What 261% Organic Growth Taught Us About SEO

The starting point

A multi-venue performing arts business in London. Six locations. 590+ enrolled students. A WordPress website that had been built several years earlier with no SEO consideration. Revenue was tied to paid advertising: Meta ads driving trial class bookings, with no organic acquisition channel.

The business had strong fundamentals (excellent reviews, high retention, genuine expertise in their field) but zero search visibility. Google Search Console showed single-digit clicks per month for non-branded terms. AI engines did not mention the business at all.

What we measured first

Before any work began, we established baselines across four areas:

Search rankings. Current positions for 148 target keywords. The result: zero top-10 rankings for any commercial-intent term. The business was invisible in Google for every query its customers were searching.

Organic traffic. Monthly organic sessions from Google Search Console (not Google Analytics, which conflates channels). The starting figure was negligible.

AI citations. A structured probe across ChatGPT, Perplexity, Claude, and Google AI Overviews with 48 buyer-intent queries. Starting citation rate: 0% across all engines.

Conversion rate. The percentage of website visitors who booked a trial class. Starting rate was below industry average, with the majority of bookings coming through paid ad landing pages rather than the main website.

The three phases

Phase 1: Foundations (months 1 to 3)

The existing WordPress site was replaced with a custom-built website on a modern framework. Every page was designed around search intent and conversion psychology, not just visual appeal.

Technical foundations included:

• Page load time under 2 seconds on mobile (previously over 6 seconds)
• Structured data across every page (Organisation, LocalBusiness, FAQPage, BreadcrumbList)
• Clean URL structure with service-specific pages for each class type and age group
• llms.txt and llms-full.txt deployed from day one

This phase alone moved the business from zero to measurable: Google started indexing pages for relevant terms within weeks.

Phase 2: Content and local authority (months 3 to 6)

With the technical foundation in place, content work began:

• Service pages expanded from 200-word overviews to 1,000+ word comprehensive guides for each class type
• Location-specific pages created for each of the six venues
• Google Business Profile optimised across all locations with correct categories, complete service listings, and regular photo updates
• Blog content targeting parent-intent queries (“dance classes for toddlers near me”, “best dance schools in North London”)
• Review strategy implemented: the business went from sporadic reviews to a steady cadence of 5 to 8 new Google reviews per month

Phase 3: AI visibility and refinement (months 6 to 12)

With rankings improving and organic traffic growing, the focus shifted to AI visibility:

• Content structured specifically for AI citation: comprehensive FAQ sections, clear entity descriptions, specific statistics woven into page content
• Third-party profile consistency ensured across Google, Facebook, Instagram, and industry directories
• Quarterly evidence cycles measuring both Google rankings and AI citation rates
• Ongoing content refinement based on which queries were producing citations and which were not

What moved the needle most

The single biggest factor was not backlinks, not blog content, and not technical SEO in isolation. It was content specificity.

The old website described the business in generic terms: “dance classes for children in North London.” The new website described each class type, each age group, each location, each teaching methodology, and each instructor in specific detail.

This specificity gave Google and AI engines the information they needed to match the business to hundreds of specific queries rather than competing for a handful of generic terms.

The second biggest factor was structured data. The combination of schema markup and llms.txt gave AI engines machine-readable facts about the business. When ChatGPT’s web browsing feature indexed the site, it found structured, unambiguous information that it could confidently include in its recommendations.

The AI visibility result

After 12 months, the structured AI audit showed:

• 92% ChatGPT citation rate (11 of 12 buyer-intent queries, 7 at first-named position)
• 58% Perplexity citation rate
• 50% Claude citation rate
• 60% overall across all four engines

The business went from invisible to being recommended alongside Wikipedia and NHS resources for relevant queries. Full verified data is available on the case studies page.

What we would do differently

Start AI visibility work earlier. We treated AEO as a Phase 3 activity but many of the signals (structured data, llms.txt, entity consistency) could have been implemented in Phase 1 alongside the technical foundations. Starting earlier would have compressed the timeline.

Blog content more aggressively. The blog generated significant long-tail traffic but was ramped slowly. Starting a weekly publishing cadence from month 2 rather than month 4 would have built topical authority faster.

Measure AI citations from day one. We did not establish an AI citation baseline until month 3. Having a day-one baseline would have given us clearer attribution of which specific changes drove citation improvements.

The numbers

These figures are verified from Google Search Console, structured AI probes, and the business’s own pipeline records. They are not estimates, projections, or industry averages.

For the full methodology behind this approach and to see whether it applies to your business, a Visibility Briefing is the starting point.