We grew Watsons’ non-brand organic clicks by 30.6%
We grew Watsons’ non-brand organic clicks by 30.6%
A platform migration had shaken indexing and sent organic performance swinging. We stabilized the technical base, mapped a sprawling category tree to real search intent, and made the brand’s visibility inside AI search something you can actually measure.
THE BRAND
About Watsons
Watsons is one of the widest-reaching health, beauty and personal care chains in Turkey. The category tree runs deep, from skincare and makeup to hair, body, fragrance and supplements, and puts global brands and Watsons’ own labels under one roof.
Stores, the mobile app and e-commerce run as one operation. Organic search sits inside that and touches both the store visit and the online cart.
Beauty moves fast. Shoppers research in search and on social, and they ask about ingredients, skin type and routines long before they settle on a product.
That turns a category page into more than a product list. It has to answer the research question too.
STARTING POINT
What We Walked Into
The issues on the table when the engagement began.
A sprawling information architecture across hundreds of subcategories, with technical debt on top
Indexing instability and performance swings following a platform migration
Trend-driven beauty queries leaving traffic on the table
Crawl budget spread across pages that earn nothing
Heavy competition from chain retailers and marketplaces
No measurement of category-level visibility inside AI search
THE PLAY
What We Did
Five phases, each one feeding the next.
1
Technical stabilization
The platform release changed how page templates behaved and broke critical SEO signals. We tracked indexing behavior, URL signals and render output to isolate every regression, fixed the SSR inconsistencies so crawlers read the right signals again, and rebuilt the sitemap so crawl budget landed on the category and product pages that actually earn.
2
Architecture and internal links
Across a tree hundreds of subcategories wide, we designed logical and contextual paths between categories and built deliberate authority flow between complementary product groups. Topic clusters gave commercially important categories a stronger signal and shoppers a clearer path.
3
Semantic category content
AI-assisted gap analysis mapped the entities around each head term and surfaced what competitors covered and target pages missed: skin type, ingredient, routine, need state. Query fan-out modeling then mapped the follow-up questions a single intent spawns in one session, and those went into micro-content blocks and FAQs on the category pages themselves.
4
Trend coverage
We vectorized the target query set with an embedding model, clustered semantically close queries, and mapped each cluster to the commercial category it could feed. The content calendar followed that priority. Skincare routines, ingredient explainers and K-Beauty pulled top-of-funnel traffic down into commercial pages.
5
AI search visibility
We ran a prompt-tracking framework across ChatGPT, Gemini and Perplexity through Brandmetric, our own visibility tool, measuring how often and in what context the brand surfaced in generative answers for commercially ranked categories. Brandmetric reads the platforms’ real interfaces rather than their APIs, so what gets measured is the answer a shopper actually sees. Findings fed straight back into the entity gaps and FAQs on category pages.
RESULTS
What Changed
| Metric | Change | Comparison |
|---|---|---|
| Non-brand organic clicks | +30.6% | Last 3 months vs. same period last year |
| Non-brand organic impressions | +17.7% | Last 3 months vs. same period last year |
| Non-brand organic clicks | +19.94% | Last 6 months vs. prior 6 months |
| Non-brand organic impressions | +6.14% | Last 6 months vs. prior 6 months |
| Non-brand keywords in top 3 | +34.72% | Same period last year |
| Non-brand keywords on page one | +29.97% | Same period last year |
| Target keyword visibility | 24.51% to 43.25% | Same period last year |
Figures come from the brand’s Google Search Console and the tracked non-brand KPI keyword set. Visibility is measured on that same set; the 18.74-point gain is a 76.5% relative increase.
TAKEAWAY
Content Cannot Compound Until the Technical Base Holds
Content and trend work does not stick while indexing is still unstable after a migration. Run it in the right order and the same keyword set nearly doubles its visibility inside a year.
Base firstInvestment made before post-release regressions are cleared cannot be measured.
Intent over inventoryNon-brand clicks grew once category pages answered research questions instead of listing products.
AI visibility, measuredPresence in generative answers was measured rather than assumed, and fed back into the same roadmap.
IN THEIR WORDS
What Watsons Says
“The partnership we built with the D-Option team through our SEO journey let us line up technical rigor with business goals. They took real ownership of the process, adapted quickly as priorities moved, and kept our teams in sync. That was one of the deciding factors in getting where we are.”
Gonca EnginDigital Marketing Manager, Watsons
Your Story Could Be Next
Let us look at where your visibility stands today and how far it can go.