+800% organic traffic at the world's largest wedding marketplace

Bridebook had a large editorial operation and was publishing at real volume — but most of it was generic, topically-scattered articles with no clear strategic purpose. Traffic was flat despite the output. The issue wasn't how hard the content team was working; it was that the content wasn't organised around how people actually search for wedding information, and Google had no clear topical authority signal to attach to Bridebook against the long-established competitors in the space.

I stopped the generic-article firehose and rebuilt the content strategy around high-demand wedding topic areas and a disciplined hub-and-spoke model.

• Research-led topic identification — mapped genuine search demand across the wedding lifecycle (planning, budgeting, venues, suppliers, ceremonies, style guides, post- wedding) and ranked topic areas by demand, commercial value, and defensibility.
• Topic bucketing — grouped related queries into a clean hierarchy of clusters rather than treating every article as an island.
• Hub pages — stood up a canonical hub per cluster as the centre of topical authority, with structured internal linking from the hub into semantically-related supporting articles and back.
• Semantic-relevance content model — every article had a clear role inside its cluster, reinforced the hub, and targeted a well-defined intent rather than chasing whatever keyword happened to look interesting that week.
• AI-era article structure — answer-first intros, consistent heading hierarchy, entity-rich supporting copy, and FAQ / how-to structure where the query intent warranted it. Exactly the shape that LLM-powered surfaces reach for when citing.
• Content team training — ongoing programmes with the editorial team on how search engines, and increasingly AI answer surfaces, interpret article structure. Every new piece fitted the model by default, with no SEO-team sign-off required.

The single biggest shift was killing the generic-article volume and concentrating effort around a bounded set of high-demand topic clusters. Once Google had a clear topical centre on Bridebook per cluster, rankings started compounding — the hubs ranked, the supporting articles reinforced the hubs, and the hubs' authority lifted the supporting articles in turn.

The AI Overview wins came on the back of that same work. Clear topic architecture plus disciplined article structure — answer-first intros, clean heading hierarchy, entity-rich supporting content — is precisely what LLM-powered search surfaces want to cite. Flagship wedding queries like "average wedding cost" started landing inside AI Overviews alongside traditional organic rankings.

The content team training was the durability lever. Once the editorial team understood the model at a structural level, they didn't need the SEO team's sign-off to stay on strategy — which is what made the gains compound rather than decay.

+800% YoY organic traffic, scaling from around 11k to 90k+ monthly organic users across 80+ markets. Organic became the dominant acquisition channel and materially reduced paid CAC.

Rankings inside AI Overviews for flagship wedding queries including "average wedding cost" — a structurally-harder surface to rank in than traditional organic, and one most direct competitors hadn't yet broken into.

The cluster + hub architecture, and the trained editorial model behind it, became the template for ongoing content investment — the property you want from an SEO-led growth engine is that it keeps compounding long after launch, and this did.