I asked ChatGPT to recommend where to buy running shoes online for someone training for their first marathon. It recommended "Fleet Feet" as its top pick, followed by Running Warehouse and Road Runner Sports. Amazon, the platform where most running shoes are actually purchased, appeared only as a caveat ("if you know exactly what you want, Amazon has the widest selection and fastest shipping"). I ran the same query on Perplexity and Gemini. All three engines prioritized specialty retailers with expert content and fitting guidance over mass-market platforms with the largest inventory.
For ecommerce brands competing against Amazon's dominance, this is a significant signal: AI search engines recommend based on expertise and buying experience, not inventory breadth or shipping speed. Specialty knowledge wins when the query implies uncertainty.
The Experiment
I asked three AI search engines: "Where should I buy running shoes online? I'm training for my first marathon and not sure what I need. Want somewhere with good guidance on selection."
ChatGPT's Response
ChatGPT recommended four retailers in order of fit, with descriptions emphasizing guidance quality over product range.
- Fleet Feet — described as "virtual fitting tool, expert recommendations by foot type and training goal, free returns, local store backup for in-person fitting"
- Running Warehouse — highlighted for "deep runner community, detailed shoe reviews by actual runners, fit guarantee, specializes in performance running"
- Road Runner Sports — noted for "Shoe Dog AI fitting tool, VIP membership with 90-day wear test, marathon-specific category pages"
- REI — described as "not running-specific but strong return policy, staff picks with detailed use-case descriptions, outdoor-focused selection"
Perplexity's Response
Perplexity gave three recommendations citing a Runner's World buying guide, an r/running thread about where to buy marathon shoes, and a running blog comparing online shoe retailers.
- Running Warehouse — overlap with ChatGPT, cited from the Runner's World guide
- Fleet Feet — overlap with ChatGPT, cited from the running blog
- Jackrabbit (formerly Running Specialty Group) — cited from the Reddit thread
Gemini's Response
Gemini recommended four retailers with emphasis on technology and service differentiation.
- Fleet Feet — overlap with both others, noted for "3D foot scanning technology and online fit quiz"
- Brooks Running (direct) — described as "Shoe Finder quiz by running style, free shipping and returns, marathon training resources on site"
- Running Warehouse — overlap with others
- Hoka (direct) — noted for "popular for first-time marathoners, wide cushioned options, training guides integrated with product pages"
What Google and Amazon Show vs. What AI Shows
Google's results for "buy running shoes online" were Amazon (dominating with product ads), Nike.com, and various paid shopping ads from DSW, Zappos, and big-box retailers. Amazon's algorithm sorted by bestseller rank, price, and Prime eligibility.
AI search engines completely reframed the question. Instead of showing where to find the cheapest or most popular shoes, they recommended where to find the best guidance for someone who doesn't know what they need. The query's "not sure what I need" and "good guidance" signals shifted the recommendation from transactional (cheapest price, fastest shipping) to advisory (best expertise, best fitting process).
What the Recommended Retailers Had in Common
They published expert buying guidance. Fleet Feet's fitting quiz, Running Warehouse's runner-written reviews, and Brooks' Shoe Finder all represented published expertise that AI engines could reference. Retailers that help customers make decisions, rather than just listing products, matched the guidance-seeking intent of the query.
They were discussed in running communities. r/running, r/RunningShoeGeeks, and marathon training forums extensively discuss where to buy and what to buy. Retailers recommended by actual runners in these communities had peer-validation signals AI engines weighted heavily. The running community is passionate about shoe selection, making community discussion a dominant signal.
They had content matching the buyer's journey stage. First-time marathon trainers don't know what they need. Retailers with content addressing this uncertainty (fitting guides, "what shoe for my first marathon," training-specific recommendations) matched the query's stage better than retailers with product-catalog-first websites.
They differentiated on expertise, not price or selection. No AI engine recommended "cheapest place to buy shoes" or "largest selection." Every recommendation was justified by the retailer's ability to guide a purchase decision. Expertise-forward positioning matched the query's need for guidance.
What the Missing Retailers Lacked
Transactional-only positioning. Amazon offers everything, ships fast, and often has the lowest price. But when someone needs guidance, Amazon's recommendation algorithm ("customers also bought") is no substitute for expert curation. AI search engines recognized this and didn't recommend Amazon for a guidance-seeking query.
No published expertise. Zappos has great return policies and huge selection but doesn't publish marathon training guides or running shoe fit content. Without expert content, AI engines have no basis to recommend it for a query where expertise is the primary requirement.
Big-box generalization. DSW, Foot Locker, and Dick's Sporting Goods carry running shoes among thousands of other products. Their websites aren't structured to provide running-specific guidance, and their content doesn't address marathon training needs. Generalists lost to specialists.
No community presence in running spaces. Retailers never discussed in running forums, never recommended by runners to other runners, had no community validation signal. For performance athletic purchases, community word-of-mouth is the strongest trust signal.
What Ecommerce Brands Should Do
Publish expert buying guidance for specific use cases. Don't just list products. Help buyers decide. "Best Running Shoes for Your First Marathon (2026 Guide)" with specific recommendations by foot type, pace, and weekly mileage is exactly what AI engines cite when someone asks for buying guidance. Open with direct recommendations, not marketing copy. Ecommerce brands optimizing for AI visibility see the strongest results from use-case-specific buying guides.
Build tools that demonstrate expertise. Fit quizzes, recommendation engines, buying guides by use case, and comparison tools all serve as evidence of expertise that AI search engines reference. Fleet Feet's fitting tool and Brooks' Shoe Finder appeared in AI descriptions because they demonstrate advisory capability.
Create content for uncertainty, not just certainty. Most ecommerce content assumes the buyer knows what they want and just needs to find it. AI search queries increasingly come from buyers who don't know what they need. Content addressing "I'm new to this, help me decide" captures these guidance-seeking queries that AI search engines answer with recommendations, not product listings.
Earn presence in enthusiast communities. Monitor r/running, r/RunningShoeGeeks, and niche sport communities. When customers recommend your store in these threads, it creates the peer-validation signal AI engines rely on. Encourage satisfied customers to share their buying experience in relevant communities. Why Reddit matters for AI search explains the mechanism.
Get featured in category editorial content. Runner's World buying guides, sport-specific comparison articles, and category authority publications drive AI recommendations for physical products. Being included in "best places to buy running shoes" editorial content is the single highest-leverage AI search signal for ecommerce retailers.
Consider direct-to-consumer content strategy. Brooks and Hoka appeared in AI recommendations through their own websites because they published marathon-specific content alongside their products. Brands selling direct can earn AI recommendations by publishing expertise content (training guides, fit information, use-case recommendations) that AI engines match to advisory queries.
How Long It Takes
Weeks 1-4: Publish 4-6 use-case-specific buying guides. Build or improve recommendation tools (fit quiz, use-case filter). Identify 3-5 editorial publications that review retailers in your category.
Months 2-3: First AI appearances for guidance-seeking queries ("where to buy marathon shoes first time," "best online store for running shoe advice"). Earn inclusion in 1-2 editorial buying guides. Generate community mentions from satisfied customers.
Months 3-6: Consistent AI presence for your category's advisory queries. Continue publishing seasonal and use-case content. Build community reputation. Track which engines recommend you for which buyer journeys.
Ecommerce AI search is fundamentally different from transactional search. Google Shopping answers "find this product at the best price." AI search answers "help me decide what to buy and where." Retailers that publish expertise and guidance for uncertain buyers will capture a growing share of purchase-intent queries that never reach Amazon's search bar.
The Loudmink AEO platform tracks how ecommerce brands appear across all five major AI search engines and identifies which product and category queries trigger competitor recommendations. Plans from $99/mo.
Frequently Asked Questions
Does my Amazon presence help with AI search?
Not for guidance-seeking queries. Amazon dominates when buyers know exactly what they want. But when queries include uncertainty ("not sure what I need," "looking for guidance"), AI engines recommend expertise-forward retailers over marketplaces. Your Amazon presence and organic search visibility serve different buyer stages than AI search.
Will shoppers buy through AI search recommendations?
Not directly (AI engines don't process payments). But AI recommendations shape which retailers buyers visit. A recommendation from ChatGPT drives traffic to your site where the purchase happens. This is top-of-funnel influence: the AI shapes consideration, your site closes the sale.
Should DTC brands invest in AI search?
Yes, particularly if you have product expertise content. Brooks appeared in AI recommendations through expert content on their own domain (training guides, shoe finder). DTC brands that publish use-case expertise, not just product pages, can earn AI recommendations that bypass both Amazon and multi-brand retailers.
How important are return policies for AI recommendations?
Return policies appeared in AI descriptions for multiple retailers ("90-day wear test," "free returns," "fit guarantee"). Generous return policies reduce purchase risk, which AI engines highlight for uncertain buyers. Make your return policy prominent and specific in your content.
Does review volume on my own site matter?
Product reviews on your site create content AI engines can reference when building descriptions. But the reviews that matter most for AI recommendations are on external platforms (Reddit discussions, editorial reviews, community forums) because they serve as third-party validation of your expertise.