ACES/PIES in Plain English
The Minimum Data You Need Before You Scale on Marketplaces
If you’ve ever said, “Our parts fit - customers just order wrong,” you’re not alone. In the aftermarket, the difference between “it fits” and fitment confidence is almost always data.
Marketplaces (and even your own site) don’t magically infer what you meant. They ingest what you provide. And when the data is incomplete, inconsistent, or not standardized, the result is predictable:
Lower conversion (customers hesitate)
Higher returns (bad fitment selections)
More pre-sale questions (and support cost)
Listing suppressions or mapping failures (especially on marketplaces)
The fastest way to prevent this is to build your catalog on the industry standard foundations: ACES(fitment/applications) and PIES (product information).
This article explains, in practical terms, what you actually need to get right before scaling.
What ACES and PIES actually do (no jargon)
ACES = “What vehicles does this part fit?”
ACES is the standardized structure for fitment, using consistent vehicle definitions (year/make/model + configuration details like engine, submodel, body, etc.). Done right, ACES enables:
Accurate vehicle targeting
Fewer incorrect “universal” matches
Cleaner marketplace mapping
PIES = “What is this part, and why should I buy it?”
PIES is the standardized structure for product content: description, dimensions, features, brand, packaging, digital assets, and more. Done right, PIES enables:
Better search relevance
Higher conversion (clear details)
Fewer “will this work?” questions
ACES gets the part found for the right vehicle. PIES gets it chosen with confidence.
The minimum viable data stack (what you need before you scale)
If you’re trying to expand to eBay/Amazon/Walmart or add millions of fitments, these are the non-negotiables.
1) A clean part identity (the “spine” of your catalog)
Every SKU needs a stable spine that never changes:
Brand
Part number
Supplier part number (if different)
Internal SKU / ID (your system key)
UPC (if available)
Product category / part terminology alignment (standardized)
Why it matters: If the spine is shaky, everything downstream breaks: mapping, deduping, variation grouping, and attribute rules.
2) Fitment that is specific enough to prevent wrong orders
This is where most catalogs lose money. “Year/Make/Model” is rarely enough.
Minimum fitment should include the configuration fields that truly impact compatibility, such as:
Engine (and engine notes when needed)
Submodel / trim where relevant
Body style where relevant
Position (Left/Right/Front/Rear/Upper/Lower)
Quantity per vehicle (and whether it’s a set)
Notes that explain exceptions (“w/ towing,” “excluding sport package,” etc.)
Rule of thumb: If a shopper could order the wrong version without noticing, you need more configuration detail (or a clear exclusion note).
3) Product content that answers buyer objections (PIES basics)
At minimum, your PIES-style content should include:
Short and long descriptions (benefit-oriented, not just technical)
Feature bullets (3-7 is plenty)
Key dimensions/specs that shoppers compare
What’s included in the box (set vs single)
Warranty basics
Installation notes (when critical)
High-quality images (see next section)
Why it matters: If content is weak, your conversion rate becomes “price-only,” and returns increase because buyers guessed.
4) Digital assets that match the listing promise
Minimum digital assets:
Primary image: clean, well-lit, accurate product
2-6 supporting images: angles, connectors, bracket points, label/part number, “in-box” if helpful
If relevant: dimension image or simple diagram (this reduces returns)
Common mistake: A great primary image with no supporting images. Shoppers fill in the gaps… and that’s where returns come from.
5) Attribute completeness (the silent conversion driver)
Attributes are how platforms filter and match products. The basics vary by part type, but the concept is the same:
Capture the attributes customers use to compare options
Use consistent units and formats
Don’t mix “marketing” and “spec” fields
Examples:
Mirrors: heated, power, signal, memory, blind spot, fold type
Lights: bulb type, lens color, housing color, SAE/DOT, LED/HID/halogen
Suspension/steering: mount type, bushing material, greaseable, diameter/length
Why it matters: Missing attributes cause bad filtering, incorrect substitutions, and suppressed listings.
The “scale check” - 5 questions to ask before expanding
If you can’t answer “yes” to most of these, you’ll scale problems instead of revenue.
Do we have a single source of truth for part numbers and brand structure?
Can we explain why each fitment is valid (and where exceptions live)?
Are position/quantity/set logic consistent across the catalog?
Do our attributes follow a repeatable schema by part type?
Do we measure returns by reason code and tie them back to data gaps?
What happens when you get this right
When ACES/PIES-style discipline is applied correctly, you usually see:
Higher conversion (better confidence + better filtering)
Fewer returns tied to “doesn’t fit”
Better marketplace mapping and fewer listing issues
Faster onboarding of new SKUs (because rules become reusable)
More scalable operations (less manual firefighting)
A practical next step (low effort, high impact)
If you want a fast win, start with a targeted catalog audit:
Pick your top 20% SKUs by revenue (or by returns)
Validate fitment specificity (especially where there are multiple options)
Standardize position/quantity/set logic
Fill missing high-impact attributes
Improve image sets for the most-returned items
That’s often enough to create immediate measurable lift before you do anything “big.”
Need help turning ACES/PIES into real-world results?
At PartsAdvisory, we help aftermarket teams translate industry standards into practical execution-so you can sell more parts, reduce returns, and scale channels with confidence.
If you want, I can:
run a simple returns → data gap diagnostic framework you can use internally, or
outline a 30-day catalog stabilization plan tailored to your product types and channels.