The state of product data in Automotive Aftermarket (2026)

Automotive aftermarket is one of the only industries with its own product-data standards body, and it still can't keep a catalog clean. ACES and PIES have existed for decades to make fitment and product information portable between manufacturers, distributors, and retailers. Yet in 2025-2026, with e-commerce growing, AI search reshaping discovery, and the Auto Care Association pushing out the biggest standards update in years, the gap between having a schema and having a complete catalog is more expensive than it's ever been.

What's actually broken

The industry has the infrastructure right. ACES (fitment) and PIES (product attributes, pricing, packaging) are machine-readable formats maintained by the Auto Care Association, backed by shared reference databases for vehicles, part categories, and attributes. In April 2026 the association released ACES 5.0 and PIES 8.0 after a yearlong industry review, adding support for digital assets, multilingual content, and Extended Producer Responsibility packaging data, explicitly to help the industry "deliver richer content, improve data accuracy and adapt to evolving global and regulatory needs" (Auto Care Association).

That's real progress on the schema. It doesn't fill in a single field. The same announcement points at the ongoing gap: the association's self-serve Catalog Assessment Tool, which lets a brand upload an ACES or PIES file and get an automated error report back in minutes, has already run hundreds of brands through it and surfaced logic and data-validation problems before those files ever reached a trading partner. A standard that hundreds of brands are still failing basic validation against isn't a data-quality solution — it's a diagnostic. The actual data — dimensions, materials, fitment notes, part numbers correctly mapped to the right vehicle configuration — is still built manually, supplier by supplier, spreadsheet by spreadsheet, in most manufacturer and distributor back offices.

Here's what that looks like on an actual product page, for something as common as a brake pad set:

Raw feed description: Brake Pad Set - Front

What an enriched attribute table looks like:

The first line is a SKU with a price tag. The second is what actually resolves whether the part fits a specific customer's truck. Most manufacturer feeds still ship closer to the first line, with fitment notes buried in a PDF cut sheet if they exist at all.

What it actually costs

Thin, inconsistent aftermarket data shows up as real, measurable damage in three places:

• Returns. Fitment mismatches are consistently cited as the single largest driver of auto parts returns, with return rates on some online storefronts running as high as 20% and industry accounts putting fitment errors behind close to half of those (X-Cart). Every one of those is a part shipped, a part shipped back, and a customer who now questions the next order too.
• Lost search and filter visibility. A brake pad listing with a blank tow-package field doesn't rank lower when a buyer filters by that spec — it disappears from the results entirely, on the retailer's own site and in Google's AI Overviews alike.
• Thin PDPs that don't convert. A page with a part number and a stock photo gives a DIY buyer no way to self-confirm fitment, which either kills the sale or pushes it back onto a phone call a counter rep now has to handle manually.

Manual enrichment doesn't scale against catalogs built from hundreds of manufacturer brands and constantly shifting vehicle configuration data. Properly researching a spec sheet, mapping it to the right VCdb vehicle record, and writing a clean attribute set runs in the 30-45 minute range per SKU — against catalogs with hundreds of thousands of line items, that math never closes.

Why 2025-2026 raises the stakes

Three things are converging at once.

AI search is replacing the click. Google's AI Overviews have shifted auto parts discovery from deterministic, rule-based rankings to a probabilistic model where an AI system is making a judgment call about which part to recommend, often without sending the buyer to a product page at all (Hedges Company). Ask an answer engine "what brake pads fit a 2016 F-150 with the Max Tow package" and it can only recommend a specific SKU if the underlying fitment data distinguishes tow-package trims from base trucks. A listing that just says "Front, various F-150" gets skipped in favor of a competitor's line item with that distinction spelled out.

The buyer and the channel are both shifting. The Auto Care Association and MEMA Aftermarket Suppliers' 2025 Joint E-Commerce Trends and Outlook Forecast puts U.S. aftermarket e-commerce at roughly $23 billion excluding third-party marketplaces and $44.6 billion including them in 2025, growing 4.6% that year with a 5.4% compound annual rate through 2030 (aftermarketNews). Consumers are now splitting spend roughly equally between online and offline channels and routinely researching a part number online before ever walking into a counter — which means the catalog data has to do the selling work a counter rep used to do in person, for a buyer generation that's used to Amazon-level product content everywhere else it shops.

The standard just got harder to keep up with. ACES 5.0 and PIES 8.0 add new fields for digital assets, multilingual content, and packaging data on top of an already sprawling attribute set. Every version bump widens the gap between what the schema now supports and what's actually populated in a manufacturer's live feed.

Where this goes

None of this gets fixed by adopting the next version of a standard, because the standard was never the bottleneck — the labor to populate it, consistently, at catalog scale, always was. Anglera plugs into whatever PIM, ERP, or flat file a manufacturer or distributor already runs — no rip-and-replace, no multi-year integration — and continuously scores, gap-fills, and enriches product attributes straight from supplier documentation, values extracted and quality-scored rather than guessed at. The PIM still stores the data. Making sure that data is complete, current, and legible to a buyer or an AI assistant is the work Anglera does on top of it.