Most local businesses don't have a GEO strategy. They have scattered tactics: maybe they've claimed their Google Business Profile, maybe they ask for reviews sometimes, maybe they have a website that hasn't been updated in two years.
The useful next step is to turn those separate tasks into one measured process with clear owners.
Important
Start with accurate public facts, a neutral review process, and useful crawlable pages. Then measure the same prompts over time to see what changed.
Here's what actually works.
Start with the foundation: your digital identity
Before you worry about AI recommendations, check whether customers and crawlers can find the correct business facts. If Google, Yelp, and the website show different phone numbers, addresses, hours, or services for the same branch, correct the material conflict. Harmless naming or formatting variations are a different issue.
Important
Prioritize wrong or stale facts that can send a customer to the wrong branch. Do not treat punctuation or address abbreviations as a proven AI penalty.
Run an audit. Search your business name across Google, Yelp, Facebook, BBB, Apple Maps, Bing Places, and industry directories relevant to your trade. Correct wrong phone numbers, former addresses, duplicate profiles, conflicting hours, and inaccurate services. Use Citation Cleanup for Local and AI Search when you need to decide which source categories deserve cleanup first.
This work is routine but useful. It prevents customers from reaching an old branch or booking a service the location does not provide.
Build your evidence layer
The visible answer and its citations give you an evidence trail. Your job is to make the business's real services, locations, and customer proof clear on useful public pages, then measure whether those pages appear.
Reviews are customer evidence. A representative, policy-compliant review profile helps buyers understand recent service experiences. Public research does not establish review volume, velocity, or sentiment as universal AI ranking factors. See how Sierra Air Conditioning & Plumbing built a systematic review operation while separately tracking AI visibility.
Structured data can clarify visible facts. Use applicable LocalBusiness, Service, FAQ, and sameAs properties only where they match the page and current platform guidance.
Third-party pages provide additional context. Accurate directory listings, association memberships, awards, and legitimate press coverage may appear in search or AI citations. Track what is actually retrieved instead of assuming every mention adds authority.
Pro Tip
Think of your evidence layer like a resume. Reviews are your work experience, structured data is your credentials, and third-party mentions are your references.
The technical layer most businesses skip
Here's where service businesses usually fall short: technical implementation.
Schema markup goes on your website. It's code that structures your business information in a format AI can parse directly. LocalBusiness schema tells AI your name, address, phone, hours, services, and service area. FAQ schema marks up your frequently asked questions. sameAs links connect your site to verified profiles across the web.
Pro Tip
Ask the developer to show that structured data matches the visible page, validates correctly, and describes the right branch. More markup is not automatically better.
Beyond schema, an llms.txt file can help. It is an emerging Markdown proposal for giving AI tools a concise guide to your business, your key pages, and your preferred source material. Not every AI system reads it, so treat it as a complement to robots.txt, sitemap.xml, schema, and visible page content. For a complete guide to implementing it, see What is LLMs.txt and Why Your Business Needs One.
Your website content matters too. AI systems pull information from your site to populate their understanding of your business. Clear, specific service descriptions. Geographic coverage. Team information. Pricing transparency where appropriate. The more concrete information you provide, the better equipped AI is to recommend you accurately.
Next step
Is AI recommending your business?
Find out how visible you are across ChatGPT, Gemini, Perplexity, and AI Overviews.
The review engine
This is where most of the work happens on an ongoing basis.
You need a systematic way to invite every eligible customer to leave honest feedback. The request must not depend on whether the team expects a positive review.
For field service businesses, the request can happen after the job when the customer has enough context to respond. NFC badges, QR codes, and follow-up links can reduce friction. Make the request optional, neutral, and available under the same eligibility rule.
For retail or hospitality, train staff to use a neutral request such as, "You can share your experience here." Do not condition the ask on a good experience or request a particular rating or wording.
Pro Tip
At Cheers, we recommend tracking review conversion rate by location and team. Treat benchmarks as diagnostic signals, not quotas. If one branch is far below the rest, inspect the process and coach the ask.
Respond to everything
Respond where a response helps the customer or future readers. Thank customers, address verified concerns, protect private information, and move account-specific resolution to a private channel.
Responses are part of the public record. Their value is customer communication; public evidence does not show that a particular response rate improves AI recommendations.
Important
Avoid empty templates because they do not answer the customer. Reference specific details only when doing so is accurate and does not expose private information.
Monitor and iterate
AI visibility changes as products, sources, competitors, and business information change. Treat measurement and source maintenance as recurring work.
Track your review velocity monthly. Watch for trends. If velocity drops, diagnose why.
Monitor your mentions across platforms. Set up Google Alerts for your business name. Know when you're being talked about.
Test AI recommendations periodically. Ask ChatGPT and Gemini for businesses in your category and location. See where you rank. See who's above you and try to figure out why.
Pro Tip
Save the prompt, provider, market, answer, citations, and date. Without a stable baseline, the team cannot tell whether anything improved.
Keep the public record current
There is no proven early-mover lock-in for local AI recommendations. Models, indexes, retrieval systems, and cited sources change.
The durable work is simpler: keep public facts accurate, collect representative customer feedback, publish useful first-hand information, and verify what each product shows.
Start with the market where a wrong answer or missing branch matters most, record the baseline, and close one verifiable source gap at a time.
Further Reading
- LocalBusiness Schema Documentation. Schema.org's official specification for local business structured data
- Google's Structured Data Guide. Google's documentation on implementing LocalBusiness schema
- The llms.txt Specification. Original proposal and format documentation for llms.txt
- Apple Business Connect. Apple's platform for managing your presence on Apple Maps and Siri
Amadeus Peterson is the CTO of Cheers, the local search platform for service businesses.