An aftermarket parts brand got its fitment catalog indexed and cited
Forty thousand SKUs, a year-make-model catalog Google could not crawl, and AI shopping answers that sent buyers to marketplaces. We made the fitment pages the product.
This is an anonymized composite of engagements of this type with aftermarket parts sellers, and the figures are typical of the work rather than one client's ledger. The brand designs and sells suspension, lighting, and exterior parts for trucks and SUVs, direct to consumer on Shopify with a fitment plugin, and through eBay Motors and Amazon. Its own site carried about 40,000 SKUs and a year-make-model selector that loaded results with JavaScript, so Google indexed the category pages and almost none of the fitment combinations buyers actually search. When a shopper asked ChatGPT or Perplexity for a leveling kit that fits a specific truck, the answer cited a marketplace listing or a competitor's guide. Over twelve months we rebuilt the catalog as crawlable fitment pages backed by the brand's ACES data, cleaned up product data so marketplace and site listings agreed, published install and comparison content, and earned coverage from truck and off-road publications. Organic revenue rose 112 percent year over year, fitment pages became the largest organic landing group, and AI shopping answers began citing the brand's own product and fitment pages.
A catalog buyers could use and Google could not.
The brand had good ACES fitment data and a Shopify store that hid it. The year-make-model selector rendered results client-side on one URL, so a search for a 2019 F-150 leveling kit had no page to land on. Google indexed about 900 URLs: collections, a few hundred product pages with generic titles, and the blog. The product pages themselves listed fitment as a scrolling table in the description with no structured data, so even when a page ranked, the snippet did not say which trucks it fit. Marketplaces had the same parts with better titles and more reviews, so they took the click and the sale, and the brand paid the fee.
The AI side followed the same pattern. When we asked ChatGPT, Perplexity, and Google AI Overviews for parts that fit specific trucks, the answers cited eBay Motors listings, Amazon, and two competitor install guides. The brand's name came up as a mention with no link. Product data was also inconsistent: the same part had three titles across the site, Amazon, and eBay, and part numbers on the site did not match the PIES export sent to the marketplaces. Backlinks were thin. Most referring domains were coupon sites, and the brand had never been reviewed or featured by the off-road and truck publications its buyers read.
Turn ACES data into pages, then make those pages the ones engines cite
A parts catalog wins organic search when every fitment combination a buyer types has a real page with a real title, a price, and stock. The brand already had the data. We built the page layer on top of it, fixed the product data so the same part looked the same everywhere, and then earned the links and citations that decide which page an AI engine trusts. Four lanes: fitment pages, product data, content, and digital PR.
The work, month by month
The same plan, laid out on the calendar it actually ran on.
Catalog audit and data cleanup
Mapped ACES and PIES data against the live site and marketplaces, reconciled titles and part numbers, and set the page generation rules.
Fitment pages live
Launched about 28,000 fitment pages with schema, re-pointed the selector, and submitted sitemaps in batches so Google crawled the high-volume trucks first.
Content and product schema
Published install guides and comparison pages, added fitment summaries and review schema to product pages, and wired everything into the fitment layer.
Digital PR and AI citation tracking
Earned reviews and buyer's guide placements from truck and off-road publications and tracked which pages ChatGPT, Perplexity, and Google AI Overviews cited for fitment queries.
What the audit found, and what shipped
The technical items that kept a 40,000 SKU catalog out of the index, with the fix for each.
Year-make-model selector loaded products client-side with no page per combination.
The link and citation graph after digital PR
Referring publications that now link to the brand's fitment and product pages, and that AI engines draw on when answering fitment questions.
The fitment pages now do the selling, on the site and in the answer
Twelve months in, organic revenue was up 112 percent year over year. Fitment pages went from nonexistent to the largest organic landing group, ahead of collections and the blog. Product pages ranked for part-number and model-plus-part queries with snippets that named the trucks they fit. ChatGPT, Perplexity, and Google AI Overviews started citing the brand's fitment and comparison pages for questions like which leveling kit fits a 2019 F-150, where the answer had previously been a marketplace listing. Marketplace sales did not fall; the direct channel grew alongside them at a better margin.
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