Nobody searches for a toy without saying how old the child is

Almost nobody shops this category without an age in the query. A parent types “toy for 3 year old girl” or “gifts for 7 year olds”, and everyone else uses the left-hand age filter, because a toy for the wrong age is not a compromise — it is a wasted gift.

The brand's catalog answered neither. The age-range attribute was blank on all sixty-one ASINs, so the listings simply vanished the moment a shopper touched the filter. The copy was no better: it described materials and play patterns in language a buyer would never type. The result was a catalog competing only on the widest, most expensive head terms, where it faced the category's biggest advertisers and lost.

We made every listing state, in structured data and in words, exactly who it is for.

• Age attributes completed across the catalog — the structured fields that drive the browse filters, aligned with the packaging's stated age grade.
• Age-modified keyword map — each product mapped to the age and occasion phrasings parents actually type, including the gift-shaped ones.
• Age language in titles and the first bullet, so the page confirms the fit within the first second of reading.
• Campaigns split by age cluster, which separated a 2–4 shopper from an 8–12 shopper and let bids follow the value of each.

Age-qualified sessions went from 11% to 44% of traffic, and revenue grew 67% on a catalog that did not gain a single new product.

The mechanism is filtering, not ranking: a listing with an empty age attribute is not outranked, it is excluded, and no bid can buy back a shopper who has already narrowed the page. Conversion rose 32% for the same reason in reverse — traffic arriving through an age filter has pre-qualified itself, which is also why ACoS fell eight points on a larger budget.