Google is now literally showing ecommerce businesses specifically how to get traffic from AI Search. Yes, directly inside your Google dashboard. This is probably one of the most useful AI Search updates for retailers in 2026. Let’s go through it. If you want to see whether your business is appearing across Google AI, ChatGPT, Claude, Perplexity, Grok and broader AI search, start here. It’s free: rightcited.com/ Google recently made its new AI Performance Insights report generally available inside Merchant Center for eligible businesses in the United States, Canada, Australia, India and New Zealand. The current report covers English-language conversational shopping queries across Google AI Mode and AI Overviews. Google is essentially giving ecommerce companies an AI Search diagnostic report. You can see whether your products are being surfaced when somebody uses Google’s AI features to research what to buy, and where competitors are getting visibility that you are not. Let’s start with share of voice. Google calculates AI share of voice based on how many AI impressions your brand or products receive compared with you and the competitor set Google has defined for related searches. What exactly does that mean? Well, when Google AI is helping shoppers research your category, how much of that visibility are YOU getting? If competitors are appearing constantly while your products barely show up, you finally have first-party Google data showing you the gap. No manually asking AI Mode the same question 50 times or trying to infer everything from referral traffic - Google itself is telling you. The report then breaks conversational shopping searches into three stages: Discovery. Evaluation. Ready to buy. This might be the most useful part of the entire report. Suppose you have strong visibility during Discovery, but it falls during Evaluation and nearly disappears at Ready to buy. That tells you your products are being surfaced early in the research journey, but losing visibility as shoppers get closer to making a decision. The opposite could happen too. Maybe you perform well for shoppers who already know what they want, but competitors are being introduced much earlier. That is far more useful than simply knowing you were “mentioned in AI Search.” And it is why SEO Stuff approaches AI Search Optimization as more than checking whether ChatGPT mentioned your name once: seo-stuff.com/gold-plan-pack… Then there are the actual searches. Traditional ecommerce SEO has historically focused heavily on relatively clean keywords like “running shoes,” “carry-on luggage” or “standing desk.” AI shopping queries can look more like: “What’s a good lightweight running shoe under $150 for someone with flat feet who runs mostly on pavement?” Those searches contain use cases, constraints, features, price ranges and preferences all at once. Google’s report is built around that reality. Its Top Terms section shows the concepts shoppers are prioritizing inside conversational queries. Google gives examples such as “maximum cushioning” and “arch support,” then tells merchants to incorporate relevant high-frequency terms into product titles and descriptions where appropriate. That is Google directly connecting conversational AI Search behavior to product optimization. The report also surfaces Popular Attributes. Think size, color, material and other structured product specifications. Google can show which attributes shoppers frequently care about and which may be missing from your product data. If shoppers repeatedly want to know whether something is machine washable, expandable, 100% cotton or supports a certain weight, but your product data does not clearly provide that information, you have an information gap. Google’s recommendation is straightforward: populate missing attributes and prioritize the ones shoppers are asking about most. Then there is Top Search Intents. This goes beyond individual terms and looks at WHY somebody is searching. A shopper may want an office chair for lower back pain, want to compare two models, stay under $300 or understand whether mesh or leather is better. Those are very different buying situations, and Google is now giving retailers more information about which conversational shopping intents are actually occurring. So the optimization loop becomes pretty straightforward: Look at your AI Search share of voice, identify where competitors are beating you, see where visibility falls across Discovery, Evaluation and Ready to buy, review the terms and attributes shoppers care about, improve your product information and keep monitoring. Google is explicitly recommending this kind of workflow. Keep product data accurate. Add relevant high-frequency terms where appropriate. Populate missing attributes. Keep reviewing the report as new opportunities appear. There is another important detail. The AI Performance report currently measures organic AI visibility. Paid Ads traffic is not included. That makes this particularly relevant for SEO. But Merchant Center product data is only part of the equation. People researching expensive or complicated products also ask things like: “What is the best X for Y?” “X vs. Y.” “Is X worth the money?” “Best X under $500.” “What should I look for when buying X?” A product feed alone may not answer those questions, but your website can. Buying guides, comparison pages, category pages, FAQs, use-case content, product education, original research and expert explanations give Google more context around the buying decision. Suppose shoppers are researching “best running shoes for plantar fasciitis.” Your product feed can help Google understand which shoes may be relevant. Your website can explain which features matter, which products contain those features, how models compare and who each shoe is designed for. That gives Google considerably more information to work with across the broader research journey. This is what the SEO Stuff Premium Content Bundle is designed to build: seo-stuff.com/premium-conten… Then there is authority. Your product feed and website are still information coming from YOU. Search and AI systems also encounter reviews, editorial publications, comparison articles, industry websites, third-party mentions and contextual backlinks discussing your company and products. That broader authority is why the SEO Stuff done-for-you package combines content with contextual DR50+ placements: seo-stuff.com/gold-plan-pack… And Google just gave ecommerce businesses considerably more data to see where those efforts may need to be focused. There is another development worth paying attention to - Google is also introducing Conversational Attributes in Merchant Center. These are optional product-data fields designed to help AI systems and conversational agents understand more nuanced information about products. They can include product questions and answers, supporting documents, related products, variant information and popularity information. Google recently disclosed a test involving lululemon. The company submitted conversational product attributes, and Google says those attributes were incorporated into relevant AI Mode product recommendations 50% of the time during testing. Mind you, that does NOT mean adding conversational attributes gives you a 50% chance of being recommended, it just means the additional information lululemon supplied was incorporated into relevant recommendations half the time within that test. Google wants more detailed product information because AI shopping queries are becoming more detailed, and it is increasingly giving merchants tools to both supply that information and measure what happens afterward. If you run an ecommerce company, I would be paying very close attention to this report. AI shopping is already measurable inside Google, and Google is now giving businesses tools specifically designed to help them understand and improve their presence within it. And if you want to see where your business stands across Google AI, ChatGPT, Claude, Perplexity, Grok and broader AI search, start here: seo-stuff.com/free-audit

Microsoft dropped an official “Here Is How To Get Traffic From ChatGPT” guide. Interestingly, it got surprisingly little attention. Let’s go over it together. Not long ago Microsoft released "A guide to AEO and GEO - Practical data strategies to empower retailers for AI search, AI assistants and AI browsers.” Everything here is drawn directly from the document and its diagrams, with some of my personal takes layered on top for clarity and execution value. I’ll also reference the pages in the PDF in case you want to go read it yourself. If you want to know where your site stands right now across Google and AI search, check here (it's free): rightcited.com Microsoft’s central message in the doc is that retail competition is shifting from “being found” to “being chosen.” They argue that traditional SEO was optimized for: Ranking, Clicks and Page Visits. Whereas AI-driven shopping replaces that with: Answers, Recommendations and Agent-Led Decisions. They’re arguing that visibility is now earned by how clearly AI systems understand your products, trust your brand and can act on your data. This is where “AEO” and “GEO” come in. (I hate both of these acronyms and prefer to just call it all AI search optimization, but this is their doc so I’ll go with their language.) This is also why we’ve seen brands struggle even with strong traditional SEO, but immediately improve AI visibility once they pair technical SEO with structured, intent-driven content and authoritative signals like those included in SEO Stuff’s done-for-you package. seo-stuff.com/gold-plan-pack… Microsoft also broke down the difference, to them, between AEO and GEO. Microsoft makes it a very clean distinction: Answer / Agentic Engine Optimization (AEO) in their estimation optimizes content and data so AI assistants and agents (Copilot, ChatGPT, Gemini) can: Find it Understand it Summarize it Recommend it Act on it This is about clarity and machine-readability. Generative Engine Optimization (GEO) optimizes content so generative AI search systems trust it as: Authoritative Credible Citable This is about credibility, reputation, and justification. Microsoft is explicit that SEO still matters, but it is now the foundation and not the endpoint. In practice, this is why execution now requires both properly structured pages and volume at scale, something SEO Stuff intentionally designed the Premium Content Bundle to solve. seo-stuff.com/premium-conten… Microsoft then delved into the AI shopping ecosystem and how discovery actually works now. One of the most interesting sections is Microsoft’s breakdown of AI browsers, assistants, and agents (pages 5–7). These are not separate systems and they overlap constantly. AI BROWSERS Edge, Chrome, or similar with embedded AI They can “see” the live page you are on and interpret it in real time. AI ASSISTANTS Copilot, ChatGPT, Gemini They answer questions, summarize options, and recommend products. AI AGENTS They: Navigate websites Add items to carts Apply promo codes Calculate shipping Complete purchases The key insight: The question is not “which AI surface am I optimizing for?” The question is what data can AI access, trust, and use? This is exactly where most sites break. The data exists, but it isn’t structured, consistent, or surfaced in a way AI can reliably act on. Microsoft then went into how AI actually decides what to recommend. Microsoft outlines a multi-stage reasoning process used by Copilot and Bing AI (pages 7–8). AI does not rely on one data source, but rather fuses: CRAWLED WEB DATA Brand reputation Category authority Expert mentions Historical understanding PRODUCT FEEDS AND APIS Price Availability Variants Inventory Key specs This is where competitive advantage often comes from, and where most brands are under-optimized. LIVE WEBSITE DATA Real-time pricing Promotions Reviews Media Checkout functionality If your live site fails, the agent fails, even if feeds were perfect. An example Microsoft gives is “rain jacket under $200.” AI reasoning includes: “Patagonia and North Face make quality jackets” (general knowledge) “Hiking jackets need to be lightweight and waterproof” (category understanding) “Brand X is known for hiking equipment” (brand positioning) “Your model is $179 and in stock” (feeds) “Competitor is $199 and backordered” (feeds) Your product makes the top recommendations because feeds, availability, price, and context align. This is why content that simply “ranks” but doesn’t explain, compare, or justify rarely shows up in AI answers without additional supporting assets. Microsoft then really breaks down the journey from SEO to AEO to GEO. They summarize the transition pretty clearly (page 6): SEO = matching keywords “Waterproof rain jacket” AEO = descriptive clarity “Lightweight, packable waterproof rain jacket with ventilation and reflective piping” GEO = justification and trust “Best-rated by Outdoor Magazine, 4.8 stars, 180-day returns, 3-year warranty” So basically, AEO drives understanding and GEO drives confidence, and you need both to be recommended. This is why brands pairing long-form, intent-driven content with authoritative backlinks and mentions often outperform those relying on SEO alone. Then Microsoft talks about three data layers you must control. They stress that retailers must show up in three distinct data planes (page 10): CRAWLED DATA What AI learned during training What it finds via real-time web search This shapes baseline brand perception. SEO still matters here. PRODUCT FEEDS AND APIS Structured data you actively provide This is where precision and control live. Feeds drive: Comparisons Rankings Recommendations This is where many retailers under-invest. LIVE WEBSITE DATA What AI agents see when they actually visit Includes: Reviews Media Dynamic pricing Checkout capability If agents cannot transact, influence stops at recommendation. Here are the three action pillars Microsoft prescribes. This is the most legit part of the document (pages 11–14). Pillar 1: Technical foundations and structured data AI requires structure and consistency, not creativity. Microsoft explicitly calls for: MACHINE-READABLE CATALOGS DYNAMIC FIELDS: Price Availability Size Color SKU GTIN dateModified ITEMLIST MARKUP FOR CATEGORIES LOCALIZED PRICING AND LANGUAGE VIA: inLanguage priceCurrency REQUIRED SCHEMA TYPES: Product Offer AggregateRating Review Brand ItemList FAQ They also highlight this: “Never serve different HTML to bots than to users.” Pillar 2: Intent-driven content enrichment AI interprets intent over keywords. MICROSOFT RECOMMENDS: Front-loading descriptions with: Who it is for What problem it solves Why it is better Use-case framing: “Best for day hikes above 40 degrees” Headings that mirror real questions Modular, citable content blocks THEY EXPLICITLY ENCOURAGE: Q&A sections Comparison content Feature lists “Goes well with” product relationships Video transcripts Detailed image alt text with ImageObject schema This is content designed for extraction as opposed to reading. This is also why scale matters. One or two pages won’t move the needle. Systems that produce dozens of structured, intent-mapped articles tend to win, which is exactly what the Premium Content Bundle is built around. seo-stuff.com/premium-conten… Pillar 3: Trust and credibility signals (GEO) AI systems prioritize verifiable truth. Microsoft highlights: VERIFIED SOCIAL PROOF Verified reviews Review volume Sentiment extraction (“highly rated for comfort and fit”) Review and AggregateRating schema AUTHORITATIVE BRAND IDENTITY Expert reviews Press mentions Certifications Sustainability badges Official brand links CONTENT INTEGRITY Avoid exaggerated claims Maintain consistent brand voice Provide structured FAQs and help content This also stood out: “AI penalizes low-trust language.” Interesting, but obviously open to interpretation. Microsoft then closed with a fairly straightforward message. Retailers already have most of the signals AI uses to rank and recommend. The winners in AI commerce will be the brands that: Treat data as a product Treat feeds as strategic assets Treat content as machine-readable infrastructure Treat trust as a measurable ranking factor This is what Microsoft calls “AI ranking readiness.” If I had to reduce this entire PDF to one core idea: AI needs to understand your products in order to justify recommending them. It needs to literally be able to act on your data in real time if you want to be a legit presence in AI-driven commerce. And if you want to know where your site stands right now across Google and AI search, check here (it's free): rightcited.com