A simple guide for e-commerce brands in the AI search era

Keyword stuffing is over

Traditional SEO rewarded long‑form pages packed with keyword variations. AI search engines such as ChatGPT, Perplexity, and Google’s AI Overviews now lift short, factual snippets, stats, and first‑hand quotes directly into their answers. If your brand isn’t the source of those snippets, you risk disappearing from the millions of users asking product questions to AI daily.

Why AI search optimization matters more for parts & equipment sellers

1. Customers (both pro and DIY) are turning to AI engines for technical troubleshooting. Instead of searching Google and clicking through results, buyers now ask ChatGPT questions like “What pump fits a Whirlpool WRS325SDHZ?” or “Is part W10348269 compatible with model ABC?” If your specifications and compatibility data aren’t structured for AI extraction, you’re invisible to this growing segment.

2. Quality troubleshooting content becomes more important. With conversational AI, customers ask sequential diagnostic questions—starting with “Why is my prep table compressor hot?”, then “How to test a compressor?”, before finally asking “What compressor fits model ABC?”. AI engines surface whoever provides the clearest answer for each question. Competitors with better troubleshooting guides can intercept customers early in their journey and win the eventual parts sale. Traditional SEO-optimized troubleshooting content that stuff keywords into pages

1. Measure your traffic

• Most AI platforms send a traditional Referer header when a user clicks on a link to your content, so you can monitor traffic from ChatGPT, Perplexity, and others in Google Analytics or your preferred analytics tool just like any other referring site.
• For ChatGPT, every referred URL includes ?utm_source=chatgpt.com.
• Google AI Overview is harder to track directly. When users click source links from the AI summaries, it appears as regular Google search traffic with https://www.google.com as the referrer.
• Each session signals tens of unseen impressions.
• Track branded mentions inside AI engines weekly; the delta tells you if you’re the answer—or the after‑thought.

All AI search products still use traditional search indexes under the hood. When you ask ChatGPT about a product, it queries Bing’s index. When Google’s AI Overview generates an answer, it pulls from the same crawled content that powers regular Google Search results.

This means your existing SEO foundation still matters tremendously. AI models need to first discover your content through traditional crawling and indexing before they can cite it in their responses. A well-optimized site structure, clean URLs, proper internal linking, and fast loading times remain critical—they determine whether your content enters the AI’s source pool at all.

The key difference is what happens after discovery. While traditional search ranked pages by authority and keyword relevance, AI engines now parse that same indexed content for factual snippets, structured data, and authoritative quotes to weave into conversational answers.

Note: User numbers are approximate and based on public estimates.

AI Product Domains (for analytics filtering):

chatgpt.com, gemini.google.com, copilot.microsoft.com, edgeservices.bing.com, perplexity.ai, claude.ai, grok.com, chat.deepseek.com, meta.ai, chat.mistral.ai

Crawlers

Each platform uses specialized bots with different purposes—some for training models, others for real-time user queries. Check your robots.txt file to ensure you’re not accidentally blocking these crawlers, and verify that any Crawl-delay directives aren’t set too high (keep under 10 seconds to avoid hindering discovery).

2. Map buyer questions & simulate prompts

1. Brainstorm 25–50 core scenarios: diagnostics, comparisons (“stainless vs aluminium food pan”), compatibility (“Will part #123 fit Whirlpool WRS325SDHZ?”).
2. Feed those prompts into each AI product via a simple script and export the answers + citations.
3. Score yourself on visibility (mentioned, cited, primary recommendation, or absent).

3. Improve your metadata

Schema.org annotations (defined in JSON-LD script tags) are an important piece of metadata that LLMs directly parse for context around your content. Here’s what matters most:

In addition to schema annotations, some have proposed a new standard, similar to robots.txt, called llms.txt for LLM-specific optimization. This proposed standard is a simple text file hosted at your root domain that gives AI crawlers a curated map of your most important pages—think product categories, FAQs, and return policies. The goal is to remove ambiguity by providing language models with structured, priority content rather than forcing them to ingest large pages and guess what matters most on your site.

While llms.txt remains a proposed standard without universal adoption, major AI companies like Anthropic and Google have published their own llms.txt files. Despite this, no LLM provider has formally committed to parsing llms.txt so our recommendation is that llms.txt shouldn’t be a major focus or investment right now.

4. Build authority with FAQs and smart citations

• Add FAQs under each product page—this surfaces direct Q&A snippets for engines to quote verbatim.
• Inline, in‑text citations outperform plain backlinks. Write “According to UL 197 standards (UL.com)….”—the model sees the trusted domain and the snippet.
• Sprinkle first‑hand data: successful repair rates, compatibility coverage across equipment models, customer satisfaction data, or performance benchmarks from your lab testing.
• Identify third‑party pages the AI already cites (Reddit threads, trade forums) and engage there visibly as a company rep.

Pro tip: keep FAQ answers ≤ 280 characters; that brevity matches snippet length bias.

FAQs

How do LLMs recognize a citation?

A clear, unambiguous mention of the source in the same paragraph—with or without a live link—carries the most weight. Backlinks still matter for discovery, but inline references drive answer selection.

What is location targeting, and should I care if I sell nationwide?

Yes, even nationwide sellers benefit from location targeting. Declaring areaServed and contentLocation tells AI models which cities to surface you for queries like “auto parts in Denver” or “same-day parts near Portland.” You can target multiple major metropolitan areas to capture local intent searches—many B2B buyers still prefer suppliers with local presence, and location signals boost visibility even for broader searches.

Key takeaway

Your site is no longer a destination; it’s a library that feeds AI with authoritative snippets. Make every product detail page, FAQ, and support article a top‑shelf reference and the engines will sell for you.

Need help? Send me an email here, and I’ll send over our simulation engine for measuring AI search blindspots.