New insights from the Turkish AI firm Citelens underscore the critical role of language-specific strategies for brands aiming to maintain visibility across different linguistic landscapes on Google’s AI Overviews. Their research, which delved into 444 business-related inquiries in both Turkish and English, discovered a striking disparity in the sources Google’s AI references when addressing identical questions in the two languages. This disparity highlights that a brand’s prominence in one linguistic market doesn’t necessarily translate to another.
In examining the overlap of domains cited by Google’s AI, the study found that a mere 22% of the sources appeared in both Turkish and English responses. This suggests that brands need to adopt tailored strategies for each language to ensure their digital presence is effectively captured by AI systems. The research also revealed that AI Overviews are generated for 96% of English queries compared to 94% for Turkish, pointing to a slight difference in response frequency but a significant variation in source selection based on language.
Citelens’s findings emphasize that businesses looking to penetrate the Turkish market cannot rely solely on their English-language digital assets. Instead, they must focus on local content creation, engage with regional authority sources, and develop language-specific optimization strategies. As AI platforms increasingly determine which data to highlight, these aspects are becoming crucial for brands seeking to enhance their visibility in Turkish AI results.
To effectively track and improve AI visibility in Turkey, the study suggests that businesses should focus on performance metrics within Turkish search environments rather than using English results as a benchmark. Citelens advocates for a distinct approach to Generative Engine Optimization (GEO) tailored to each language and market, which is vital for businesses aiming to establish a robust presence across different regions.
The research methodology involved comparing AI-generated responses under various country and language settings, with an analysis of source domains from a multitude of queries. This highlights the growing need for localized strategies as AI visibility becomes a more nuanced challenge, requiring businesses to adapt their approaches to cater to diverse linguistic and regional contexts effectively.
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