AI Visibility in Türkiye: 3 Pilot Benchmark Case Studies

In Türkiye, consumers no longer rely only on traditional search results when researching products, comparing options, and deciding what to buy. AI-generated answers can now summarise products within a specific budget, suggest supermarket options, or explain the differences between two brands directly in the response.

If a brand does not appear in those answers, it may be excluded from consideration before any traffic even reaches the product page.

That is why the key question for FMCG, retail, and e-commerce teams is no longer just, “What position do we rank in search?” It is now, “Does our brand appear in a realistic shopping prompt from Türkiye, and which source is the answer based on?”

The three pilots below were designed to give a practical answer to the question: how do you build an AI visibility benchmark? These examples are repeatable test scenarios that can be applied across sectors. The figures should not be read as real client performance results, but as pilot records that demonstrate how measurement can be designed.

What is a benchmark, and how should it be read?

A benchmark is a way of comparing performance against a defined starting standard. In AI visibility, that standard is created through a fixed prompt set, chosen platforms, country and language settings, date range, and scoring rules. The goal is not to treat one answer as proof of success, but to create a signal that can be compared over time when the test is repeated.

A simple measurement flow looks like this:

• Select 20 to 50 prompts that reflect shopping intent in Türkiye.
• Record brand mentions, product visibility, and source links for each prompt.
• Take a baseline measurement before any content changes.
• Update product pages, category copy, or help content.
• Re-run the same prompts under the same platform conditions.
• Compare mention rate, citation rate, source relevance, and answer accuracy.

A mention means the brand appears in the response text. A citation means the response shows a source link supporting the brand or product. A brand may be used as a source without being named directly in the answer.

That is why reading a benchmark is not just about total citation count. You also need to check whether the source points to the correct product, the current page, and the specific claim being made.

Pilot 1: Clearer product evidence on FMCG product pages

Before:
Run 20 prompts for a snack or cleaning product, such as “good options for daily use in Türkiye” or “best value-for-money products.” At baseline, the product may be mentioned in some answers, but it does not receive a citation, or the source points only to a general brand page.

Change:
Add short, verifiable information blocks to the product page. Clearly state intended use, size or weight, ingredient or composition details, certifications, suitability information, and the consumer need the product addresses. Make sure every claim is supported by an up-to-date, accessible source.

After:
Run the same 20 prompts again. In the pilot, compare three metrics:

• brand mention rate
• citation rate to the product page
• product identification accuracy

Published benchmark work has suggested that a sample citation rate of 12% on basic product pages, followed by a 6-point improvement within 90 days after focused content work, can be a realistic target range. That does not mean the same result is guaranteed for every brand in Türkiye; it simply provides context for goal-setting.

Practical takeaway:
For FMCG teams, short product evidence that AI can easily verify is often more valuable than long brand storytelling.

Pilot 2: Visibility in retail comparisons

Before:
Test short, high-intent prompts such as “Which supermarket options are better for family shopping in Istanbul?” or “How do these two products compare in category X?” Even if the brand appears in the answer, pricing, availability, or product differences may be explained incorrectly or incompletely.

Change:
Add direct comparison sections to category and product pages. Make it clear that pricing can change over time. Present stock status, delivery region, pack size, and product features in separate, easy-to-read fields. Instead of vague language, specify the Türkiye store and delivery context clearly.

After:
Across 20 comparison prompts, compare:

• brand visibility rate
• correct category match rate
• number of citations to the relevant retail page

Even if improved content leads to stronger results, reporting should still reflect platform, location, stock conditions, and prompt wording. Because AI systems can rely on different sources across platforms, no single output should be treated as a permanent ranking.

Practical takeaway:
Retail teams should support comparison moments not only with category pages, but also with proof blocks that quickly explain product differences.

Pilot 3: Citation quality in e-commerce help content

Before:
Test 20 help and FAQ prompts such as “Who is this product suitable for?”, “How is it used?”, or “How does the return or delivery process work?” The brand may appear in some responses, but the citation may lead to the homepage, an outdated help article, or a page that does not directly explain the product.

Change:
Reorganise FAQs around real customer questions. Under each heading, provide a one-sentence direct answer first, followed by any necessary detail. Clearly state delivery regions, return terms, usage limitations, and product suitability, along with the latest update date.

After:
Track not only citation count, but also citation quality. Score whether the source:

• explains the correct product
• genuinely supports the claim being made
• takes the user to a page that helps with the purchase decision

Benchmark findings have also shown that user-generated content and community signals can play an important role in AI recommendations. That means review themes and verifiable customer evidence should be managed as part of the overall product experience.

Practical takeaway:
E-commerce help content is not just there to reduce support costs. When structured well, it can also help AI explain a product more reliably in shopping-related answers.

The shared lesson across all three pilots

The common pattern across these benchmarking examples in Türkiye is clear: clarity, comparability, and verifiability.

Short AI shopping and comparison answers are more likely to draw from pages that contain:

• clear product information
• direct answers
• reliable supporting sources

That said, content changes alone do not guarantee success. Prompt set, model, country setting, date, stock conditions, and source quality all need to be tracked together.

For teams in Türkiye, a practical starting point is to benchmark 10 high-priority product or category pages first. A measurement window of four to eight weeks can help show which prompts respond to content changes and where improvements are actually visible.

Most importantly, AI visibility should not be read as a traffic metric alone. It is better understood as a signal of discovery, evaluation, and source credibility in the modern shopping journey.