SEO | Strategy | Catalogue enrichment
694% increase in top 3 ranking keywords
£812,000 annual revenue increase
1,000+ products

The challenge

After its launch in 2010 quickly became one of the world’s best-known furniture and homeware retailers, and a leader in ecommerce. It operated internationally, with a huge range of furniture and homeware products available in eight territories, and with annual revenue of more than £170 million.

The team at knew their product catalogue had huge search potential, but they didn’t have the data to properly assess it, or the means to get the data on the scale they needed.

As a leader in enterprise-scale ecommerce, their site was already ranking well for a lot of terms, but not as well for interchangeable product names or synonyms. The team came to us wanting SEO help to inform their future content strategy, and so grow the site’s search presence.

Because of the sheer size of the site and the topics it could cover, the team needed us to provide keyword research at a scale that covered their entire search footprint. This meant they would be able to fully understand the ranking potential of the site, get a full view of their query spaces, and prioritise content based on both opportunity and seasonal peaks.

The work

The team were on a tight deadline and needed a quick solution. The scale and variety of keywords and query spaces was huge – sells everything from sofas and desks to glassware, kitchen accessories and wall art. This makes for a huge variance in keywords, from directly product-related terms (2-seater sofas, leather sofas) to informational ones (how to reupholster a sofa).

Using our unique process for carrying out keyword research at scale, we combined various data sources (product lists, category pages, product filters) and combined informational modifiers (how, where), along with other inputs, to define a seed keyword list.

This meant we could provide organised keyword research for the full product set, focusing on bottom-of-funnel terms, as well as keyword research suitable for editorial top-of-funnel content.

On top of this data, we mined search volume over a 12-month period, which allowed us to find when search volume peaked.

Our recommendation was then to produce relevant content at least a month prior to each peak, to make sure content would be indexed and visible in time. We suggested this could also be timed with a paid campaign, to really maximise the peak of interest.

All the data we collected was stored in a database, which we added a front-end application to so that the team at could take it away and arm their internal content team, without needing expert knowledge of SQL and Python.

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