AI search rapidly reshapes ecommerce traffic and influences how consumers discover products online. Understanding AI product discovery and how to optimize your website for AI search is now critical for online stores that want to succeed across both traditional search engines like Google and Bing, as well as AI and LLM-powered platforms such as ChatGPT, Perplexity, and Google AI Overviews.
In this AI indexing benchmark report for ecommerce, we provide a data-driven look at how brands across key categories (fashion, beauty, electronics, and home decor) perform in AI-driven search results in 2025. We also share a practical ecommerce SEO and AI SEO playbook, packed with actionable strategies and insights, to help brands improve AI indexing performance, boost product visibility in AI-generated results, and enhance overall brand discoverability across AI platforms.
TL;DR of AI Indexing Report for Ecommerce
- AI search dominates product discovery: 91%+ of ecommerce product queries now trigger AI-generated results, with fashion and beauty reaching 94–95% coverage.
- Impact of AI search on ecommerce SEO and traffic: AI answers push traditional organic results far down the page. Only around 20% of AI-cited pages overlap with Google’s top 10 organic results.
- Changes in search results per product category:
- Fashion and beauty: AI favors visuals, reviews, Q&A, and trend-driven content.
- Electronics: objective specs, schema, and comparison guides dominate citations.
- Home and decor: mix of factual specs and visual inspiration.
- How major AI platforms surface ecommerce product results:
- ChatGPT: labels products (“budget-friendly” and “most popular”) based on third-party data.
- Google AI Overviews: rewards schema-rich, structured data (66% of citations are not from the top 10 organic).
- Perplexity: heavy Reddit bias (46.7% citations) and 20+ sources per query.
- Winning strategies (AI SEO playbook for ecommerce):
- Get your technical SEO AI-ready: use schema markups, structured data, and tools like Prerender.io to ensure product pages are easily indexed.
- Publish AI-friendly content: create listicles, FAQs, reviews, and comparison guides that directly match product queries.
- Build multi-channel visibility: secure mentions in third-party reviews, influencer content, and visual platforms (e.g., YouTube, Pinterest) to increase AI citations.
AI Search Visibility and Impact on Ecommerce Traffic
The Rise of Ecommerce Product Search on AI Platforms
Love it or hate it, AI is now driving the majority of ecommerce traffic. ChatGPT alone handles over 1 billion searches weekly—and according to Adobe, traffic from other generative AI sources has surged 1,200% between July 2024 and February 2025. A significant portion of this AI-driven traffic is ecommerce-related, with retailers seeing more referrals from AI chat results. In fact, a recent survey found that 58% of consumers now rely on AI platforms for product recommendations.
AI-powered results now appear in roughly 91% of product-related searches, and in high-volume categories like beauty and fashion, coverage reaches 94–95%. This shift is redefining brand discoverability—visibility now hinges on how AI summaries present and cite your products.
Unlike traditional search engines that typically feature one primary source per result, AI summaries don’t just cite one familiar site that ranks in the top spot but gather from many sources. A typical expanded search generative experience (SGE) answer includes around 10 to 11 links in total, drawing from roughly 4–5 different domains.
How AI Product Discovery Affects SEO and Traffic
From an SEO perspective, this rise of generative search results is a double-edged sword.
On the one hand, AI answers quickly satisfy users. On the other hand, they squeeze traditional organic listings far down the page.
The displacement effect is particularly pronounced in ecommerce. When users expand an AI result, the previous number 1 organic listing drops an average of 1,718 pixels for fashion queries and 1,700+ pixels for electronics.
Furthermore, the placement of AI-generated content above organic results creates an additional barrier, forcing users to scroll further down the page. Sadly, most users abandon their search before reaching these traditional results. Our survey data (conducted among consumers and SEO) shows this behavior shift is accelerating: 44.2% of respondents now place equal or greater trust in AI responses compared to conventional search results.
From an SEO brand standpoint, the shift in AI search visibility poses two challenges:
- You may lose some traffic you used to “own” on Google SERPs (as AI answers siphon clicks away or direct them to competitors).
- You also have new opportunities to gain visibility for queries where you never ranked before on Google (if you can get featured in the AI results).
No ecommerce brand is immune to the shift to AI product discovery, as even branded searches can trigger generative answers that include third-party sites or even competitors. As Aleyda Solis noted, brand retail sites’ category pages are particularly at risk. An established retailer’s product listing page (PLP) may see traffic leak to other online stores’ product detail pages when SGE provides a direct comparison.
“In the case of top retailers & brand sites, clicks go to URLs leveraging many features. There’s a very long tail of clicks going to product detail pages besides relevant PLPs. This aligns with ecommerce platforms showing a major trend of PDP traffic growth.” – Aleyda Solis
For instance, in the past, someone searching for a product name might click through the brand’s category or catalog page. Now, the AI overview might instead suggest a few specific models or styles from various sellers, each linking straight to those products.
For ecommerce searches on AI platforms, the AI system doesn’t care who officially owns the product or which site has the best price. It cares about which source has the most relevant, richly detailed product information for the user’s query. That’s where most ecommerce traffic is headed today.
Now, let’s take a deeper look at how different categories of ecommerce products are faring with AI.
How AI Search Reshapes Ecommerce Product Visibility (Trends and Patterns)
We analyzed product-focused queries across fashion, beauty, electronics, and home to understand how AI-driven search surfaces products. For each query, we tracked:
- Whether an AI answer appeared
- Number of sources shown (collapsed vs. expanded)
- Left-most source domains
- Organic rank displacement (in pixels) for the previous number 1 link
- AI and organic domain overlap
- Share of voice per domain (share of citations)
Not all ecommerce categories behave the same under AI-driven search. Below, we break down the granular patterns that reveal how products are discovered and cited by AI systems.
How AI Search Reshapes Ecommerce Product Visibility (Trends and Patterns)
We analyzed product-focused queries across fashion, beauty, electronics, and home to understand how AI-driven search surfaces products. For each query, we tracked:
- Whether an AI answer appeared
- Number of sources shown, as well as collapsed vs. expanded results
- The domains of the first-listed sources
- Organic rank displacement (in pixels) for the previous top-ranked link
- AI and organic domain overlap
- Share of voice per domain (share of citations)
Not all ecommerce categories behave the same under AI-driven search. Below, we break down the granular patterns that reveal how AI and LLM-driven searches discover and cite products.
Fashion and Apparel Products
The fashion and apparel category shows one of the strongest AI presences in generative search, with queries triggering AI responses more than 90% of the time. These results lean heavily on visual elements and community-driven advice. For instance, queries like “best winter coats 2025” or “women’s ski jacket for Whistler” almost always return AI-powered product recommendations due to the high retail coverage in this vertical.
Within Google’s SGE, fashion-related searches often generate a horizontal product carousel featuring thumbnails, titles, and prices for clothing and accessories. Because style decisions are subjective, large language models (LLMs) draw heavily from contextual and opinion-based content, including fashion blogs, magazine “best of” lists, and Q&A forums where consumers discuss fit, styling, and trends.
The organic visibility drop for fashion-related products is steep, with the first traditional listing pushed down nearly 1,718px when an expanded AI answer appears. This means shoppers may see an entire screen filled with outfit options, styling tips, and multi-source links before reaching any organic result.
Interestingly, Q&A-driven content has a strong foothold in AI search for fashion. Authoritas’ research found that Quora ranks among the top generative result domains in this segment. This suggests Google frequently elevates community advice—for example, surfacing answers to queries like “How do I style a velvet blazer?”—right alongside product listings.
Ecommerce SEO and AI SEO tips for fashion products:
Fashion brands must recognize that high-quality visuals, trend context, and third-party endorsements (e.g., bloggers or stylists) heavily influence AI rankings. The LLM isn’t just listing specs, but also curating a mini lookbook or advice column from across the web.
Beauty and Personal Care Products
In the Beauty category (covering skincare, makeup, and fragrances), AI search behavior closely mirrors Fashion, with AI results appearing in 94% of queries. For example, a search like “best hydrating serum for winter” typically generates an AI summary that blends product descriptions with user experiences.
AI answers in Beauty often draw from expert reviews, ingredient breakdowns, and customer testimonials highlighting specific skin types or concerns. Because beauty products are highly personal (driven by factors like skin condition or fragrance preference), AI frequently emphasizes use-case phrasing such as “ideal for oily skin” or “suitable for sensitive skin”.
The Beauty and Personal Care category has been hit especially hard by AI-driven changes: it shows the largest year-over-year decline in average paid search CTR, down 15.41%. As a result, visibility strategies must adapt to the new AI-first environment.
Ecommerce SEO and AI SEO tips for beauty products:
Beauty and personal care brands should ensure their product pages and content marketing include clear benefits and third-party validations.
Electronics and Technology Products
In electronics, search queries often revolve around specifications, performance comparisons, and reviews—making them prime opportunities for AI-driven product discovery.
The electronic product category shows more resilience to AI search visibility than others. Electronics and home appliances maintain a 3.6% average conversion rate, outperforming most ecommerce verticals. Adobe’s data even highlights electronics and jewelry as top categories for AI-driven conversions, suggesting that technical products benefit from AI’s ability to pre-qualify shoppers.
Because electronics are standardized products, much of the content is objective—specs, features, and prices that can be directly compared. LLMs, therefore, prioritize factual, structured data and comprehensive comparisons. AI results frequently cite tech review sites, spec tables, and detailed comparison articles. For example, an SGE result of an electronic product search might include a snippet like:
“According to TechRadar, CameraCo Model Z offers a 12-hour battery life and 128GB storage, outperforming CameraCo Model Y in low-light conditions.”
While manufacturer or retailer pages may be used when AI search presents electronic product specs or comparison charts, third-party review content often wins thanks to its narrative, context-rich style. This includes niche tech blogs, forums (e.g., Tom’s Hardware, Arduino forums), and Q&A sites (Reddit), blending multiple sources to cover buyer intent. On average, electronics generative search results cite 4–5 sources, reflecting the comparison-heavy nature of the journey.
Ecommerce SEO and AI SEO tips for electronic and tech products:
Electronics brands should focus on structured data (schema) and detailed content. Providing comparison charts on your own site (e.g., comparing your laptop models, or how your phone stacks against a competitor) can make it easier for AI to cite you. For broader “best gadgets” queries, participating in affiliate review programs or getting featured in expert roundups is key, since those listicles often surface in AI recommendations.
Home and Decor
The Home category is broad, spanning furniture and décor as well as appliances and home improvement. As a result, it displays a blend of AI search patterns. Interestingly, this vertical shows resilience in paid search, with Home and Home Improvement achieving a 13.95% year-over-year CTR increase—a sign that advertisers adapt effectively to the AI-driven landscape.
For commodity-like appliances (refrigerators, vacuums, air conditioners), AI behavior mirrors Electronics. LLMs prioritize objective specs, user ratings, and side-by-side comparisons. AI answers typically highlight a few models with key stats—such as runtime, dustbin capacity, or price—drawing heavily from sources like Consumer Reports, Wirecutter, and established tech blogs.
By contrast, home décor and furniture queries lean into style and visuals. Since these searches often balance utility with aesthetics, AI search responses vary widely. A home improvement query might surface DIY forums and instructional content, while décor-related queries are more likely to include multimodal results with images, resembling a Pinterest-style inspiration feed.
Ecommerce SEO and AI SEO tips for home and decor products:
Home goods retailers should cover both bases for better performance on AI brand discoverability: 1) ensure technical info and dimensions are readily available (for appliances or any product where specs matter), and 2) inspire with visual and contextual content for decor (think style guides, lookbooks, and influencer partnerships that produce blog content).
Now that we’ve seen the impact of AI across these ecommerce verticals, it’s time to look at how AI platforms select products to recommend.
How ChatGPT, Google AI Overviews, and Perplexity “Rank” Ecommerce Products
The way AI models index and rank ecommerce content is driven by factors both familiar and novel. Traditional SEO signals (relevance, authority, and structured data) still matter, but each LLM-based search platform has its own algorithms and quirks.
ChatGPT for Ecommerce Shopping
ChatGPT’s shopping algorithm combines explicit user intent analysis with unnamed third-party data providers rather than consumer-friendly factors like shipping speed or return policies. The system generates AI-interpreted labels, such as “Budget-friendly” and “Most popular,” based on review analysis and pricing context, rather than verified merchant data.
Source: ChatGPT
Google AI Overviews for Ecommerce Product Search
Google AI Overviews prioritize schema markup, including reviews, images, pricing, and real-time inventory counts. Google AI search requires mobile-first schema implementation with JSON-LD structured data preferred over microdata. Crucially, 66% of AI Overview citations come from sources not ranking in traditional top 10 results, completely bypassing established SEO hierarchies.
Perplexity AI For Shopping Ecommerce Products
Perplexity AI shows extreme Reddit concentration at 46.7% of top citations, emphasizing real-time web search over training data. The platform also processes over 20 sources per query, compared to ChatGPT’s 3-4 sources, with subjectively superior source attribution and strong performance for comparison queries.
The technical requirements create clear winners and losers, but through all these, we’ve been able to spot some key patterns in how AI behaves with ecommerce verticals. Let’s break down these patterns and their implications in ecommerce.
Impacts of AI Search on Ecommerce Products’ CTR, Rankings, and Content Strategies
A. Number of Sources and Click Through Rate Implications
Unlike a traditional search snippet that may cite one or no sources, AI answers often cite multiple. However, according to Search Engine Journal, the exact number of sources varies depending on the query and content vertical.
Google’s AI Overviews, for example, typically include between three and five source links in their initial, collapsed view. Certain ecommerce product verticals, such as finance or hospitality, often show fewer sources (around three), while others, like electronics or beauty, may display four or five sources.
This distinction is crucial for understanding click-through rates (CTR) for ecommerce product strategy. A lower number of sources means each one is immediately visible without horizontal scrolling, which increases its likelihood of capturing a click. In fact, being one of only three sources generally yields a significantly higher average CTR per link than being one of five.
For brands, this highlights a strategic ecommerce AEO opportunity: focus on optimizing product search queries where AI answers tend to display a limited number of sources. These are high-value opportunities where inclusion can lead to substantial visibility and clicks.
It’s also important to note that when an AI presents an answer with more than three sources, Google often places the extra links in a horizontally scrollable carousel. Therefore, being among the first three sources (leftmost) is essential for maximizing brand visibility in ecommerce AI search.
B. AI vs. Organic Rankings: Implications for Brand Discoverability
Perhaps one of the biggest shifts is that ranking first organically no longer guarantees a top result in the AI. Studies show that, overall, only ~20% of pages cited in SGE were also among the top 10 organic results for that query.
For example, in product-heavy searches (retail, tech, etc.), the overlap between AI-cited domains and page-1 organic domains is far lower than in service-oriented searches. Google seems to purposefully diversify AI sources, favouring informational and review content when the organic results are mostly ecommerce store pages.
C. Optimizing Content Format and Structure for Ecommerce AI Indexing
Across the board, AI ranking rewards specific content formats. Notably, concise, well-structured answers to the query tend to be selected. Pages that clearly address the user intent, like listicles, FAQs, how-to guides, or straightforward product descriptions, seem to dominate AI citations.
In an analysis of AI generative engines’ favorite domains by category, many winners are sites with a very “lean” technical structure and Q&A or list-style content. For instance, Quora’s question-answer pages are plain text, easily digestible, and directly on-topic, which likely contributed to Quora being a top 20 SGE source in 11 out of 15 verticals studied.
Similarly, product review sites that use clear subheadings for each product (such as “Best for X”, “Best Budget Option for Y”, etc.) give AI a clean summary to grab.
The presence of schema markup also helps LLMs interpret content. Ecommerce pages that implement Product schema (with price, ratings, availability) and FAQ schema or ItemList schema for listicles provide machine-readable context that can make it easier for an AI to extract the relevant bits.
Need help with schema markup optimization? Follow our schema markup guide for beginners.
D. Objective Data vs. Subjective Context
LLMs try to satisfy both factual questions and subjective advice in one result. Thus, they leverage objective data for standardized comparisons and subjective/contextual data for experiential queries.
For example, Google’s integration of the Shopping Graph into SGE means that for product queries, the AI has access to real-time data on pricing, stock, and specs across many retailers. That’s how an AI-based answer can confidently show “$299 – $349” as the price range for a gadget and list multiple retailers. It’s pulling from the structured product feed.
So for standardized products (such as electronics and appliances), having accurate feeds and structured data is more critical than ever.
In contrast, for more subjective queries (such as “what does X perfume smell like?” or “is Y brand sofa comfortable?”), the AI scours reviews, forum discussions, and articles to gauge sentiment and descriptive info. This is where influencer content and user-generated content play a role. If many people mention that a particular running shoe “runs small, order a half size up,” an AI answer might include that advice, citing a source like a forum or a user review snippet.
Learn more about how to get your ecommerce website indexed by ChatGPT.
E. Emerging Patterns in AI Visual and Multimodal Search
While our focus is text-based AI results, it’s worth noting that generative AI in search is increasingly multimodal.
Google search generative experience incorporates images into the answer when relevant. In fashion and home décor, for instance, it might show product images or style inspiration photos alongside text.
Early trends indicate that products with high-quality, mobile-friendly images can indirectly gain more prominence if the AI chooses to display an image carousel; often, it will use the image from the source it cites.
There’s also the matter of Pinterest and social content. Currently, LLMs don’t crawl Instagram or TikTok widely, but they index Pinterest images (which have text descriptions) and YouTube (especially the video descriptions and transcripts).
So, an interesting pattern is that ecommerce brands are increasingly using platforms like Pinterest and YouTube for SEO in the AI era. A Pinterest pin that has the exact product name and a descriptive blurb might surface in AI results, especially through Bing’s Image Search or when an LLM is looking for visual examples.
Ecommerce SEO and AEO tips:
Influencers who cross-post content to blogs or YouTube are becoming particularly valuable, since the AI can “see” their content there even if their Instagram content is invisible.
AI Product Discovery Playbook: How to Optimize Ecommerce SEO and AI SEO to Win in the AI Era
Finally, based on the data and patterns we’ve analyzed, here’s a playbook of actionable strategies for ecommerce brands to boost their visibility in the era of shopping assistants and generative AI search results. You can think of these as the learned lessons and best practices from the current state of AI search visibility.
1. Optimize Product Pages for AI Discovery
Treat your product detail pages as both sales tools and authoritative information sources. Start with comprehensive descriptions that naturally weave in key attributes and real-world use cases.
Instead of describing your products with generic terms like “compact,” use contextual language like “ideal for dorm rooms“. This matches conversational searches and connects directly to how people actually ask questions.
You should also incorporate external validation directly into your pages. When a page like KitchenMag names your blender “best budget option,” feature that quote prominently on your product page. AI systems frequently cite authoritative statements, and hosting these accolades on your site increases the likelihood of being referenced as the source.
The goal is to create a comprehensive information hub that positions your product pages as the definitive resource AI would cite when answering related queries.
2. Use Multi-Channel Marketing Technique
The multi-channel marketing strategy (or surround-sound marketing) involves strategically placing your brand and products across multiple touchpoints throughout the digital ecosystem, creating consistent visibility no matter where users or AI systems encounter information about your category.
You need to use this multi-channel marketing strategy to ensure you’re not limited to just your site and that your brand appears consistently across the web. Partner with affiliates, influencers, and publishers to get your products featured in listicles, comparison guides, and reviews on high-authority domains. This ecosystem approach is essential for smaller brands competing against retail giants like Amazon.
If “Top 10” articles consistently appear in AI results for your product category, focus your efforts on earning placement in those pieces. Develop targeted affiliate programs and outreach campaigns, but be selective—prioritize authoritative, content-rich sites in your niche that AI systems are likely to reference.
Your strategy should focus on your most important product queries. Aim for multiple touchpoints: your product detail page, guest posts on relevant blogs, video reviews, and even strategic Quora responses from your team. This multi-angle approach increases the likelihood that AI encounters your product regardless of which sources it draws from.
Remember, quality matters more than quantity. Thin, templated content won’t earn AI citations simply because you paid for placement. The goal is to expand your share of voice across the information ecosystem that feeds AI responses. When users search for solutions your product provides, you want to be part of the conversation from multiple credible sources.
3. Improve your Ecommerce Technical SEO and AEO Performance with Prerender.io
If you’re wondering how to optimize a website for AI search, start by ensuring it is crawler-friendly—meaning it should be mobile-friendly, lightweight (quick page load speed), and have JavaScript-generated content optimized for fast indexing.
AI systems reward sites that are easy to crawl and parse, so you need to keep pages lean and fast. Most modern ecommerce sites rely heavily on JavaScript frameworks like React or Vue.js, and while this enhances the website’s functionality, it also presents challenges because AI crawlers can not render JavaScript. This is where technical SEO and AI SEO solutions like Prerender.io become essential.
Prerender.io works by creating a 100% indexed ready version of your ecommerce dynamic pages and serving them specifically to search engine crawlers and AI bots. When a human user visits your site, they are given the interactive JavaScript-powered web experience, but when Google’s AI or other crawlers arrive, they receive pre-rendered HTML that contains all your SEO product content.
Prerender.io ensures proper AI indexing for ecommerce sites by making your complete product catalog, customer reviews, and detailed specifications accessible to crawlers that might otherwise struggle with processing and indexing JavaScript-heavy pages.
So, if you’re wondering how to get recommended by ChatGPT, Prerender.io’s practical path is to make your pages clean and machine-readable, and you can get started now for free!
Learn more about Prerender.io’s process and benefits in this blog, or watch this quick explainer video.
4. Add Heading Tags and Optimize Product Page for Local SEO
You should also structure your content using semantic HTML and strategic heading tags to improve how it gets cited on ChatGPT and similar AI search platforms. Clearly label sections with descriptive headings like <h2>How to Choose the Right Size</h2> followed by concise answers.
Global brands need to address SEO localization proactively since AI systems don’t always handle geography well independently when determining how to get indexed by ChatGPT and other platforms.
Create region-specific content even when using the same language—a “best budget smartphones” query in the UK should find your UK-focused roundup with GBP pricing, not a US version with dollar amounts. Implement hreflang tags and utilise local terminology to assist AI systems in serving the relevant regional content.
This combination of clean semantic markup, content localization, and proper rendering ensures optimal AI indexing for ecommerce content that’s both accessible and contextually clear.
Pro tip: Track zero-source answers—a share of queries return AI summaries with no external citations. When that happens for topics tied to your brand (e.g., warranty terms, dimensions, and care instructions), ensure the information is accurate and add schema on your site and your knowledge panels. You may not earn the click, but you can prevent bad information from spreading.
5. Leverage Listicles and Comparison Content on Your Own Site
Traditional SEO wisdom warns against creating content that might cannibalize your own rankings, but AI-driven search may change this view.
For instance, publishing helpful comparison articles and “best of” guides on your own site can establish your domain as an authoritative source that AI systems naturally turn to for answers.
If you’re an ecommerce brand with multiple products, create comprehensive comparison guides like “Which XYZ Brand Vacuum is Right for You?” that help users choose between your different models. These pieces directly satisfy “X vs. Y” search queries while keeping traffic within your ecosystem.
Retailers can take a broader approach with guides like “Best Kitchen Gadgets for Tiny Apartments” that thoughtfully mix products from across your catalogue.
Source: Rtings.com
The goal is for your guide to be the one the AI uses to answer a query, which not only gives you a citation, but possibly multiple links if it cites your page and then the individual product pages from your domain. If you can get into SGE, there’s a chance to secure multiple link placements from one domain in a single answer.
6. Add Social Proof and Customer Voices to Your Ecommerce Website
We already discussed reviews, but you can take that further by surfacing customer Q&As, testimonials, and use-case stories on your site.
If you have a product that’s often recommended for a certain scenario, include a quote from a customer review about that scenario. For example, “‘I bought this tent for a winter camping trip in the Rockies and it withstood a night of heavy snow’ – Jane D.”. An AI summarizing “best winter tents” could latch onto that detail and cite your page.
Also, answer questions transparently: If users often ask “Is this furniture easy to assemble?”, have an answer from your support or community on the page. LLMs often pull Q&A content directly, so if you preemptively put Q&As on your product pages (or in a dedicated FAQ section of your site), you’re feeding the AI ready-made responses.
7. Experiment with AI-Specific Content for Better ChatGPT Indexing
As a forward-looking strategy, consider creating content specifically with AI consumption in mind. For instance, publish a brief expert article that directly answers a common question (something that might not have huge search volume, but could be a stump query in AI).
Use a conversational question as the title and answer it definitively in the text. This could be as granular as “How do I remove coffee stains from a leather sofa?” If that’s relevant to your business (say you sell leather sofas or cleaning products), having the most straightforward answer could land you a mention when someone asks an AI assistant the same question.
You can also maintain a glossary or knowledge base on your site for topics related to your niche. Not only does that help traditional SEO, but it also positions your site as a credible and factual resource. We’re talking about earning the AI’s trust the way you’d earn a regular search engine’s trust by being the best answer provider.
8. Avoid Black-Hat SEO or Manipulative SEO Tactics
It may be tempting to think of ways to “trick” the AI or game the system (like this post suggests). While that may seem great in the short term, you have to remember that AI models have additional layers of quality and can read context in a way simpler algorithms couldn’t.
Tactics that undermine the user experience will likely be ignored by the AI or even trigger it to exclude a site. Focus on genuine improvements. For example, do not auto-generate content just for volume. This could lead to the AI summarizing something incorrectly if your content is fluff. Quality over quantity is even more true here: one authoritative page can yield multiple citations, whereas dozens of thin pages will yield none.
Get Your Ecommerce Website Ready for AI Generative Search
AI-driven search results are reshaping the ecommerce landscape, demanding that brands rethink their content and SEO strategies. We’ve seen that winning in this new environment requires a blend of traditional SEO best practices and new approaches tailored to AI—from structuring data for machine readability to producing content that an AI would deem citation-worthy.
The data clearly shows both the threats (declining organic clicks, new competitors on your branded queries) and the opportunities (higher engagement from AI referrals, chances to appear in multiple positions via one query) that generative search brings.
It’s important to underscore that this report captures a moment in time. The “state of AI search” will continue to evolve rapidly. Google’s SGE and similar tools are effectively beta products, and their algorithms and policies (like how many sources to show, or how to handle copyrighted content) can shift with little notice.
The silver lining is that the core goal of ecommerce SEO hasn’t changed: provide the best, most relevant information to consumers.
AI is simply forcing everyone to do that in a more direct and data-backed way. Brands that invest in high-quality content, that genuinely answer customer queries, and that stay agile in their SEO/SEM tactics are poised to not only survive but thrive in the era of AI-driven search. The algorithms may change, but a brand’s commitment to understanding and serving its customers will always be a winning strategy.
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