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Who we serveAuto parts & aftermarket ecommerce

SEO and GEO for auto parts and aftermarket ecommerce

Year-make-model catalogs, ACES and PIES fitment data, eBay Motors and Amazon, and AI shopping answers that cite one specific part page. We make your fitment pages the ones that rank, and the ones ChatGPT and Perplexity point to when a buyer asks what fits their truck.

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Fitment catalogsACES and PIESMarketplacesAI shopping answers
Ranked #1ranked, year-make-model query
PARTS ECOMMERCE GROWTH · 12 MONTHSLIVE
+112%organic revenue, composite parts brand
Top 3
for core fitment queries
3
AI engines citing part pages
AI CITATION● CITED
ChatGPT cites your part page
THE HEADLINE NUMBER
+112%
organic revenue growth in a typical first year for an aftermarket parts catalog
4 of 5
parts queries include a vehicle or part number
3
AI engines citing fitment pages after GEO
CITEDChatGPT · Perplexity · AI Overviews
6 to 9 mo
to compounding fitment rankings
The parts buying reality

Parts buyers search by vehicle, not by brand. Your catalog has to answer that way.

A parts search starts with a year, a make, a model, and a symptom or a part number. Marketplaces already answer those queries at scale. We build the fitment, category, and brand pages that let your own site answer them too, and that AI shopping results can cite.

WHAT THE ECOMMERCE DIRECTOR ACTUALLY SEARCHESLIVE QUERY
brake pads for 2019 f-150
WITHOUT US, THEY FINDCompetitors, analysts, and generic listicles. Your brand is absent from the answer, so the committee forms an opinion without you.
SO WE BUILDIndexable fitment pages per vehicle and part type RANKED AND CITED FOR THIS QUERY
COMMITTEE COVERED
1/5
One fitment searchYear, make, model, and part type resolve to a page that ranks and gets cited.3 to 5FIELDS IN A TYPICAL PARTS QUERY1PART PAGE AN AI ANSWER CITES
Signature: the fitment authority network

The sources that decide whether a part page ranks and gets cited

A parts site earns authority from a specific set of neighbors: the enthusiast forums where fitment gets debated, the installer and how-to sites engines quote, and the marketplace listings that point back to your catalog. We map that network, then earn the links and mentions that move the center.

Owner forums (F-150, Silverado, Jeep)DR 62
Install and how-to publishersDR 58
Reddit r/MechanicAdvice, r/CartalkDR 91
YouTube install channelsDR 94
eBay Motors listingsDR 93
8
source types that typically feed a parts citation
2x
typical referring-domain growth in year one
The fitment problem

A selector is a feature. A page is a ranking.

Most parts sites let a shopper pick year, make, and model and then render results in the browser. Search engines and AI crawlers never see those results. We turn ACES fitment data into static, indexable pages, so every vehicle and part type you sell has a URL that can rank and be cited.

ACES application data mapped to one URL per vehicle and part type
Canonical and facet rules so filters stop competing with fitment pages
Product schema with vehicle compatibility for shopping surfaces
Part number and interchange pages for the buyers who search by number
CATEGORY DEFINITION
01your product name
02the [x] platform
03best [category] tool
04the categoryOWNED
1 URLper vehicle and part type
Tailored by sales motion

Every parts seller has a different catalog problem

Select a motion to see the challenge and the plays we run for it.

DIRECT-TO-CONSUMER CATALOG

The marketplaces own the vehicle query. Your site should too.

A shopper types a year, make, model, and part. The first page is Amazon, eBay Motors, and a couple of catalog giants. Your own site loses the click and the margin because its fitment pages are not indexable or not distinct.

+112%organic revenue, composite parts brand
PLAYS WE RUNDIRECT-TO-CONSUMER CATALOG
Indexable fitment pagesOne crawlable URL per vehicle and part type, built from ACES data, not from a selector.
Symptom category contentPages that answer the noise, vibration, or warning light a buyer searched before the part.
Product and fitment schemaMarkup that gives shopping surfaces price, stock, and compatibility in one read.
Citation trackingTrack which part pages ChatGPT, Perplexity, and AI Overviews cite for fitment questions.
What we do

A growth system built for a fitment catalog

DASHBOARDOne dashboard for parts revenueFitment rankings, AI citations, and organic orders by category live in one place, so SEO and GEO are measured against the same revenue line the finance team reads.
+112%
Organic revenue, composite
3
AI engines citing
SEO + AUDITSTechnical SEO for catalogsCrawl budget, faceted URL control, fitment page architecture, and schema across every SKU. Audit first, then a roadmap your dev team can ship.
GEOGenerative engine optimizationStructured fitment answers, part number pages, and buying guides that ChatGPT, Perplexity, and AI Overviews cite when a buyer asks what fits.
CONTENTContent for parts buyersSymptom guides, install walkthroughs, and comparison pages built around the queries that precede a purchase.
LINK BUILDING + REDDITLinks, PR, and communityLinks from forums, installers, and trade press, plus a real presence in the Reddit threads where fitment gets argued.
PROOF

Parts and data companies growing with us

All case studies →
PARTS ECOMMERCE+112%organic revenue YoYDirect-to-consumer aftermarket parts catalogFitment pages indexedAI shopping citationsVIEW CASE STUDY →AUTOMOTIVE DATA+64%organic traffic YoYB2B vehicle data APIs+2,400 keywords rankedAPI docs restructuredVIEW CASE STUDY →
Signature: the catalog crawl audit

What a parts catalog usually gets wrong before a crawler ever sees a part

Most parts sites hide their best pages behind a fitment selector, then let faceted URLs waste the crawl budget that the real pages needed. This is the audit we run on the catalog first.

SCAN RESULTS6 CHECKS
HEALTH SCORE41→88
Fitment pages behind a JavaScript selector● FAIL

Year-make-model results load client side. Crawlers see one empty page for every vehicle.

HOW WE RUN IT

From catalog audit to compounding parts revenue

1WEEK 1

Map the catalog

Fitment data source, platform, marketplace channels, and the vehicle and part queries that drive your gross.

2WEEK 2 TO 4

The catalog audit

Crawl, facet, schema, and citation gaps across SEO and GEO, with the fitment page architecture spec.

3MONTH 2+

Ship fitment pages and content

Indexable fitment URLs, symptom guides, schema, and links, on a roadmap your dev and catalog teams can run.

4ONGOING

Compound

Fitment rankings, AI citations, and organic orders tracked weekly and tuned as the catalog grows.

FAQ

Auto parts ecommerce SEO questions, answered

Ask us directly
For the queries that matter to you, yes. Marketplaces win the generic head terms. A focused catalog can win vehicle plus part type queries, part number queries, and symptom queries in its categories, because those pages can be more specific than a marketplace listing. We pick the categories where you have depth and build there first.
It is the raw material. ACES application data tells us every vehicle a part fits, and PIES gives us the attributes and descriptions. We turn that into static fitment pages, part attributes on page, and schema, instead of leaving it locked inside a selector.
The platform controls the catalog and most templates. You still control category copy, landing pages, blog content, local pages, and much of the technical configuration. We work inside those limits and document what needs a platform request.
Engines favor a page that states fitment plainly, carries product schema with compatibility, and is corroborated by forums, install guides, and reviews. We build those signals on the page and earn them off it, then track which of your pages get cited.
Typically 6 to 9 months to compounding results, faster for categories where you already have authority. Technical fixes and schema tend to show first, then fitment page rankings, then AI citations as corroborating sources accumulate. We report progress weekly and never promise a date.
Free consultation

See how parts buyers find you today

We run a free snapshot of your fitment rankings and AI citations before the call. No commitment.

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