AI Visibility for Pet Brands: Why Chewy and PetSmart Dominate Every AI Recommendation

The shift: from “best dog food” to “ask the AI”

The pet industry has always been driven by trust. Pet owners choose brands based on veterinarian recommendations, peer referrals, ingredient research, and trial and error. But the starting point of that journey has moved online — and it’s now moving again, from Google to AI chatbots.

67% of pet owners research products online before purchasing, according to Packaged Facts’ 2024 Pet Market Outlook. Google’s own data shows that searches for “best dog food” consistently rank among the most competitive commercial queries in the US, with over 300,000 monthly searches. The American Pet Products Association (APPA) reports that online pet product sales reached $38.4 billion in 2024, representing roughly 36% of all pet spending — up from 30% in 2022.

That online-first behavior is now colliding with the AI chatbot wave.

Gartner forecast in February 2024 that traditional search engine volume will drop 25% by 2026 due to AI chatbots and virtual agents. ChatGPT reached 1.8 billion monthly visits by late 2024, making it one of the top 10 most-visited sites on the planet. Perplexity AI grew to over 100 million monthly visits by Q4 2024. Pew Research Center found that 23% of US adults had used ChatGPT by early 2024 — a figure that rises to 43% among adults aged 18–29.

The queries are changing too. Instead of typing “best dog food for allergies” into Google and scanning ten review articles, a pet owner asks ChatGPT: “What’s the best dog food for a French Bulldog with sensitive skin?” or “Which pet insurance company has the best coverage?” or “Compare Chewy vs. PetSmart for monthly pet subscriptions.” The AI responds with a narrative answer — mentioning specific brands, specific products — and the pet owner follows that recommendation without ever seeing your brand in a search result.

Millennial and Gen Z pet owners, who now represent the largest pet-owning demographic (APPA, 2024), are particularly prone to AI-first discovery. These are the same cohorts spending $126.8 billion on their pets annually. The traditional funnel — Google search → review site → product page → purchase — is being bypassed entirely. And for a $150 billion industry built on brand loyalty, the implications are enormous.

Who AI actually recommends for pet products

We asked. Across hundreds of queries to ChatGPT, Perplexity, Gemini, Claude, and Grok, using pet-owner-intent prompts like “What is the best dog food?” “Where should I buy pet supplies online?” and “What’s the best pet insurance?” — the same names appear over and over:

The pattern is clear. Chewy — publicly traded (NYSE: CHWY), with $11.15 billion in net sales for fiscal year 2024 and over 20 million active customers — dominates AI responses across every pet category. PetSmart, the largest specialty pet retailer with approximately 1,650 stores and an estimated $9.7 billion in annual revenue, follows closely. Petco (NASDAQ: WOOF), with approximately 1,500 stores and $6.3 billion in revenue (2024 annual report), rounds out the top three.

On the manufacturer side, Mars Petcare — with an estimated $25 billion in global pet care revenue spanning Royal Canin, Pedigree, Whiskas, IAMS, and Banfield Pet Hospitals — and Nestlé Purina PetCare — with approximately $17 billion in global sales (Euromonitor, 2024) — dominate product-level recommendations. The two companies together control roughly 60% of the global pet food market by value.

DTC pet brands, independent pet stores, emerging pet health companies, and breed-specific specialty brands are almost never mentioned. This includes brands with passionate customer bases, superior ingredient profiles, and strong review scores that simply lack the web corpus volume to register in AI training data.

This isn’t a bug. It’s how these systems work. And for an industry increasingly defined by premiumization, personalization, and niche specialization, the consequences are severe.

Why your pet brand is invisible to AI

AI chatbots generate recommendations based on patterns in their training data — billions of web pages, news articles, Reddit threads, review sites, and forum discussions. The brands that appear most frequently in that data are the ones AI recommends.

Consider the math:

• Chewy.com receives roughly 120–140 million monthly website visits (SimilarWeb, 2024), publishes thousands of product reviews, breed guides, and pet health articles, and is cited across financial news, pet industry publications, and Reddit threads constantly.
• PetSmart.com generates approximately 50–60 million monthly visits, with extensive product content, grooming and adoption service pages, and local store listings that generate massive third-party citation volume.
• Petco.com receives roughly 30–40 million monthly visits, plus extensive veterinary services content and pet wellness coverage.
• A typical DTC pet brand website receives 50,000–500,000 monthly visits, has limited third-party press coverage, and appears on perhaps 10–20 review sites and directories.

That’s a 200x–2,800x gap in web presence. And web presence is what AI systems learn from.

Three specific factors determine whether AI mentions your pet brand:

1. Corpus frequency: How often your brand appears across the web. Chewy has millions of mentions across financial coverage (Wall Street Journal, CNBC, Motley Fool), pet forums (Reddit r/dogs, r/cats, r/pets), review sites, and product listings. A DTC pet food brand might have 500–2,000 total web mentions. When AI generates a response about “best dog food,” it surfaces the brands it encountered most frequently during training.
2. Source authority: AI weights authoritative sources more heavily. Royal Canin and Hill’s Science Diet are cited extensively in veterinary journal articles, PetMD, and the American Veterinary Medical Association (AVMA) resources. Chewy and PetSmart appear in financial analysis from Morgan Stanley and Goldman Sachs. A newer DTC brand may have excellent reviews on its own site but lacks authoritative third-party validation that AI trusts.
3. Content structure: The Princeton/Georgia Tech GEO study (2023) found that content with statistical citations and clear factual claims was up to 40% more likely to be cited by generative AI systems (Aggarwal et al., “GEO: Generative Engine Optimization,” 2023). Major pet brands publish structured product data, detailed nutritional analysis, AAFCO compliance statements, and feeding guides with specific quantities. Many DTC brands rely on lifestyle marketing (“real food for real dogs”) without the structured, citable data AI needs to extract and recommend.

Most emerging pet brands fail on all three. They have low corpus frequency compared to the giants, limited authoritative mentions outside their own marketing channels, and lifestyle-oriented content that AI can’t easily parse into recommendations. To understand these dynamics more broadly, read our guide on how brands show up in AI recommendations.

What AI gets wrong about pet brands

Even when AI does mention a pet brand, there’s a significant chance it gets the facts wrong. Our testing found AI gives incorrect or outdated information in approximately 30–40% of pet-specific queries. In an industry where incorrect product recommendations can affect animal health, accuracy isn’t optional. For more on this problem, see our deep dive on fixing AI hallucinations about your brand.

The most common errors we find in AI responses about pet businesses:

Pricing and availability

Pet food prices fluctuate frequently based on ingredient costs, supply chain disruptions, and promotional cycles. The Bureau of Labor Statistics reported that pet food prices increased 8.4% year-over-year in 2023, one of the highest inflation categories in the CPI. AI chatbots frequently cite pricing that is months or years old. A pet owner asking “How much does a 30-lb bag of Royal Canin cost?” might receive a figure that is $10–$20 off the current price. Worse, AI sometimes recommends specific product formulas that have been discontinued or reformulated — a common occurrence in the pet food industry where brands rotate SKUs regularly.

Breed-specific nutrition recommendations

One of AI’s most consequential errors in the pet space involves breed-specific dietary advice. Different breeds have vastly different nutritional needs — a Great Dane puppy requires carefully controlled calcium-to-phosphorus ratios to prevent developmental orthopedic disease, while a Dalmatian needs low-purine diets to prevent urate stones. AI frequently provides generic nutritional advice tagged to specific breeds, conflates puppy and adult nutritional requirements, or recommends brands for breed-specific conditions without verifying that the product actually meets those specific nutritional parameters.

Veterinary diet claims

Prescription and veterinary diets (Hill’s Prescription Diet, Royal Canin Veterinary Diet, Purina Pro Plan Veterinary Diets) require veterinary authorization. AI sometimes recommends these products without noting the prescription requirement, or conflates veterinary-exclusive products with over-the-counter versions of the same brand. The FDA’s ongoing investigation into the potential link between grain-free diets and dilated cardiomyopathy (DCM) in dogs — which has generated over 1,100 reports to the FDA since 2018 — is another area where AI provides inconsistent and sometimes dangerously outdated guidance.

Pet insurance coverage details

The pet insurance market has grown to approximately $4.5 billion in the US (North American Pet Health Insurance Association, NAPHIA, 2024), with over 5.36 million pets insured. AI frequently provides incorrect coverage details, outdated premium estimates, and wrong waiting period information. Premium pricing varies significantly by breed, age, location, and coverage level — a pit bull in New York City pays 2–3x what a mixed breed in Iowa pays. AI typically cites national averages that are meaningless for individual decisions.

Product ingredient lists and sourcing

Pet food ingredient lists are regulated by AAFCO and the FDA, and formulations change frequently. AI sometimes cites ingredient lists from old formulations, attributes ingredients from one product line to another within the same brand, or makes sourcing claims (e.g., “made in the USA”) that don’t reflect current manufacturing practices. For premium and DTC brands where ingredient quality is the core value proposition, AI getting the ingredients wrong undermines the brand’s primary differentiator.

The $150 billion market AI is reshaping

The US pet industry is massive — and still growing:

• Total US pet industry spending exceeded $150 billion in 2024 (APPA National Pet Owners Survey, 2024–2025), up from $136.8 billion in 2022 and $123.6 billion in 2021.
• Pet food and treats represent the largest category at $64.4 billion (APPA, 2024), followed by veterinary care and products at $38.3 billion, pet supplies and live animals at $31.5 billion, and other services (grooming, boarding, insurance, training) at $16.5 billion.
• Chewy reported $11.15 billion in net sales for fiscal year 2024 (annual report), with 20 million+ active customers and Autoship subscription revenue representing approximately 78% of net sales.
• Mars Petcare generates an estimated $25 billion in global pet care revenue across pet food (Royal Canin, Pedigree, Whiskas, IAMS, Nutro) and veterinary services (Banfield, VCA, BluePearl) — making it the largest pet care company in the world.
• Nestlé Purina PetCare generated approximately $17 billion in global sales in 2024 (Euromonitor, 2024), making it the second-largest pet food company globally.
• The pet insurance segment reached $4.5 billion in the US (NAPHIA, 2024), growing at 21.7% CAGR since 2019, with Morgan Stanley projecting the market could reach $12–$15 billion by 2030.
• The global pet care market is projected to reach $350 billion by 2027 (Euromonitor International), growing at 6–7% annually.

Yet despite its size, the pet industry’s AI visibility landscape is heavily concentrated. The top 5 retailers and top 5 manufacturers capture the overwhelming majority of AI recommendations. The hundreds of DTC brands, independent pet stores, emerging pet health companies, and specialized service providers that collectively represent billions in annual revenue are largely invisible to AI.

Packaged Facts reports that premium and super-premium pet food now accounts for over 40% of total pet food sales in the US, up from 30% a decade ago. This premiumization trend is driven by brands like The Farmer’s Dog, Ollie, NomNomNow, Open Farm, and Stella & Chewy’s — brands whose entire value proposition is being different from mass-market options. Yet AI consistently recommends the mass-market giants first, undermining the premiumization trend that is actually driving industry growth.

You can’t buy your way into a ChatGPT recommendation. There are no ad slots. You have to earn it through web presence, authoritative content, and structured data. And right now, only a handful of companies are earning it. For more on why this matters in B2B contexts too, see why B2B SaaS brands are invisible in ChatGPT.

Where pet brands get discovered — and what’s changing

Understanding the discovery landscape reveals how AI is shifting the competitive dynamics. A pet brand visible on Google may be completely invisible in AI — and the migration from one to the other is accelerating. Learn more about how we measure AI visibility across these channels.

The gap between Google and AI recommendations for pet products is significant. On Google, a well-optimized DTC pet brand can compete for long-tail queries like “best fresh dog food delivery” or “grain-free cat food for sensitive stomach.” Paid search gives smaller brands access to the same visibility real estate as Chewy. In AI chatbot responses, there is no paid option. The same 3–4 giant retailers and 3–4 legacy manufacturers appear whether the query is generic or highly specific.

Particularly concerning: AI collapses the research funnel. On Google, a pet owner might click through 5–8 review sites, visit 3–4 brand websites, and compare options across multiple sessions before purchasing. In AI, they get a single curated recommendation in one response. If your brand isn’t in that response, you don’t get a second chance. The “discovery window” has shrunk from multiple touchpoints over days to a single AI-generated paragraph in seconds.

What actually works: the AI visibility playbook for pet brands

The good news: AI visibility is a solvable problem. And because almost no one in the pet industry is working on it strategically, early movers have a disproportionate advantage. Here’s what works, based on our research into turning AI visibility data into action.

1. Audit what AI currently says about you

Before fixing anything, you need to know what’s broken. Query ChatGPT, Perplexity, Gemini, and Claude with prompts your customers would actually use:

• “What is the best dog food for [specific breed]?”
• “Tell me about [your brand name] dog food”
• “Where should I buy pet supplies online?”
• “What is the best pet insurance for dogs?”
• “Compare [your brand] vs. [competitor]”
• “What are the best DTC pet food brands?”

Document every mention (or absence), every error, and every competitor that appears instead of you. Or run a Metricus AI visibility report that does this across hundreds of query variations automatically. For a quick start, try our free AI visibility check.

2. Publish data-rich, citable content

AI systems cite content that contains structured claims, statistics, and authoritative data. The GEO research from Princeton/Georgia Tech found that content with statistical citations was up to 40% more likely to be cited by generative AI (Aggarwal et al., “GEO: Generative Engine Optimization,” 2023).

For pet brands, this means:

• Detailed nutritional analysis pages with specific macronutrient percentages, caloric content per cup/can, AAFCO feeding trial statements, and guaranteed analysis comparisons. Not “packed with protein” — but “32% crude protein from deboned chicken, chicken meal, and turkey meal, meeting AAFCO Adult Maintenance nutritional profiles through feeding trials.”
• Breed-specific feeding guides with specific quantities by weight, age, and activity level. AI struggles with breed-specific recommendations because most brands provide generic feeding charts. A brand that publishes “Feeding Guide for French Bulldogs: 2026 Data” with specific caloric recommendations gives AI a citable, authoritative source to reference.
• Transparent pricing pages with current prices, price-per-ounce comparisons, and subscription savings calculations. The pet industry’s reluctance to publish pricing (“check your local store” or “see Chewy for current pricing”) cedes the pricing narrative entirely to AI, which then guesses.
• Ingredient sourcing pages with specific claims: country of origin, facility certifications, supply chain details. This is the content premium and DTC brands should own — and that AI is desperate to cite when answering questions about ingredient quality.
• Veterinary-reviewed content: Resource articles on common pet health conditions, nutrition science, and breed-specific health concerns. Content reviewed or co-authored by veterinarians carries significantly more authority in AI training data than marketing copy.

3. Build citations on authoritative third-party sources

AI doesn’t just read your website. It reads everything about you across the web. The sources that carry the most weight for pet brands:

• PetMD, Dogster, Catster — major pet information platforms that AI heavily references. Getting your brand mentioned, reviewed, or featured in editorial content on these sites significantly increases AI citation likelihood.
• Dog Food Advisor — one of the most-cited pet food review sites in AI responses. Having an accurate, complete listing here matters.
• Veterinary publications and AVMA resources — for veterinary diet and pet health brands, authoritative medical citations are gold for AI visibility.
• Reddit (r/dogs, r/cats, r/pets, r/rawpetfood, breed-specific subreddits) — AI heavily weights genuine community discussions. Authentic brand mentions in Reddit threads carry significant corpus weight.
• Wirecutter / NYT Reviews, Forbes Advisor, Business Insider — “best of” roundups from high-authority publications are among the most-cited sources in AI pet product recommendations.
• Chewy.com product reviews — even if Chewy is your competitor as a retailer, having your products listed and well-reviewed on Chewy generates citations that AI can reference. The same applies to Amazon reviews.
• APPA and industry association mentions — being referenced in industry reports and data publications adds authoritative corpus presence.

4. Fix your structured data

Implement comprehensive schema markup on your website:

• Product schema with detailed offers, pricing, availability, brand, and review data for every product page
• FAQPage schema for common pet owner questions (ingredients, feeding amounts, pricing, breed suitability)
• Review and AggregateRating schema for customer reviews
• NutritionInformation (where applicable) and detailed product descriptions in schema
• Organization schema with brand details, founding year, certifications, and veterinary advisory board information

Structured data helps AI systems understand what your brand offers, how your products compare, and what makes them different — even when your website has less raw traffic than Chewy or PetSmart.

5. Own your niche with condition-specific and breed-specific content

This is where smaller pet brands can genuinely outperform the giants. Chewy has breadth but not depth. A DTC brand focused on senior dog nutrition can publish more detailed, authoritative content on glucosamine requirements, omega-3 fatty acid research for joint health, and age-adjusted caloric needs than a generalist retailer ever will. Build topical authority in your specific niche:

• Breed-specific nutrition guides (data-rich, not just marketing)
• Condition-specific feeding recommendations (allergies, kidney disease, weight management)
• Life-stage transition guides (puppy to adult, adult to senior) with specific nutritional benchmarks
• Ingredient science explainers that cite peer-reviewed veterinary nutrition research

When a pet owner asks AI “What is the best food for a dog with kidney disease?” — a query where generic retailer content falls short — the brand with the most authoritative, specific answer in the training corpus is the one AI cites.

6. Correct errors at their source

If AI is getting your pricing, ingredients, or product details wrong, the error is coming from somewhere. Usually it’s an outdated product listing on Chewy or Amazon, stale information on Dog Food Advisor, an old review article from a pet blog, or inconsistent data across your own web properties and retail partners. Find the source, fix it, and the AI corrections will follow over time as models retrain on updated data.

The case for auditing your AI visibility now

The pet industry is at an inflection point. The APPA reports that 66% of US households — approximately 86.9 million homes — own a pet, the highest rate ever recorded. Spending continues to climb. The humanization of pets — treating them as family members, not just animals — drives premiumization across food, health, and services. And the discovery mechanism for all of these purchases is shifting from search engines to AI chatbots.

The pet brands that understand their AI visibility now — while competitors are still focused exclusively on Amazon ads, Google Shopping, and Instagram influencers — will have a structural advantage that compounds over time. Every piece of authoritative, data-rich content you publish today enters the training data that shapes AI recommendations tomorrow.

The cost of waiting is real. Consider the math for a DTC pet food brand:

• Average customer lifetime value for a DTC pet food subscription: $1,200–$3,600 (12–36 months at $100/month average order value)
• If 10% of potential customers now start their purchase journey with AI (a conservative estimate given adoption trends), and AI never mentions your brand, the lost customer math is significant
• For a DTC brand acquiring 5,000 new customers per year, 10% AI-driven discovery loss = 500 lost customers = $600,000–$1.8 million in lost lifetime revenue
• For a pet retailer with higher transaction volumes, the numbers scale accordingly — Petco’s $6.3 billion revenue means even a 1% shift in discovery patterns represents $63 million in at-risk revenue

Morgan Stanley’s 2024 analysis of the pet industry noted that e-commerce penetration in pet products continues to accelerate, with online channels expected to capture over 40% of pet product sales by 2027. As that online channel increasingly includes AI-mediated recommendations, the brands that are visible in AI responses will capture a disproportionate share of growth.

The pet insurance segment illustrates this perfectly. NAPHIA data shows that pet insurance grew 26% year-over-year in 2023, with total premiums reaching $4.5 billion. When a pet owner asks ChatGPT “What is the best pet insurance?” — an increasingly common query — the brands that appear in the response capture the consideration set. Our testing shows Lemonade, Trupanion, and Healthy Paws dominate these responses, while dozens of competitive insurers with strong coverage and competitive pricing are invisible.

Sources: American Pet Products Association (APPA) National Pet Owners Survey (2024–2025); Packaged Facts Pet Market Outlook (2024); Euromonitor International global pet care data (2024); Morgan Stanley pet industry analysis (2024); North American Pet Health Insurance Association (NAPHIA, 2024); Bureau of Labor Statistics CPI pet food data (2023); FDA DCM investigation updates (2018–2024); Chewy Inc. fiscal year 2024 annual report; Petco Health and Wellness 2024 annual report; Mars Petcare corporate data; Nestlé Purina PetCare segment reporting (2024); SimilarWeb traffic estimates (2024); Gartner search prediction (Feb 2024); Pew Research Center AI adoption survey (2024); Princeton/Georgia Tech GEO study (Aggarwal et al., 2023). AI mention rates based on Metricus internal testing across ChatGPT, Perplexity, Gemini, Claude, and Grok (2026). Learn more about how we measure AI visibility.

Related reading

• The 5-step AI visibility action plan — the general framework for turning audit findings into fixes.
• Fixing AI hallucinations about your brand — the deep dive on correcting factual errors at their source.
• What is AI visibility? — the complete explainer on how brands appear in AI.
• Why B2B SaaS brands are invisible in ChatGPT — the same dynamic in a different industry, with transferable strategies.
• Free AI visibility check — run a quick manual check before ordering a full report.
• AI visibility scores explained — how Metricus measures and benchmarks AI visibility.