Marketer's Preparedness Guide: How to Influence LLMs and Win Customers

TL;DR: Influencing LLMs is now central to how customers find and choose your brand. The first job is getting AI to surface your brand as an answer, and that matters more than ever: 42.7% of adults used an AI tool for local search in the past month, and among $150k+ households, 54.5% now start their search with AI, ahead of Google. But influence works on two levels. Your brand has to be surfaced, then chosen, and increasingly acted on when an agent transacts on a customer's behalf. This is the playbook for all of it: the foundational moves and the ones the agentic era demands.

The two levels of LLM influence

The first job of influencing LLMs is getting them to surface your brand as an answer. Structure your brand data, keep it consistent, show up. Being absent from AI answers is functionally not existing.

What matters most is the goal of the influence. As Melissa Todisco, Senior Director, Product Marketing at Yext, frames it: "You're not going to control the LLM. But you can control all the endpoints that an LLM is most likely to go to to find information — and then you can also control how trusted that content is, and how it shows up."

There's no fixed playbook for showing up in an LLM and no direct feed that guarantees placement. Ask the same question today and tomorrow and the answer can change. So influence has never been about the model. It's about the sources the model trusts. What's new is that your brand now influences LLMs to do more than talk about it. Your brand influences them to pick it over a competitor, and to act on its behalf, booking or recommending or transacting, when an agent stands between your brand and the customer. Two layers of influence, not one. This is the shift from SEO to AEO and GEO, and it changes what your brand optimizes for.

Build a foundation of verified, consistent data

The foundation is accurate, consistent brand data across everything AI “reads” in order to build an answer. That is how your brand influences what an LLM says about it.

There are three qualities LLMs reward in the content they trust: It's fresh, meaning updated frequently, not a stale page. It's relevant, aligned to what the customer is actually asking. And it's consistent across every place your brand appears. Consistency is where most marketers lose. If your listings say one thing but your website says something different, that's not going to send a very strong signal to an LLM. Whereas if your competitor has those two things in sync, the LLM has to choose who to trust. And it's going to choose the one with the consistent information.

That's why the Knowledge Graph matters. It isn't just where your brand centralizes data. It's the verified record AI cites from and agents act from. And the numbers show doing this well is what decides who gets cited. About 79.9% of AI citations still point to sources brands control, their own websites and listings, though that share slipped from 88.2% a quarter earlier. The brands managing their data deliberately are the ones holding the answer. Adding a verification signal on top compounds the effect: pages with Brand Certified Facts saw a 9.2% lift in Google Gemini citations, a 35% lift on Bing, and a 37% click lift on Yahoo.

Distribute one source of truth everywhere AI reads

It's tempting to treat influence as four separate digital touchpoints: listings, local pages, reviews, and unstructured signals. They're really four ways of saying one thing. Get your brand's verified data everywhere LLMs look. It isn't four things to juggle by hand. It's one system: a single structured source of truth, pushed directly to every surface AI reads.

Why push to all of them? Because no one can predict which one any given LLM will trust. "Not every LLM prioritizes the same endpoints," Melissa says. "Their algorithms might change on a daily basis. And it changes by industry, and it changes for different prompt types." She gives the mechanism in plain terms. A subjective, unbranded question like "what's the best pizza place in Midtown Manhattan?" leans toward reviews. A near-me question like "a noteworthy pizza chain near me" leans toward listings. A brand-level question routes to your brand's website, while a highly local one prefers a detailed local page with real product and service information. Each AI engine weights different sources, so covering one is not covering all.

"Essentially it's a race to say who can be the first one to get the most content out there answering the types of questions that are most likely to be driving foot traffic to your store," she says. Your brand wins that race with one source of truth and direct distribution to 200+ surfaces with no aggregator in between. That now includes a Yext-owned, AI-crawlable directory (Location.com) and direct LLM partnerships. Those touchpoints become outputs of the system, not a checklist. And it moves the needle. Brands whose data is synchronized across the network rank +2.71 positions higher within a mile, and up to +6.20 in the most competitive markets. For multi-location brands, that local edge is the whole ballgame.

Measure your brand's visibility against competitors

Making content “AI-friendly" means nothing without a way to know whether it worked. A brand can't influence what it can't see, and, Melissa argues, monitoring alone is no longer enough.

A year ago it was just about monitoring whether you were showing up. Now it's shifting from monitoring to making sure you can agentically take action on those things. And to take it a step further, you can close the loop and tie it back to the fact that those actions actually delivered ROI and improved your AI visibility.

There are two things most marketers still miss. First, visibility has to be managed at both the brand and the location level, because LLMs pull from very different sources for a brand-reputation question versus a "near me" one. Multi-location brands live in both worlds at once. Second, visibility has to be measured against the competition, not just your brand's past performance. "Marketers are influencing markets that are far more dynamic," Melissa says. "LLMs are taking into consideration what your brand is doing and what all your competitors are doing. If you're not accounting for that, you're operating blind, and you're operating in a vacuum."

That's what Yext Scout is built for. It shows where your brand is winning and losing in AI answers, by location, benchmarked against real competitors: 10B+ signals across 12M+ locations, 150 visibility metrics per scan, 19 competitors per location, across ChatGPT, Gemini, Perplexity, and Claude. This is the competitive intelligence layer that turns influence from a guess into a measured, location-level target. As Melissa puts it, winning visibility means winning foot traffic and market share.

Prepare for AI agents that take action

The newest frontier of influence is getting LLMs to act, not just answer. Agents have moved past finding and recommending your brand. They're starting to do things with it: book the appointment, place the order, add to the cart on a customer's behalf. "The big shift this last year: it used to just be about questions and answers and creating content," Melissa says. "Now we're getting into a world of how you actually help LLMs take action, and how they connect to different sources."

That world is arriving fast. Protocols like MCP and A2A are standardizing how agents connect to brands and carry out tasks, and McKinsey projects up to $1 trillion in US agentic-commerce retail revenue by 2030. Influence no longer stops at the recommendation. It runs all the way into the transaction, and what an agent does for your brand is only as good as the data it acts from.

So the job grows: influence not just what an LLM says about your brand, but how an agent acts for it. The lever is the same verified source of truth. "You need a structured source of truth for agents to be working on," Melissa says, "because you don't want an agent executing the wrong task because it has access to the wrong information." An agent pulling stale data books a slot your brand doesn't have, quotes a price it doesn't offer, or routes a customer to a location that closed. The Knowledge Graph is the record the agent acts from. Get it right, and the agent acts right.

Influence now runs the full arc: surface your brand, get it chosen, and have an agent act on it correctly. Every step traces to the same verified data, everywhere AI looks and everywhere agents reach.

Your step-by-step influence playbook

Influence works in layers. Here's the playbook: the foundational moves, plus the ones the agentic era demands.

1. Verify your data at the source. Build one structured source of truth, the Knowledge Graph, so every answer about your brand traces back to a record your brand controls.
2. Distribute it directly. Push that verified data to 200+ surfaces with no aggregator in between, including Location.com and direct LLM partnerships. Keep it fresh, relevant, and consistent everywhere.
3. Influence per engine. Don't treat the engines as one. They weigh sources differently. Gemini leans ~52% on brand websites, OpenAI ~36% on listings, Perplexity ~51% on websites, and Anthropic ~20% on social and reviews. Only ~5% of citations overlap across models, so cover every endpoint.
4. Measure where your brand is losing. Use Scout to see your brand's win rate against competitors, by location, across every major model, and turn the gaps into prioritized actions.
5. Be ready for the action moment. Shore up reviews and verified data so your brand is chosen at the hand-off, not just mentioned.
6. Bring your own LLM. Connect the model your team already uses directly to Scout's data via the Yext MCP Server, so your team can query its visibility and act on it in natural language.

The interfaces will keep changing, and so will the models. Influencing what AI says, chooses, and does about your brand always comes back to the same thing. A verified source of truth, everywhere AI looks, measured against the competition. Build that, and your brand becomes the one the LLM chooses to trust.