How to Get Cited by AI in Your Industry: GEO for Local Services, SaaS, and E-commerce

The fundamentals of generative engine optimization are the same everywhere: cite real sources, quantify claims, quote named experts, make the page machine-readable, and let the crawlers in. The 2023 study by teams from Princeton, Georgia Tech, The Allen Institute for AI and IIT Delhi established the tactics and measured them, reporting visibility lifts of up to 40% in their benchmark (arXiv:2311.09735).

But the work those fundamentals translate into is completely different depending on what you sell, because the question the engine is answering is different. "Best project management tool for a design agency" requires the model to compare products. "Is there an emergency plumber near me right now" requires it to resolve a local entity and assert facts about it. "Will this fit a 2019 Silverado" requires it to reason over specifications.

Same acronym. Three different jobs. Here is what each one actually demands.

Local services: the game is entity resolution

When someone asks an assistant for a local recommendation, the model must decide it knows who you are confidently enough to name you. That is not a content problem first — it's a data-consistency problem.

What decides it:

1. Corroborated facts across sources. Your site, your LocalBusiness JSON-LD, your Google Business Profile, and third-party directories must agree on name, address, phone, hours, and category. When they conflict, the model hedges or omits you — an omission is not a penalty, it's uncertainty. BrightLocal's long-running Local Consumer Review Survey has consistently shown how heavily consumers weight third-party corroboration; engines now apply that standard programmatically.
2. A complete Business Profile. Categories, hours including holiday hours, service area, photos, and reviews with replies. Google's Business Profile guidelines define what's permitted; completeness is what makes the entity legible.
3. Structured data that mirrors reality. The most specific applicable schema subtype, with real geo coordinates, openingHoursSpecification, and areaServed. Google's structured data documentation is explicit that this helps its systems understand the page.
4. Answers to operational questions. "Do you come out at 2am?" "How fast can someone get here?" "Do you charge for the estimate?" These are the actual prompts, and a page that answers them in a FAQPage block is directly extractable.

What does not work for local: thin city pages for towns you don't serve. They dilute entity consistency, which is the one asset the whole strategy rests on. The counter-pattern is covered in our multi-location SEO playbook.

Prompts to track: "best [service] in [city]", "[service] near me open now", "how much does [service] cost in [city]", "emergency [service] [city]", "is [brand] licensed."

SaaS: the game is honest comparison

Software buyers ask assistants comparison questions before they ever open a search box — "best X for Y," "X vs Z," "alternatives to X." The model's job is to compare, which means it needs comparative facts it can trust.

What decides it:

1. Comparison pages that name competitor strengths first. This is counterintuitive and it is the highest-leverage move in the vertical. Engines cross-reference claims against the competitor's own pages; a comparison that misrepresents them gets dropped rather than cited. A page that says "choose them if X, choose us if Y" is quotable precisely because it survives fact-checking.
2. Published pricing. "Contact us for pricing" removes you from every cost question a model answers — and cost questions are a large share of software prompts. If you cannot publish exact numbers, publish ranges and what moves them.
3. Specific, quantified capability claims. Not "powerful automation" but "runs a fixed prompt set across six engines on a daily schedule." Vague claims are unextractable; specifics are quotable.
4. Integration coverage as an entity graph. "Does it work with Salesforce" is a prompt. A page listing real integrations with real auth methods answers dozens of these at once — the way our integrations page enumerates auth model and scope per connection.

What does not work for SaaS: listicles where you're conveniently #1 in your own ranking, and competitor pages built from guesses about their features. Both fail the cross-reference.

Prompts to track: "best [category] software", "[you] vs [competitor]", "alternatives to [competitor]", "how much does [category] software cost", "[category] for [industry/company size]".

E-commerce: the game is specification precision

Product questions are compatibility and constraint questions. "Will this fit," "is it dishwasher safe," "what's the return window," "does it ship to Canada." The model needs unambiguous attribute data, not marketing copy.

What decides it:

1. Complete Product structured data — price, currency, availability, condition, GTIN or MPN, and AggregateRating only if the ratings are real. Fabricated rating markup is a fast route to being distrusted by both Google and any engine that cross-checks.
2. Attributes as data, not prose. Dimensions, materials, compatibility lists, and fitment ranges belong in structured, tabular form. A model can extract a table row; it hesitates over a paragraph that mentions three measurements.
3. Policy pages that state numbers. Return windows in days, shipping thresholds in dollars, warranty length in years. "Hassle-free returns" answers nothing; "30 days, free return shipping over $50" answers a prompt exactly.
4. Review content that reads as first-hand. Real reviews mentioning specific use cases give a model the concrete language it needs to justify a recommendation.

What does not work for e-commerce: identical manufacturer descriptions across thousands of SKUs. There is nothing to extract that a hundred other retailers don't have verbatim, so the engine cites the manufacturer or the largest marketplace instead.

Prompts to track: "best [product] for [use case]", "does [product] fit [model]", "[product] vs [product]", "where to buy [product] online", "[brand] return policy".

The comparison, side by side

The three things every vertical still has to do

Let the crawlers in. GPTBot and OAI-SearchBot (OpenAI bot docs), PerplexityBot (Perplexity crawler docs), ClaudeBot (Anthropic's crawler policy), Google-Extended, Applebot-Extended, CCBot. A blanket disallow added in 2023 and never revisited is still the single most common own-goal in this discipline.

Render server-side. If the body appears only after client-side JavaScript, a fetch-only crawler sees a shell. Bing's webmaster guidelines have long recommended critical content be in the initial HTML response, and the same principle protects you here.

Measure, don't assume. AI answers are non-deterministic — the same prompt gives different results on consecutive runs. You need repeated multi-engine sampling before any conclusion holds, which is what a visibility tracker is actually for. Our walkthrough of how that sampling works covers the method; the general tactic list lives in our GEO guide.

The mistake each vertical makes most often

Local services: optimizing the website while the profile rots. Teams rewrite service pages for months while the Google Business Profile still lists 2023 hours and a category the business stopped serving. The profile and the directories are half the corroboration set; a beautiful page cannot compensate for facts that disagree with themselves. Fix the data, then write.

SaaS: hiding pricing. "Contact us for pricing" is a defensible sales strategy and an unrecoverable GEO position. Cost questions are one of the largest prompt categories in software, and a model answering "how much does [category] cost" will name the vendors who published numbers and skip the ones who didn't — including in comparisons where you would otherwise win. If exact pricing is genuinely impossible, publish ranges and the variables that move them.

E-commerce: treating category pages as the destination. Most product prompts are constraint questions — fitment, compatibility, dimensions, policy — and constraint answers live on product pages, not category pages. A category page optimized for a head term answers almost none of the questions buyers actually ask an assistant. Put the specification tables where the specifications belong.

All three: publishing and stopping. Citations drift. An engine that named you in March may name a competitor in June after they publish something more specific. This is a maintenance discipline, which is exactly why the measurement loop matters more than any individual page you ship.

Picking your first ten prompts

Whatever the vertical, the first prompt set comes from the same place: things buyers have actually said to you. Sales calls, support tickets, the "quick question" emails. Not a keyword tool — a keyword tool gives you "CRM software," and nobody types that into an assistant.

Then structure the ten as:

• Three category questions — "best [category] for [your buyer type]"
• Three comparison questions — you against the two competitors you lose to most
• Two problem-first questions — the pain in the buyer's words, before they know the category name
• Two constraint questions — the specific compatibility, coverage, or eligibility question that gates a purchase in your vertical

Baseline all ten across every engine before you publish anything new. Without a pre-change reading, every improvement afterward is arguable — and that baseline is the cheapest thing on the list. For what the full program costs once you scale it, see our GEO pricing breakdown; if you're weighing tooling against a retainer, the three-way comparison lays out where each option wins.

Frequently asked questions

How do I get cited by ChatGPT in my industry? Start by identifying what job the engine is doing for your buyers. Local services need entity consistency — matching name, address, phone, hours and category across your site, structured data, Google Business Profile and directories. SaaS needs honest comparison pages that name competitor strengths and publish real pricing. E-commerce needs complete Product structured data and specification tables. All three need open AI crawler access and server-rendered content.

Does GEO work differently for local businesses than for software companies? Yes, substantially. For local businesses the binding constraint is whether the model can resolve your business as an entity confidently enough to name it, which is a data-consistency problem. For software the constraint is whether your comparison and pricing content survives fact-checking against competitor pages. The underlying tactics overlap; the work does not.

What structured data should I use for AI search visibility? Match the schema to what you sell: LocalBusiness (or the most specific subtype) plus FAQPage for local services; SoftwareApplication or Product plus FAQPage for software; Product with Offer, GTIN or MPN and real availability for e-commerce. Add Article to editorial content and BreadcrumbList sitewide. Never mark up ratings you did not receive.

Why does e-commerce struggle to get cited by AI engines? Usually because product pages carry manufacturer-supplied descriptions identical to a hundred other retailers'. There is nothing distinctive to extract, so the engine cites the manufacturer or a large marketplace instead. Original specification tables, real fitment and compatibility data, numeric policy statements and first-hand reviews are what differentiate a retailer page.

How long does it take to see AI citations in my vertical? Local services often move fastest — four to eight weeks — because fixing entity consistency has an immediate effect on whether a model will assert facts about you. SaaS and e-commerce typically take eight to twelve weeks, since both depend on new content being retrieved, indexed and preferred over incumbents.

Should I write comparison pages against competitors? For SaaS and considered purchases, yes — they are among the highest-value pages you can publish, provided they are honest. Name the competitor's real strengths first and state when to choose them. Engines cross-reference claims against competitor pages, so a self-serving comparison is more likely to be dropped than cited.