2026_SommerhalderChrisMordasiniElio_FRAISA_PMS
AI search engines such as ChatGPT, Microsoft Copilot, and Google Gemini answer buyers' questions directly. Brands that are not cited in these answers risk disappearing from the shortlist. This thesis shows how FRAISA can measure and strengthen its visibility in AI-generated search results.
Sommerhalder, Chris;Mordasini, Elio, 2026
Type of Thesis Bachelor Thesis
Client FRAISA SA
Supervisor Fenner, Johannes
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Generative AI search engines are replacing ranked links with synthesized, citation-backed answers. For FRAISA, a Swiss precision tool manufacturer whose European B2B customers increasingly begin their supplier search in these environments, absence from AI-generated answers risks exclusion from the buyer's consideration set at the earliest stage. FRAISA's current digital visibility strategy focuses on traditional SEO and social media, leaving the emerging domain of Generative Engine Optimization (GEO) unaddressed.
Following a qualitative, exploratory design, the study combines a literature review with a persona-based prompt simulation along the industrial procurement process, a comparative market study of GEO-analytical tools, and a GEO competitive analysis of FRAISA against key European competitors across ChatGPT, Microsoft Copilot, and Google Gemini, using Peec AI as monitoring platform. The findings were prioritized together with FRAISA representatives in a collaborative workshop and later translated into a structured GEO process, which was refined based on FRAISA's feedback for operational use.
The analysis shows that FRAISA is already well positioned where the brand is known: it ranks among the most visible brands in AI-generated responses and leads its competitor set across several key visibility metrics. In problem-oriented, awareness-phase queries, however, where potential customers search for solutions without prior brand knowledge, FRAISA remains largely invisible, revealing a decisive gap at the very start of the procurement process. Based on these findings, a catalogue of fifteen on-page and off-page measures was developed and translated into a structured GEO process operating at three rhythms: monthly monitoring, quarterly optimization and validation, and annual scaling and reflection. The catalogue of measures defines what to improve, and the governed, recurring GEO process provides FRAISA with an actionable mechanism to implement, measure, and scale these improvements as AI search behavior continues to evolve.
Studyprogram: Business Administration International Management (Bachelor)
Keywords AI, GEO, SEO, Search Engines, LLM, B2B, EEAT, Customer Journey, Search Behavior
Confidentiality: vertraulich