THE IMPACT OF JSON-LD METADATA ON CHATGPT VISIBILITY

Peter Schanbacher ()
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Peter Schanbacher: Hochschule Furtwangen University, Furtwangen, Germany

Journal of Advance Research in Business, Management and Accounting, 2026, vol. 12, issue 1, 16-23

Abstract: Purpose: This study examines whether implementing structured metadata (JSON-LD Schema.org markup) on websites can improve a business’s visibility in ChatGPT responses. We focus on real estate agencies as a case study, analyzing which site attributes correlate with an agency being “known†or referenced by ChatGPT. Methods: We gathered public data on 1,508 real estate agents in Germany and identified which of these agents ChatGPT could provide information about (indicating ChatGPT visibility). For each agent’s website, we recorded the presence of Schema.org metadata (FAQPage, Organization, Product schemas), as well as SEO/technical factors like mobile optimization, robots.txt, sitemap, headings usage, image alt-text, internal links, and page load speed. A logistic regression was used to determine which factors significantly predict ChatGPT visibility, controlling for other variables. Results: Agents whose websites included FAQPage schema markup were far more likely to be visible on ChatGPT (6.2% of visible agents had FAQ schema vs. only 0.8% of non-visible; p = 0.002). Presence of Product schema (e.g. schema for listings or services) also strongly correlated with visibility (17.2% vs 1.8%; p

Keywords: ChatGPT; Artificial Intelligence; GEO; Generative Engine Optimization; online marketing (search for similar items in EconPapers)
Date: 2026
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DOI: 10.61841/xt3he524

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