News & insightsIndustry
In 2026, a growing share of hologram-vendor evaluation starts inside a chat interface. Here's what changed for buyers, vendors, and the definition of "first click."
Published 5 July 2026
For most of the last decade, a B2B buyer researching hologram technology followed a predictable path. Google search on "hologram company" or "hologram display for events." A ranked list of vendor sites. Three or four vendor sites open in tabs, comparing product specs and case studies. A shortlist by end of Friday, an RFP the following week.
That path still exists. But it's no longer the only one, or even the dominant one for early-stage discovery.
In 2026, an increasing share of first-touch vendor evaluation happens inside ChatGPT, Perplexity, and Claude before the buyer ever visits Google. The prompt looks like: "Which company makes life size hologram displays for hotel check-in?" or "How much does a Holobox cost?" or "What's the difference between the Holobox and Proto's Epic display?"
The LLM answers with a mix of description, comparison, and, critically, vendor citations. The buyer then visits one, maybe two vendor sites based on the citation, skipping the Google-ranked list entirely.
For hologram vendors, this changes what "top of funnel" means. Being cited in the LLM answer is now the equivalent of ranking first on Google, but the citation is decided by the LLM's understanding of authority, not by traditional SEO signals.
The citation pattern matters as much as the volume. Users arriving via ChatGPT tend to come with a specific question already narrowed ("do you have deployments in hospitality?", "what's the AI Hologram tier?"), indicating the LLM had already scoped their evaluation before sending them our way.
For vendors, three implications:
First, structured entity data matters more. LLMs weight sameAs identifiers, foundingDate signals, and named-customer references heavily when deciding who to cite for enterprise B2B queries. A vendor with clean structured data outcompetes a vendor with a prettier homepage.
Second, comparison content earns its keep. Pages like /holobox-vs-led-wall, /holobox-vs-proto, /holobox-vs-arht-media exist specifically because "X vs Y" queries are one of the highest-frequency LLM prompts. Every vendor comparison page is a citation opportunity.
Third, direct-answer content wins long tail intent. A question-titled page like /what-does-a-hologram-cost gets cited when a user asks that exact question, often bypassing the vendor's product pages entirely.
LLM citation logic is opaque. There's no analog to Google Search Console. We can measure which chatgpt.com referrals arrive, but we can't see the full universe of queries where the LLM decided not to cite us.
That opacity forces a different content strategy: publish everything a buyer might ask, be honest about vendor comparisons, and structure the content so an LLM can extract a clean answer. What worked for SEO in 2018 doesn't work for GEO in 2026, but the discipline of putting the right answer in front of the right reader survives the platform shift.
