Online shopping now has an intermediary. Before a customer reaches your store, an assistant has often already compared options and produced a shortlist — filtering on price, attributes, materials, dietary needs, sizing, whatever the question demanded. Products that cannot be read in that comparison are not rejected; they are never considered.
This makes product data a revenue question rather than a technical chore. Complete, accurate schema and healthy feeds are what let you be compared at all. Then the second half of the problem takes over: pre-sold visitors still abandon perfectly good stores over slow pages, unclear delivery terms, thin product information and awkward checkouts. Both halves have to work, and most stores are weak on one or the other.
What the work actually consists of
- Complete Product, Offer and Review schema so assistants and search engines can match your catalogue to a specific request.
- Feed health keeping price, stock and variants correct everywhere machines read them.
- Category and comparison content that answers how shoppers phrase things, not how your catalogue is organised.
- Conversion work across browse-to-buy — speed, product detail, objection handling and checkout friction.
- Retention and repeat purchase, because the second order is materially cheaper than the first.
What to expect, honestly
Conversion improvements can land quickly and are measurable almost immediately. Discoverability in search and AI shopping builds over a few months and then compounds. The strategic aim is a revenue mix that leans less on paid acquisition every quarter — growth you own rather than rent by the click.