Electrolux Case Study

Electrolux Dayanıklı Tüketim Mamulleri Sanayi ve Ticaret A.Ş., the group's legal entity in Türkiye, worked with Webtures on a programme that treated organic search and generative AI visibility as one problem. In home appliances the peak moment for search is not before purchase but when an error code appears on the display. The work covered every surface from pre-purchase comparison to after-sales support.

Why Electrolux needed SEO and GEO support

Five structural challenges shaped the brief.

  • Scale multiplied by intent. Hundreds of product codes each generate error code, spare part, manual and energy label intent at once. Filling that matrix with thin pages risks scaled content abuse; leaving it empty hands the answer to third parties.
  • A blurred brand architecture. The AEG and Zanussi domains redirect to electrolux.com.tr, yet the destination has no section for either, so who owns AEG in Türkiye is not answered from the brand's own source. Electrolux Professional AB, a separate listed company since 2020, adds a second conflation risk.
  • Inconsistent local data. The official site lists an Altunizade address, while a payment surface carrying the brand name shows a different spelling, address and phone. That degrades local results and entity trust.
  • Regulation setting the limits of content. Turkish energy labelling regulation redefined the scale from A to G, and an A+++ display is now misleading. After-sales regulation requires authorised service data to be filed with the Ministry's service information system within 30 days. The advertising amendment effective 1 August 2026 covers targeted and AI-generated ads and influencer content.
  • Global and country versions competing. Where language and country signals are wrong, a model not sold in Türkiye can surface as the answer.

Goals set for the Electrolux project

The programme aimed to bring the brand's own sources forward on support queries, make the Turkish legal entity and brand portfolio machine-readable, bring service data into consistency, cover product demand at scale without thin pages, and strengthen presence in AI-assisted search. Claims about efficiency or savings entered only where they could be evidenced.

How Webtures approached SEO and GEO for Electrolux

Webtures combined technical SEO, content architecture and generative visibility. There is no separate mechanism for generative answers: Google states that AI Overviews and AI Mode need no special markup, and the precondition is being indexable and snippet-eligible.

Technical SEO and AI accessibility

Crawlability and parsability for AI systems were reviewed; two items dominated. The first was language and country signals between the global and Turkish sites: a wrong match sends a reader to a product not sold here. The second was format: manuals, error code tables and warranty terms that exist only as PDFs cannot be quoted in a generative answer.

Content and semantic authority

Content was developed around the questions people put to search engines and AI assistants, which arrive at two levels of urgency. At the decision stage they ask what an energy class means, how built-in ovens differ, how long the warranty runs. At the point of failure they ask what an error code means, usually on a phone in front of the machine. In both cases the first sentence had to answer. Energy class content followed the rescaled A to G system, with older displays audited out. Depth was invested at family level; product code pages had to carry differentiating information, not a repeated template.

GEO and AI visibility

Work targeted presence across ChatGPT, Gemini, Perplexity and Google AI Overviews. The question set was split three ways: support, comparison and brand architecture. The third group was isolated because accuracy on portfolio and entity questions is a matter of trust, not ranking. Uncited questions became content gaps. Complaint and comparison platforms on brand queries were monitored too; Google's quality rater guidelines state that reputation is assessed from independent sources, which makes official support content a direct trust signal. Measurement relied on Search Console's Generative AI performance report.

Experience and conversion

Most conversion here is not a purchase but a support action: a service request, a manual download, a spare part lookup. Those surfaces were mobile-first, and the authorised service directory was built as an information layer the regulation itself expects.

What was carried out in the Electrolux project

Main workstreams across technical SEO, content, structured data and GEO:

  • Crawl and indexation audit
  • HTML counterparts for support documents
  • Brand portfolio and entity relationships clarified through structured data
  • Consistency work across service data
  • Energy label content reviewed against regulation
  • Question-set monitoring across generative surfaces
  • Page experience and Core Web Vitals improvements

What the SEO and GEO work delivered

Three layers were tracked: organic performance on support and decision queries, service-led local visibility, and citation in generative answers.

Organic traffic and search visibility

Coverage widened across support, manual and energy label queries, and the document layer was audited for HTML counterparts. Search visibility was reported by query intent rather than volume.

Visibility across AI platforms

Generative answers to the question set were monitored, tracking separately where the brand was cited and where it was absent. Accuracy on portfolio and entity questions was its own check: an error there sends a reader to the wrong company or product.

The outcome of the Electrolux and Webtures partnership

In a category where the critical search moment is a breakdown, not a purchase, durable visibility is held not by a one-off optimisation but by a content discipline that keeps support information accessible and an update cycle that keeps it compliant.

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