For years, the eCommerce funnel thrived on search engine optimization, but the era of AI discovery has ushered in a new funnel. It used to be that shoppers would put their queries into Google, browse several sites, and check multiple products as they narrowed down their choices. eCommerce funnel Today, shoppers are turning to answer engines like ChatGPT, Gemini, Claude, Perplexity, and some eCommerce-native AI platforms. As such, eCommerce are making major model changes to adapt to the AEO (Answer Engine Optimization) new entry point. Something else unique to the new AI eCommerce model is that it helps shoppers almost come to a decision, compared to the era of SEO, where shoppers have to check product listing pages and weigh their options. Read on as we discuss AI answers serving as the new gatekeepers of what product shoppers see. AEO Becomes the New Top of the eCommerce Funnel AEO Becomes the New Top of the eCommerce Funnel AEO (Answer Engine Optimization), as is mostly used in the eCommerce context, is the process of optimizing product data, content, and PDP signals so that AI assistants and LLMs can list your products when answering consumer shopping questions. PDP With the use of these AI models soaring, AEO is replacing the traditional SEO-driven discovery, and eCommerce platforms have to adjust. Despite traditional still dominating AI searches in terms of traffic, data highlights AI queries resulting in higher conversion rates. This is super significant for an eCommerce business, and the reason for the higher conversion makes a lot of sense. Imagine a shopper asks their AI tool for “the best budget laptops under $500” or the “fastest shipping air fryer”. The AI links the user to a couple of PDPs, shortening the typical funnel in a traditional search. the best budget laptops under $500 fastest shipping air fryer As AI now chooses the products that qualify to appear in the answer, the crux of the issue for eCommerce sites is how to ensure their products make it into AI suggestions. Here are some of the specific signals the AI considers in its selection process: Structured Attributes:  When AI models filter products in response to a query, they do so in a way that mimics a data-trained personal shopper. Structured attributes serve as the primary matching signal and could be the number one reason why your product appears or doesn’t appear in AI recommendations. It helps the AI identify what the product is, who it serves, what category it belongs to, the problems it solves, and how it compares to its alternatives.
For someone shopping for electronics, the AI would scrape for details such as processor type, RAM, battery capacity, ports, size, etc., that best suit the user's query. As such, it’s important that your PDP does not miss any major keywords that are associated with your product.


Reviews: Like humans, AI also needs to analyze reviews for social proof. AI is also trained to analyze reviews beyond the star rating. AI performs sentiment analysis to check for positive (“worth it”, “amazing quality”) and negative (“returned”, “broke after use”) language. It gives more weight to recent reviews and those that match the requester’s needs, bringing straightforward logic to find a product that fits the query. Structured Attributes:  When AI models filter products in response to a query, they do so in a way that mimics a data-trained personal shopper. Structured attributes serve as the primary matching signal and could be the number one reason why your product appears or doesn’t appear in AI recommendations. It helps the AI identify what the product is, who it serves, what category it belongs to, the problems it solves, and how it compares to its alternatives.
For someone shopping for electronics, the AI would scrape for details such as processor type, RAM, battery capacity, ports, size, etc., that best suit the user's query. As such, it’s important that your PDP does not miss any major keywords that are associated with your product. Structured Attributes:  When AI models filter products in response to a query, they do so in a way that mimics a data-trained personal shopper. Structured attributes serve as the primary matching signal and could be the number one reason why your product appears or doesn’t appear in AI recommendations. It helps the AI identify what the product is, who it serves, what category it belongs to, the problems it solves, and how it compares to its alternatives. Structured Attributes: For someone shopping for electronics, the AI would scrape for details such as processor type, RAM, battery capacity, ports, size, etc., that best suit the user's query. As such, it’s important that your PDP does not miss any major keywords that are associated with your product. Reviews: Like humans, AI also needs to analyze reviews for social proof. AI is also trained to analyze reviews beyond the star rating. AI performs sentiment analysis to check for positive (“worth it”, “amazing quality”) and negative (“returned”, “broke after use”) language. It gives more weight to recent reviews and those that match the requester’s needs, bringing straightforward logic to find a product that fits the query. Reviews: Like humans, AI also needs to analyze reviews for social proof. AI is also trained to analyze reviews beyond the star rating. AI performs sentiment analysis to check for positive (“worth it”, “amazing quality”) and negative (“returned”, “broke after use”) language. It gives more weight to recent reviews and those that match the requester’s needs, bringing straightforward logic to find a product that fits the query. Reviews: Availability: AI double-checks the product's availability before deciding on grade recommendations. It checks stock accuracy, whether products are out of stock, low-stock, backordered, unavailable in the shopper’s region, or lack availability details. Availability: AI double-checks the product's availability before deciding on grade recommendations. It checks stock accuracy, whether products are out of stock, low-stock, backordered, unavailable in the shopper’s region, or lack availability details. Availability Price: As a human would, AI optimizes for value by comparing product prices against competitors, category averages, recent price drops, bundled offers, and more. Knowing this, your product shouldn’t be significantly higher without justification. Here are examples of some “good justification” to price higher: extra features, longer warranty, faster delivery, etc.
Product Clarity: This is essentially a sum of all the points listed above. Answer engines prefer PDPs that have clear and structured descriptions. Rather than “high quality product with great performance”, answer engines prefer descriptions that focus on the features and qualities that make the product high quality. As such, use bullets for specs, features, FAQs, and comparison tables, with a clear description of the product, the problem it solves, and who it’s for. Price: As a human would, AI optimizes for value by comparing product prices against competitors, category averages, recent price drops, bundled offers, and more. Knowing this, your product shouldn’t be significantly higher without justification. Here are examples of some “good justification” to price higher: extra features, longer warranty, faster delivery, etc. Price Product Clarity: This is essentially a sum of all the points listed above. Answer engines prefer PDPs that have clear and structured descriptions. Rather than “high quality product with great performance”, answer engines prefer descriptions that focus on the features and qualities that make the product high quality. As such, use bullets for specs, features, FAQs, and comparison tables, with a clear description of the product, the problem it solves, and who it’s for. Product Clarity Rather than the traditional SERP(Search Engine Results Page), the visibility metric for AEO is now “Answer Rank”, referring to your product’s position in an AI-generated recommendation. The New Compressed AEO Funnel The New Compressed AEO Funnel AI has shortened the typical buyer journey on the old eCommerce funnel, and this is definitely for the better. In the old funnel, shoppers had to pass through at least six steps before buying. The typical journey would be Search → PLP → Category page → Filters → Comparison pages → Product Page → Cart → Checkout. buyer journey Search → PLP → Category page → Filters → Comparison pages → Product Page → Cart → Checkout. But the funnel risked losing shoppers at each stage. AI collapsing the middle funnel now brings buyers from the prompt straight into a product page. If a user asks an AI, “Which monitor under $200 is best for gaming?”, the AI would respond with its top 3-5 options.The new compressed funnel now becomes:AI Answer → PDP → Cart → Checkout. Once the AI provides its top options, the shopper is mid-funnel (eliminating the risk of losing them in the old funnel) and is likely to have high intent. AI Answer → PDP → Cart → Checkout The High-Intent Advantage The High-Intent Advantage eCommerce sites are paying more attention to AEO because the traffic they provide behaves differently from typical search engine traffic. Answer engines direct high-intent traffic to eCommerce sites; a sharp contrast from the cold, casual browser who may still be unsure what they want. By the time a shopper clicks on an AI answer, they have partly decided to make a purchase. It also helps that shoppers have trusted the AI to do all the vetting and filter the best products that suit their query. As if that is not already interesting enough for eCommerce businesses, answer engines also show exceptionalism in increasing Average Order Value (AOV). Because AI assistants understand user intent and the product ecosystem, they can recommend bundle offers and complementary offers at the time of decision. Average Order Value (AOV). If a user is looking to buy a camera, the AI can recommend an SD card, a protective carrying case, and a microfiber cleaning cloth, provided it has enough context. Measuring AEO Success: The New KPIs Measuring AEO Success: The New KPIs With AEO as the new entry point of the eCommerce funnel, traditional SEO metrics do not capture the full picture. Search ranking and organic sessions are falling behind as KPI metrics, and now, eCommerce businesses are looking at how often and how confidently AI systems recommend their product. Now the key AEO metrics to monitor are answer rank, AI impression share, PDP visits from AI answers, add-to-cart rate from AI recommendations, and AOV uplift. Measuring these AEO metrics can reveal how much impact answer engines are having on your eCommerce business. So instead of organic clicks or SERP impressions, it now makes sense to focus more on the AEO metrics listed above to take advantage of this amazing new funnel.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

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AEO (Answer Engine Optimization): The New Entry Point of the eCommerce Buying Funnel

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