You already know SEO. Now you are staring at a 100,000-SKU industrial catalog. You can do title tags, internal links, schema. None of that is the hard part here. The hard part is that there are 200,000 pages, the descriptions were written by Parker and Rockwell instead of you, and the highest-intent queries are part numbers a keyword tool reports as nothing.
SEO at catalog scale
You don’t optimize pages at a distributor. You optimize templates. There are 200,000 SKUs and one product template, so a single change to the title pattern or the schema block hits every one of them at once. Get the template right and the whole catalog moves. Get it wrong and you shipped the same mistake 200,000 times. The classic failure is an SEO who has never seen catalog scale, hand-editing one hydraulic fitting page while 199,999 sit untouched. That isn’t slow. It’s the wrong unit of work.
Part numbers: zero volume, near-100% intent
A buyer types 1756-L61 replacement, or Gates equivalent of Parker 387 hose. Your keyword tool reports zero volume on both, so a generic SEO ignores them. That buyer is one search from cutting a purchase order. Nobody types a part number to browse. The pattern is densest in automation aftermarket and hydraulics: 1756-L61 replacement, what replaces the discontinued PowerFlex 4, SLC 500 to CompactLogix migration. The OEMs answer these in gated PDFs, the volume metrics say nothing, and the distributor who builds one crawlable page for the exact string wins a sale the analytics never saw coming.
Killing manufacturer-fed duplicate content
Every reseller of a Parker hose or a Rockwell drive runs the same manufacturer blurb. Word for word. So your product page is a thin mirror of a thousand others, and Google has no reason to pick yours. You fix it one of two ways: rewrite copy at template scale, or layer in data only you have — your stock, your lead times, your application notes, your cross-references. Add the SKF-to-NTN interchange to a 6205-2RS1 bearing page and it stops being a mirror. It starts being the better answer.
Faceted navigation and the PIM gate
Facet pages — by thread size, by pressure rating, by food-grade rating — are some of the best SEO real estate a distributor owns, because they match exactly how a buyer narrows a search. But you can only build the facets your PIM has clean attributes for. No normalized “thread size” attribute, no “by thread size” page, no spec table on it, no schema under it. So the SEO bottleneck usually isn’t SEO. It’s data completeness in the PIM. Your real job is often fighting for attribute coverage before a single facet page can exist.
Cross-reference and interchange pages
Interchange content is the highest-intent, lowest-competition asset a distributor can own. Equivalents, obsolescence migrations, brand-to-brand swaps. A regional Parker distributor’s Parker-to-Gates interchange chart can out-rank Parker’s own crossref tool — the distributor publishes flat crawlable HTML, and Parker buried theirs in a JavaScript app a crawler never renders. Discount Hydraulic Hose, HFI, and Tompkins already publish exactly this. The data usually lives in your PIM or ERP already. It just isn’t on a page yet.
Where catalog SEO meets AI search
The same extractable spec table that ranks in Google gets cited by an answer engine. That’s not two projects. Classic SEO is the foundation the AI-search work sits on top of: clean HTML specs, real Product schema, crawlable cross-references serve Google and the models reading the same page. The reverse holds too. A spec table locked inside a JavaScript widget or a PDF loses both lanes at once. So the catalog SEO you do for rankings is most of the catalog SEO you’d do to get cited. Same data, two surfaces.