1,840 to 2,640 qualified leads a month.No new ad spend.

The client sells hydraulic fittings, adapters, and assemblies — roughly 8,500 SKUs across 150-plus categories. Through mid-2024, qualified inbounds had flattened at about 1,840 a month, and the cost of buying growth back through paid search kept climbing.

The trigger was visible in the results pages: AI Overviews had started answering the engineering queries that used to land on the client’s category pages — and the citations went to the manufacturers, whose spec sheets were the cleanest structured source available. A distributor lives downstream of its own suppliers in AI answers unless its catalog gives the engines something better to cite.

Catalog copy was largely manufacturer-supplied — the same descriptions ran on competitor sites — and the product schema had gaps wide enough that engines had no reason to prefer the client’s pages over the manufacturer’s. And the brief came with a hard line: grow qualified inbounds without adding ad budget. Whatever moved had to come from the catalog itself.

AI engines cite whichever page makes the answer easiest to assemble. The manufacturers were winning by default because their spec data was structured; the schema rewrite moved that advantage to the distributor — the same specs, plus stock, pricing, cross-compatibility, and application context a manufacturer page can’t offer.

Once the category pages and answer hubs gave the engines a better source, citations followed — and the traffic those answers route arrives pre-qualified. An engineer who lands from a thread-sizing answer already knows what they need; that is why the lead count moved faster than raw sessions did.