Question 1

What exactly does the European Visibility Index (EVI) measure?

Accepted Answer

We measure your “share of model.” That means figuring out exactly how much authority your company commands inside AI engines like ChatGPT, Perplexity and Gemini. AI doesn't just list links. It actively recommends vendors and partners. We quantify how often and how favorably your company surfaces when decision-makers ask these engines about your industry. The resulting score runs from 0 to 100. It evaluates your corporate reputation across five dimensions: market leadership, trust, innovation, employer branding and sustainability. The output is an independent, industry-adjusted benchmark.

Question 2

Why does the EVI evaluate visibility across these specific five dimensions (market leadership, trust, innovation, employer branding, sustainability)?

Accepted Answer

AI models don't evaluate businesses in a vacuum. They reflect the information you feed into the market. We track these five dimensions because they cover both immediate commercial success and long-term survival. Market leadership and trust are your foundation. If an AI doesn't know your market share or whether you are reliable, you lose deals today. Future readiness relies on the rest. You need technical innovation to stay relevant, strong employer branding to attract talent, and clear sustainability commitments to meet ESG demands. Evaluating all five shifts the EVI from a basic mention-counter to a practical diagnostic tool. It shows executives exactly where their narrative is landing and where a blind spot is quietly costing them authority.

Question 3

How is the Visibility Score calculated?

Accepted Answer

The math comes down to three components. Industry relevance drives 90% of your score. We average your performance across the five dimensions by tracking both mention frequency and position rank. National relevance makes up another 10%. This tracks whether your brand surfaces in broad queries about Germany's top corporations. We also include a cross-industry bonus. Companies can earn up to 6 extra points if the AI recommends their expertise outside their core sector. Everything is normalized to the 0–100 scale so you can track progress and benchmark against peers.

Question 4

How does the index prevent bias, especially against B2B companies or mid-sized market leaders?

Accepted Answer

Left unchecked, AI models default to DAX and consumer brands simply because they generate massive amounts of internet noise. We force the AI into a strict industry-by-industry logic to fix this. A highly specialized B2B software company is never judged against a global automaker. It competes exclusively against its direct peers in the IT and software category. This lets a specialized hidden champion score a 95 by dominating its specific niche.

Question 5

Which factors actually influence my company's position in the index?

Accepted Answer

It comes down to how clearly you are documented and what you are known for. AI models rely on structured, clean data. If your company has a well-maintained Wikipedia page, active Wikidata entries and consistent coverage in respected trade publications, the AI can retrieve your facts without guessing. Beyond basic data hygiene, the AI needs to associate your brand with macro-level concepts. Being a known manufacturer isn't enough to rank highly. The model needs to see clear ties between your brand and concepts like trust or innovation in your specific market. Expanding that reputation into neighboring sectors is what pushes top performers into the highest percentiles.

Question 6

Why does the index rely on a concentrated set of just 10 industry prompts per company? Is that enough data?

Accepted Answer

Tracking thousands of keyword variations makes sense for a traditional search engine. Generative AI requires a completely different testing method because LLMs compile and summarize. When we ask, “Which pharmaceuticals companies are considered the most innovative?”, the model is already evaluating millions of underlying data points to formulate that single answer. We are measuring the AI's final conclusion. We run a standardized matrix of 10 core prompts across 28 industries. This minimizes hallucinations and gives us a clean, noise-free environment to compare companies fairly.

Question 7

Do you separate holding companies from their equally named core brands (like Volkswagen Group vs. Volkswagen passenger cars)?

Accepted Answer

No. We treat them as a single consolidated entity. The EVI measures overall corporate reputation. When we ask an AI about company culture or market leadership, the model doesn't distinguish between a holding company and its core brand of the same name. They share the same narrative. Highly independent subsidiaries with completely distinct brand names (like Porsche or Audi) are tracked separately.

Question 8

If the EVI focuses on corporate reputation, does it reflect how well my specific products or services are doing in AI search?

Accepted Answer

Corporate authority and product success are deeply linked in generative AI. One of our core prompts specifically asks the AI which companies in an industry are most frequently recommended by customers. To answer that, the model pulls directly from real-world product reviews, forums and service ratings across the web. A high corporate EVI score is a strong proxy for product portfolio strength. A solid reputation for trust and innovation makes the AI much more likely to recommend your specific products during deep-dive consumer queries.

Question 9

How can my marketing and PR team optimize our brand for AI search and improve our EVI ranking?

Accepted Answer

Stop obsessing over product keywords and start feeding the AI the structured facts it craves. AI models heavily depend on third-party validation. Look at the “top domains” insights in your EVI dashboard. That tells you exactly which industry magazines, review platforms and associations the AI trusts most in your sector. Earning placements there is critical. On your own channels, make the data machine-readable. Use clean Schema.org markup. Build logically structured “About Us” sections. Post clear, unambiguous statements about your market position. You also need to explicitly tie your corporate PR to broader themes like sustainability or employer branding so the AI learns to associate your company with industry leadership.

Question 10

Why is it called the “European” Visibility Index if it currently focuses on Germany?

Accepted Answer

We started in Germany because it's Europe's largest economy and a remarkably complex market. It has massive global conglomerates operating right alongside highly specialized B2B hidden champions. Establishing a fair, industry-adjusted benchmark here was a rigorous test for the methodology. The foundation works. We will soon expand the index to include other major European markets and pan-European corporations in our upcoming editions.

Question 11

Which industry-specific prompts does the index use and what exactly do they measure?

Accepted Answer

We calculate your core industry relevance using a set of 10 targeted prompts customized for your sector. That means two specific questions per dimension. To measure market leadership, we ask the engines to name the “top 10 companies” and “leading providers” in your space. For innovation, we look for companies “driving technological innovation” and those labeled as “future-oriented.” When assessing trust, we split the concept. We separate emotional reliability (“most trustworthy”) from active customer advocacy (“most frequently recommended by customers”). This focused matrix proves whether you actually hold authority in your niche.

Question 12

Which “National Relevance” prompts does the index use and what is their purpose?

Accepted Answer

National relevance measures your footprint outside your specific industry bubble. We use 10 broad prompts that treat the AI like a general observer of the German economy. We ask it to identify companies crucial to economic stability. We ask for the most attractive employers. We also ask which corporate brands shape the country's economic reputation worldwide. Performing well on these questions rewards companies that have achieved true household-name status or systemic importance across the wider economy.

Question 13

Why does the index only track visibility across ChatGPT, Gemini and Perplexity? What about Claude, Copilot or others?

Accepted Answer

We test the engines people actually use for discovery. Right now, that means the “Big 3”: ChatGPT, Gemini and Perplexity. Models like Claude and Meta's Llama are excellent, but people treat them as utilities. They use them to write code or draft documents. When decision-makers and investors want to research a market or vet a vendor, they open ChatGPT or Perplexity. Gemini is just as critical because it directly feeds Google's AI Overviews. Including models that no one uses for search just creates noise. We want your score to reflect actual market behavior. If the landscape shifts and another engine captures real search intent, we will simply add it to the index.

Question 14

You say the EVI is not a "tool" — what is it then?

Accepted Answer

Marketing tools are tactical software. They are built for the operational tracking of endless, highly specific prompt variations. The EVI is an independent market index. Think of the DAX or the S&P 500. We built the exact same mechanism for corporate AI reputation. C-level executives and communications leaders don't need another dashboard to micromanage individual AI queries. They need a clear baseline of who actually controls the narrative in their industry. You don't log into the EVI to chase granular ranking fluctuations. You use it to benchmark competitors and drive your Generative Engine Optimization (GEO) strategy.

Question 15

How representative are the prompts you use? Is it realistic that a real user would type in exactly: “Which German FMCG companies offer young professionals the best career opportunities?”

Accepted Answer

A 22-year-old applicant will probably type something much messier, like: “I study marketing in Cologne, which cool brands pay well?” Exact-match search volume is irrelevant in generative AI. We test the engine's baseline knowledge. Our prompts act as a direct stress test for the AI's training data. Asking a highly structured question forces the model to scan the entire market. It has to pull together HR rankings, employee reviews and industry discussions to figure out who actually leads the pack. If the AI doesn't recognize your brand as a top employer during this broad market scan, it won't recommend you when a real applicant asks a highly specific query. We measure the core reputation that dictates all of those individual answers.

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