How pet supplies shoppers search now — and why your catalog isn't the answer
Pet shoppers now ask AI for recommendations, not just search terms. Here's why thin product data makes your catalog invisible — and what to fix first.
A pet parent shopping for a hip-and-joint supplement used to type "glucosamine chews for dogs" into Google and scroll ten blue links. Increasingly, they're asking ChatGPT, Gemini, or Google's AI Mode a real question instead — and the answer engine picks the products for them. If your catalog can't answer that question in structured, machine-readable form, you don't lose a ranking. You disappear from the conversation entirely.
The channel shift is already measurable
This isn't a future-tense story. Google's AI Overviews now surface on roughly 14% of shopping queries overall, up 5.6x in just a few months, and on "best [product]" style queries — exactly how pet owners search — the presence is far higher, climbing from about 5% to 83% year over year. Adobe's holiday 2025 data showed traffic to retail sites from generative AI sources up nearly 700% year over year, with those visitors converting 31% more often and spending far more time on-site than typical traffic.
Pet retail is not a bystander here. OpenAI's ChatGPT has moved into instant checkout with retail partners, Google Gemini is testing shopping integrations with pet-category retailers, and Amazon is steering more queries through its Rufus assistant instead of the search bar. Chatbots built specifically for pet stores — Petbarn's PetAI among them — are already fielding "what should I feed my senior cat with kidney issues" instead of routing shoppers to a category page.
The mechanism matters more than any single stat: AI answer engines don't rank pages, they assemble answers. They pull attributes out of your product feed, your schema markup, and your PDP copy, then recombine them into a recommendation. If the attribute isn't there in a parseable form, the AI can't use it — no matter how good your photography or brand copy is.
Why pet supplies data breaks AI recommendations specifically
Pet is a harder category for thin data to survive in than most, because the questions shoppers actually ask are conditional and multi-variable:
- "Safest grain-free food for an 8-year-old golden retriever with allergies"
- "Litter that's safe for a kitten with respiratory issues"
- "Joint supplement that won't upset a small dog's stomach"
None of those map to a single keyword. Each one requires the AI to cross-reference several structured facts at once: species, life stage, breed size, weight range, ingredient exclusions, and health-condition suitability. A raw supplier feed almost never carries all of these as queryable fields — most of it is buried in a paragraph of marketing prose, or missing outright.
Here's what that gap looks like on an actual product.
Raw feed, as it arrives from most pet suppliers:
| Field | Value |
|---|---|
| Title | Premium Hip & Joint Chews for Dogs |
| Description | "Support your dog's mobility with our vet-formulated chews." |
| Category | Dog > Supplements |
| Price | $34.99 |
Enriched, machine-readable version:
| Attribute | Value |
|---|---|
| Pet type | Dog |
| Life stage | Adult, senior (7+ years) |
| Breed size suitability | Medium, large, giant breeds (25 lbs+) |
| Key ingredients | Glucosamine 600mg, chondroitin 300mg, MSM |
| Form | Soft chew |
| Health condition fit | Osteoarthritis, post-surgical mobility support |
| Allergen flags | Grain-free, no artificial dyes |
| Serving guidance | 1 chew per 25 lbs body weight, daily |
| Contraindications | Not formulated for puppies under 12 months |
The raw version reads fine to a human skimming a page. It gives an AI system almost nothing to match against a specific question. The enriched version is the difference between "we sell joint chews" and being the answer when someone asks an AI to recommend a joint supplement for a large senior dog with early-stage arthritis.
The "ask an AI" test every pet retailer should run
Open ChatGPT, Gemini, or Perplexity and type something like: "recommend a hypoallergenic dog food for a medium-sized dog with a chicken allergy, under $60." Watch which brands come back, and notice what's absent from your own catalog when you check it against that exact request. If your PDP doesn't state the allergen exclusion, the breed-size fit, and a price attribute in a structured way, you are mathematically unable to appear in that answer — not because the AI dislikes your brand, but because it can't verify a claim it can't find.
This is also why schema markup keeps showing up in analyses of AI-cited pages: a large share of pages cited by AI search tools carry structured product markup, because it's the cleanest signal an answer engine can trust without having to infer meaning from prose.
What "AI-readable" actually requires
Three things, in order of leverage:
- Attribute completeness. Every field a shopper's conditional question could touch — life stage, breed/size fit, ingredient and allergen flags, health-condition suitability — filled in, not left blank or buried in a description.
- Consistency across the catalog. One SKU calling it "grain-free" and another calling it "no grains added" forces an AI to guess whether they mean the same thing. Consistent vocabulary is what lets an answer engine trust a pattern across your whole assortment.
- Structured markup on the page, not just in the backend feed, so the same facts are visible to both the shopper scanning the PDP and the crawler assembling an AI answer.
Most pet retailers' PIMs already hold a version of this data — it's just incomplete, inconsistent, or trapped in unstructured text. Anglera plugs into whatever PIM or commerce platform you're already running, continuously scores every product against gaps like these, and gap-fills the missing attributes so your existing catalog becomes something an AI can actually recommend from. Your PIM stores the data; Anglera does the work of making it legible to the systems now doing the shopping for your customers.