This article was sponsored by Uberall. The opinions expressed in this article are those of the sponsor.
Local consumers stopped looking for the way we built our marketing.
This significant shift in buyer habits has occurred quietly over the past 18 to 24 months.
According to a recent Uberall study on AI search behavior, it is estimated that consumer spending already amounts to $750 billion on AI-powered search. Roughly 60% of all searches now end without a single click to a website. And in a discovery that should stop every marketer, or at least those who work for multi-location companies, in their tracks, 68% brands are completely absent from the recommendations generated by AI engines in their category.
This problem goes beyond channels. This is a rapidly evolving viewability issue that has the potential to impact conversions and revenue.
Generative Engine Optimization (GEO) is the discipline built for this moment. Where SEO optimizes pages for ranking, GEO optimizes entities for recommendation.
The goal is no longer just to be found in search engine results pages (SERPs). It must be cited, summarized and reliable when a model responds to your client’s name.
In GEO, three pillars carry the weight. If you’ve been working in SEO for a while, the form will look familiar: compound visibility isn’t new, it’s the surface area that’s changed.
- Source of truth. Basic information about your brand (name, address, hours, services) should match wherever a pattern might appear. Inconsistent signals cause AI engines to trust you less.
- Context engineering. Your content should answer the questions customers actually ask, in the language they ask them. Of course, conversational responses should take priority over keyword groups.
- Orchestration. You measure citations, refresh content, and improve visibility over time.
Here’s how these three pillars translate into one realistic 90 day plan teams can actually race.
Phase 1 (week 1): fundamental analysis
You can’t optimize what the model can’t analyze. The first week is a data hygiene sprint, rather than a content sprint.
Start with the local SEO basics that most teams consider already clear:
- Audit your PAN Details (Name, Address, Phone) in Google Business Profiles, Apple Maps, Yelp, Bing Places and major data aggregators. Even small inconsistencies (a missing suite number, an old phone format, new branding that never caught on) cause AI engines to treat your brand as an entity with less trust.
- Check your location pages, about page, and product pages for structured data. The diagram is not a magic AI switch: recent tests suggest that LLMs read it largely like any other text on the page. This reduces ambiguity about what your business is and does, and this clarity is what helps a model interpret and quote you correctly.
- Type the questions your customers actually ask into ChatGPT, Gemini, Perplexity, and Google AI previews. Non-branded queries – real queries like “best orthodontist near Lincoln Park”, “which electric vehicle charger works with a Ford Lightning”, “dog friendly cafes in Berlin”. Note where you appear, where you don’t, and which competitors appear instead.
This list of gaps becomes your memory for the next 80 days. This is also where most brands discover blind spots they didn’t know existed.
Phase 2 (days 7 to 30): context engineering and targeted content
Once you know which prompts you’re missing, the work becomes specific. For each blind spot, you create content that a model would actively want to cite.
Some models that persist in all sectors:
- One prompt, one page. If “best family dentist in Austin with Saturday hours” returns three competitors and none of your locations, create or optimize pages that answer that exact question. Don’t bury the answer three times down.
- Write for the question, not the keyword. AI engines extract complete answers, not sentences. A Well-structured FAQ with direct, factual answers often outperform a 2,000-word, keyword-filled guide that circles around the point
- Cite yourself credibly. Include dates, local details, original data, named authors, and explicit comparisons. Models reward specificity and devalue vague statements.
This is the phase where the actual cited content starts to look different from the content designed for the old ranking game. It’s more specific, more factual, and structured around how someone would ask a question out loud.
Phase 3 (days 30 to 60): surgical placement and off-page authority
Off-Page Authority still counts. However, the economic situation has reversed itself.
The instinct is to chase away top-tier publishers. For GEO, this is generally a bad decision.
The sites where generative engines most often come from are not always the ones with the highest domain authority. These are the ones that are relevant to your business and are cited more frequently, even if they are not large publications.
A more effective approach:
- Focus on sites that already rank in Google for prompts your customers use – the type of credible, timely sources you’d want them to find when searching. Placement at the first level is not the objective; any authoritative site that actually serves your audience counts.
- The AI engines from the vendors already listed in your category are the ones that models trust enough to source from. Run your Phase 1 prompts again, follow the areas that continue to appear in citations, and there you have your shortlist.
- Size and prestige are not reliable indicators of AI citation rates. A specialist publication with real topical authority in your category often gets more AI citations than a larger, more generic name.
The goal is not link volume. It is mentioned, in context, in sources that models in your category already trust.
Phase 4 (days 60 to 90): orchestration and composition
On day 60you should have new content online, citations starting to appear on publisher sites, and enough signal to measure. Phase 4 This is where GEO stops being a project and starts being a system.
Three steps to follow each week:
- AI citation rate — how often your brand is named in AI-generated responses for your priority prompts.
- Share of voice – your citation rate compared to your competitors in the same set of prompts.
- Content degradation – which cited pages lose citations over time and need to be updated with new data, dates or information.
The cumulative effect here is profound. Brands that treat GEO as a continuous loop – audit, publish, place, measure, refresh – see significantly higher citations and conversion rates. A recent Search Engine Journal Webinarfeaturing Uberall with AthenaHQ, states that GEO-conscious brands see 2x more citations and 3-9x higher conversion rates in 90 days compared to brands that still optimize for traditional search only.
This delta matters more than it seems. As no-click behavior grows, the quote inside the AI response is the conversion surface.
For a concrete example, Audika France, a multi-location hearing care brand and Uberall customer, managed this orchestration loop as an early adopter. They used it to track how AI engines described their clinics, spot missing attribute patterns, and close the gap between visible and recommended. Their results show how a multi-location brand went from an AI blind spot to consistent recommendation.
What to do next
This trend is consistent across several industries, including retail and restaurants. Brands launching today are building a structural advantage that is difficult to lose once the category has caught up. Those who wait end up explaining to their board a year from now why a competitor has become the default recommendation for every model their customers use.
If you want a snapshot of how your locations perform in AI searchcheck out our AI Visibility Grader tool. It gives you a quick overview of your AI visibility and the factors that shape it.
Or if you want to go further and obtain a better definition image of where you are in AI researchGEO Studio’s free trial will map your brand’s presence across key generative engines.
Local search has changed. This is how you become the default response.
Image credits
Featured image: Image by Michelle Azar/Uberall. Used with permission.
In-Post Image: Image from Uberall. Used with permission.
