table can come in tens, sometimes hundreds, of permutations across swatches, configurations, backing, and handing options. That combination of a large, highly configurable catalog and a high-value, considered purchase journey makes product discovery critical. If a shopper cannot quickly find the right product, they are likely to abandon the site, a costly drop-off given Sterling’s average basket size.

The challenge

Sterling migrated to Shopify to modernize a website that relied heavily on manual data entry and order processing, aiming to improve both the front-end customer journey and back-end efficiency. As part of that migration, the team initially chose Shopify’s native search and merchandising tools, believing they were powerful enough for the new site.

A three-month post-migration review told a different story. With an average order value north of £600 and a catalog where individual products can have hundreds of variant permutations, generic or loosely relevant search results are a bigger problem for Sterling than for lower-consideration categories. A shopper hunting for “leather sofas” or “corner sofas” who gets a page of poorly ranked results is far more likely to abandon a £900 basket than a £30 one. Because the site integrates directly with Sterling’s ERP to sync product data and orders, every product variant needs its own unique URL, an already complex structure that native Shopify search struggled to search across. Simple, high-intent queries like “green sofa” often failed to surface the right product.

Sterling was also running its Summer Sale across the trading period, which meant any read on performance needed to isolate what was genuinely being driven by better product discovery versus the seasonal promotional lift the whole site was getting.

Objectives

Sterling set three objectives for the work.

1. Separate discovery gains from promotional lift: measure search performance in a way that could be read independently of the Summer Sale running across the same period.
2. Make a configurable catalog searchable: return the right product for high-intent queries across a catalog where one product can carry hundreds of variant URLs.
3. Protect basket value at the point of discovery: surface the considered, high-value items that match what shoppers were looking for, given an average order value above £600.

The solution

Following the post-migration review, Sterling’s internal team brought Athos Commerce site search, merchandising, and recommendations into the Shopify site to bring more relevant, intent-driven results earlier in the customer journey. The work targeted the high-value queries that rank among the site’s top searches: “leather sofas,” “dining tables,” “sideboards,” and “corner sofas.”

Because the platform is fully API-powered, retrofitting it into the new Shopify site was not a heavy lift. With the internal team already familiar with the platform from prior experience, the setup and migration process was straightforward.

Implementation

The timing aligned with a planned rebuild of Sterling’s product listing pages. The team used the opportunity to add availability “chips” to product cards, indicating which colors, leathers, fabrics, and sizes a product is available in, rather than only showing swatch colors as before.

The results

The clearest signal in the data is what happened to basket size. Post-launch, searcher average order value rose 30% (£685 to £893).

That figure also settles the Summer Sale question. The sale lifted the whole site, so the read needed a control, and the two shopper groups supply one. Non-searcher average order value rose 16% over the same period. Searcher average order value rose 30%, nearly twice as far. The sale explains part of the movement for both groups. Search explains the gap between them.

Search users have also become the most valuable visitors on the site. Making up roughly 12% of sessions, they now generate 30% of online revenue, a 2.5x over-index that has widened since launch.

• +8% increase in overall site conversion rate since implementing Athos
• +32% per day revenue from search sessions (£44.5k over 38 days pre-launch versus £50.9k over 33 days since launch)
• +34% revenue per search session (£3.40 to £4.58)
• +30% average order value from search (£685 to £893), versus +16% for non-searchers over the same period
• +52% search conversion rate (0.50% to 0.76%)

Turning search data into range decisions

Beyond on-site performance, Sterling’s team uses the Athos dashboard to compare what customers are searching for against current product availability, with a red, yellow, and green system flagging where demand and range do not line up. That visibility helps the team spot emerging trends and decide where to source or add new products.

It also helps with a challenge that runs the other way for Sterling compared with most retailers: not every product it sells is listed on the website. Some items are complex enough that customers need in-store expert help to configure them, so search trend data also informs which products are suited to a self-service online journey and which are better kept as an in-store, assisted experience.

Looking ahead

With search and merchandising established on Shopify, Sterling’s internal team continues to use search trend data to guide range and inventory decisions, identifying gaps between customer demand and product availability to inform what gets added to the site next.

What transfers to other retailers

1. Index at the variant level when the catalog is configurable. Sterling’s ERP integration gives every variant its own URL, which is what native Shopify search could not search across. A made-to-order catalog only becomes searchable when the index matches the structure the ERP produces, which is why “green sofa” started returning green sofas.
2. Use searcher and non-searcher cohorts as a built-in control during promotions. Sterling measured through its own Summer Sale and could still read the result, because reporting splits the two groups by default. Any retailer measuring a discovery change during a trading event has the same control available without setting up a test.
3. Feed search demand back into range planning as well as the results page. The demand-against-availability view turns search logs into a buying signal, including the inverse case where the data tells Sterling which complex products belong in store rather than online. That is merchandising intelligence, and most retailers throw the input away.

Sterling’s catalog did not change. What changed is how much of it a shopper can find.

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