This post was sponsored by Uberall. The opinions expressed in this article are the sponsor’s own.
Local consumers have stopped searching the way we built our marketing around.
This significant change in buyer habits has been quietly happening in the last 18 to 24 months.
According to recent Uberall research into AI search behavior, an estimated $750 billion in consumer spend is already shifting toward AI-powered search. Roughly 60% of all searches now end without a single click to a website. And in a finding that should stop every marketer cold, or at least those working for multi-location businesses, 68% of brands are missing entirely from the recommendations AI engines generate in their category.
That problem goes beyond channels. It’s a fast-moving visibility problem that risks affecting conversions and revenue.
Generative Engine Optimization (GEO) is the discipline built for this moment. Where SEO optimized pages for a ranking, GEO optimizes entities for a recommendation.
The goal is no longer just to be found in Search Engine Results Pages (SERPs). It’s to be cited, summarized, and trusted when a model answers on your customer’s behalf.
In GEO, three pillars carry the weight. If you’ve worked in SEO for any length of time, the shape will look familiar — compounding visibility isn’t new, it’s the surface that’s changed.
- Source of truth. The basic facts about your brand (name, address, hours, services) need to match everywhere a model might look. Inconsistent signals train AI engines to trust you less.
- Context engineering. Your content has to answer the questions customers actually ask, in the language they ask them. Of course, conversational answers should take priority over keyword clusters.
- Orchestration. You measure citations, refresh content, and compound visibility over time.
Here is how those three pillars translate into a realistic 90-day plan teams can actually run.
Phase 1 (Week 1): Foundational Analysis
You cannot optimize what the model cannot parse. The first week is a data hygiene sprint, rather than a content sprint.
Start with the local SEO basics most teams assume are already clean:
- Audit your NAP details (Name, Address, Phone) across Google Business Profiles, Apple Maps, Yelp, Bing Places, and the major data aggregators. Even small inconsistencies — a missing suite number, an old phone format, a rebrand that never propagated — train AI engines to treat your brand as a lower-confidence entity.
- Check your location pages, about page, and product pages for structured data. Schema isn’t a magic AI switch — recent tests suggest LLMs largely read it like any other on-page text. What it does is reduce ambiguity about what your business is and does, and that clarity is what helps a model interpret and cite you correctly.
- Type the questions your customers actually ask into ChatGPT, Gemini, Perplexity, and Google AI Overviews. Not branded queries – real ones like “best orthodontist near Lincoln Park,” “which EV charger works with a Ford Lightning,” “coffee shops in Berlin that allow dogs.” Note where you appear, where you don’t, and which competitors show up instead.
That gap list becomes your brief for the next 80 days. It’s also where most brands discover the blind spots they didn’t know they had.
Phase 2 (Days 7–30): Context Engineering And Targeted Content
Once you know which prompts you’re missing from, the work becomes specific. For each blind spot, you are building the content a model would actively want to cite.
A few patterns that hold up across industries:
- One prompt, one page. If “best family dentist in Austin with Saturday hours” returns three competitors and none of your locations, build or optimize the pages that answer exactly that. Don’t bury the answer three scrolls down.
- Write for the question, not the keyword. AI engines extract complete answers, not phrases. A well-structured FAQ with direct, factual responses often outperforms a 2,000-word, keyword-stuffed guide that dances around the point
- Cite yourself credibly. Include dates, local details, original data, named authors, and explicit comparisons. Models reward specificity and downgrade vague claims.
This is the phase where content that actually gets cited starts to look different from content built for the old ranking game. It is tighter, more factual, and structured around how someone would ask a question out loud.
Phase 3 (Days 30–60): Surgical Placement & Off-Page Authority
Off-page authority still matters. The economics, however, have flipped.
The instinct is to chase top-tier publishers. For GEO, that is usually the wrong move.
The sites that generative engines pull from most often aren’t always the ones with the highest domain authority. These are the ones relevant to your business and are cited more frequently, even if they’re not huge publications.
A more effective approach:
- Focus on sites that already rank in Google for the prompts your customers use — the kind of credible, topical sources you’d want them to find when they’re researching. Top-tier placement isn’t the goal; any authoritative site that actually serves your audience counts.
- The publishers AI engines already cite in your category are the ones models trust enough to source from. Re-run your Phase 1 prompts, track which domains keep appearing in the citations, and that’s your shortlist.
- Size and prestige aren’t reliable proxies for AI citation rates. A specialist publication with real topical authority in your category often earns more AI citations than a bigger, more generic name.
The goal isn’t link volume. It is being mentioned, in context, in the sources your category’s models already trust.
Phase 4 (Days 60–90): Orchestration And Compounding
By day 60, you should have new content live, citations starting to show up on publisher sites, and enough signal to measure. Phase 4 is where GEO stops being a project and starts being a system.
Three metrics worth tracking weekly:
- AI citation rate — how often your brand is named in AI-generated answers for your priority prompts.
- Share of Voice — your citation rate relative to competitors across the same prompt set.
- Content decay — which cited pages are losing citations over time and need refreshing with new data, dates, or insights.
The compounding effect here is profound. Brands that treat GEO as an ongoing loop — audit, publish, place, measure, refresh — see substantially higher citations and conversion rates. A recent Search Engine Journal webinar, featuring Uberall with AthenaHQ, states that GEO-savvy brands see 2x as many citations and 3–9x higher conversion rates within 90 days compared to brands still optimizing purely for classic search.
That delta matters more than it looks. As zero-click behavior grows, the citation inside the AI answer is the conversion surface.
For a concrete example, Audika France, a multi-location hearing-care brand and Uberall customer, ran this orchestration loop as an early adopter. They used it to track how AI engines described their clinics, spot the attributes models were missing, and close the gap between visible and recommended. Their results show how one multi-location brand went from an AI blind spot to a consistent recommendation.
What To Do Next
The pattern is consistent across multiple industries, including retail and restaurants. Brands that start now build a structural advantage that is hard to unwind once the category catches up. The ones that wait end up explaining to their board a year from now why a competitor became the default recommendation in every model their customers use.
If you want a snapshot of how your locations are performing in AI search, check out our AI Visibility Grader tool. It gives you a quick view of your AI visibility and the factors shaping it.
Or if you want to take this further and get a higher definition picture of where you stand in AI search, GEO Studio’s free trial will map your brand’s presence across the major generative engines.
Local search has changed. This is how you become the default answer.
Image Credits
Featured Image: Image by Michelle Azar/ Uberall. Used with permission.
In-Post Image: Image by Uberall. Used with permission.
