Generative engine optimization (GEO) is the practice of structuring content so it gets surfaced and cited inside AI-generated answers from engines like ChatGPT, Gemini, and Claude. User-generated content (UGC) — ratings, reviews, questions and answers, photos, and videos created by real customers — has quickly become one of the most powerful inputs for GEO, because AI systems prioritize authentic, trustworthy content when they build answers.
To understand why, we spoke with Tamara Sanderson, Lead Client Success Manager at Bazaarvoice, who has spent the last six years advising brands on content strategy from her base in London. Bazaarvoice began two decades ago as a ratings and reviews company in Austin, Texas, and — after a series of acquisitions — has grown into one of the world’s largest user-generated content platforms. Below, she breaks down what’s changing in AI search, the framework her team uses to measure GEO readiness, and the low-hanging fruit most brands are still missing.
The key takeaway: AI search now has two customers
The single biggest mental shift, according to Sanderson, is that brands are no longer optimizing content for one audience.
“At the moment, what we see is that there are two customers. We have shoppers and then AI. So we need to speak to both of them and help them with that validation experience and that process.”
In practical terms, that means every review, Q&A thread, and customer photo has to do double duty: reassure a human shopper who is weighing a purchase, and feed a large language model the authentic signals it needs to cite your brand. “If your content is not structured in a proper way, then it won’t be surfaced in AI search,” Sanderson warns — “which then allows you to not be visible for customers to discover you as a brand.”
This is where UGC has a structural advantage. LLMs are actively prioritizing authentic, trustworthy content, and, as Sanderson puts it, “there’s no better trustworthy content than actual customer feedback.”
Why user-generated content is built for GEO
Three properties make UGC uniquely suited to generative engine optimization:
- Authenticity at the source. AI bots are prioritizing customer feedback and authentic content precisely because it is harder to fake than polished brand copy. Ratings, reviews, and Q&A come directly from buyers, which is exactly the kind of validation both shoppers and AI models look for.
- Cost efficiency and scale. “User-generated content is cheaper to create,” Sanderson notes, “and it’s also a priority for AI chatbots and LLMs, as well as for customers.” That combination — low production cost, high citation value — is rare in content marketing.
- A regulatory tailwind. Because authentic content is increasingly governed by regulations such as GDPR, the EU Omnibus Directive, and FTC guidelines, brands that invest in genuine, verifiable customer feedback are also future-proofing against compliance risk.
The result is more emphasis on UGC than ever before. “Because AI search is prioritizing user feedback,” Sanderson says, “there’s more focus put on UGC now than ever before.”
The three KPIs of a GEO-ready content strategy
Bazaarvoice measures GEO readiness against three KPIs. They form a useful checklist for any brand auditing its own AI search visibility.
| KPI | What it means | Why it matters for AI search |
|---|---|---|
| Accessibility | Your content is crawlable for LLMs in AI search | If AI engines can’t crawl it, they can’t cite it |
| Abundance | High-quality content produced at volume and distributed across touchpoints | AI search is “a numbers game” — more authentic signals improve the odds of being surfaced |
| Authenticity | Genuine customer feedback that meets regulatory standards | LLMs prioritize trustworthy, verifiable content over promotional copy |
On accessibility, the point is blunt: content that isn’t structured properly simply won’t appear in AI answers. On abundance, Sanderson describes it as scaling quality “across as many touchpoints as possible within the customer journey.” And on authenticity, the emphasis on real customer feedback is reinforced by the regulatory environment brands already operate in.
For reviews specifically, she frames the same idea around quality, recency, and quantity: “It’s all about abundance and making sure that that content is accessible.”
The GEO audit: scoring how you show up in AI search
To turn these principles into action, Bazaarvoice runs what Sanderson calls a GEO audit — an AI audit of a specific product category that answers a simple question: how visible are you inside AI search, and what’s holding you back?
“It’s an AI audit where we can take a look at a specific product category and review what is the GEO score. How do you show up from a visibility point of view, and what do you need to do? What are the missing gaps in content quality?”
A GEO audit typically surfaces three kinds of gaps:
- Missing claims and one-sided content. A product page might lean entirely on emotional appeal — “this product smells amazing” — while ignoring the practical details shoppers (and AI models) actually search for.
- Content quality gaps across the category, where competitors provide richer, more structured signals.
- Search-query blind spots. Much like SEO keyword research, the audit examines “what people are putting into AI search” so brands can align their content with real AI-driven demand.
That last point is worth emphasizing: GEO isn’t a departure from SEO fundamentals so much as an extension of them. The discipline of understanding query intent carries directly over from classic search into answer engines.
The most common mistake: working in silos
Ask Sanderson where brands lose the most ground, and the answer isn’t technical — it’s organizational.
“Companies still work in silos. So you have marketing and social and e-commerce, and they’re not speaking to each other, so they’re creating different types of content.”
When teams don’t coordinate, they produce fragmented, inconsistent content that dilutes the authentic signals AI engines are looking for. The fix pairs neatly with a second piece of advice: prioritize ruthlessly.
“You can’t do a thousand things. So where can we double down, and how can we create quality content for the most important products?”
For most brands, the highest-leverage move is not producing more content everywhere, but concentrating quality UGC around the handful of products that matter most — then making sure it’s accessible, abundant, and authentic.
Which AI engines should brands optimize for?
Sanderson names three engines where Bazaarvoice sees the most customer activity today: ChatGPT, Gemini, and Claude. These are the AI search channels where brand visibility is increasingly won or lost, and they’re the natural starting point for any GEO measurement program.
How to get started with GEO for UGC
Pulling Sanderson’s guidance together, here’s a practical sequence for brands beginning their generative engine optimization work:
- Run a GEO audit on your most important product category to establish a baseline visibility score and identify content gaps.
- Fix accessibility first — ensure your UGC is structured and crawlable so LLMs can actually surface it.
- Break down silos between marketing, social, and e-commerce so content signals are consistent.
- Double down on priority products rather than spreading effort thin across the catalog.
- Scale authentic UGC — reviews, Q&A, photos, and video — with attention to quality, recency, and quantity.
- Track visibility across ChatGPT, Gemini, and Claude so you can see where you’re cited and where you’re invisible.
Tamara Sanderson is Lead Client Success Manager at Bazaarvoice, based in London. You can find her on LinkedIn under Tamara Brzozowski. This article is based on an interview conducted by Otterly.ai.
