Marketplace Readiness Checklist

25 Data Checks Before You Launch on eBay, Amazon, or Walmart

Launching on marketplaces can feel like a simple expansion: upload listings, push inventory, turn on ads.

In reality, most marketplace “launch problems” aren’t marketing problems - they’re data problems. And when the data isn’t ready, you get the same outcomes every time:

• listing errors and suppressed items

• poor fitment confidence → low conversion

• higher “doesn’t fit” returns

• endless manual fixes and exceptions

• wasted time rebuilding listings that should have worked the first time

This checklist is designed to help you validate your catalog before you scale. Run it once, fix what fails, and you’ll launch faster with fewer painful surprises.

How to use this checklist

Pick a representative slice of your catalog (don’t start with everything):

• top 200-500 SKUs by sales, or

• top SKUs by returns, or

• your first category to launch (mirrors, lights, suspension, etc.)

Then score each item Pass / Needs Work. The goal isn’t perfection - it’s preventing predictable failure.

25 Data Checks Before You Launch

A) Part Identity and Catalog Spine (1-5)

These prevent duplicates, mapping conflicts, and bad listing builds.

1. Brand name is consistent (no variations like “ABC”, “A.B.C.”, “ABC Auto”).

2. Manufacturer Part Number (MPN) is stable and formatted consistently.

3. Each SKU has a single internal unique ID (no duplicates representing the same product).

4. UPC/GTIN exists where applicable (and is valid format).

5. Product is assigned to the correct standardized part type / category (your taxonomy is consistent).

B) Fitment Confidence (ACES logic) (6-12)

These prevent the most expensive problem: fitment returns.

1. Fitment is not just Y/M/M when configuration matters (engine, drivetrain, body, etc.).

2. Engine qualifiers are included wherever multiple engine options exist.

3. Submodel/trim qualifiers are included where needed (sport, premium, etc.).

4. Position is explicit (Left/Right, Front/Rear, Upper/Lower) when applicable.

5. Quantity per vehicle is clearly defined (single vs pair vs set).

6. Inclusion/exclusion notes exist for known exceptions (w/ tow, w/o hybrid, excluding package).

7. You have a rule for overlaps and conflicts (one SKU shouldn’t fit both mutually exclusive configs unless you can prove it).

C) Title and Buyer Clarity (13-16)

These reduce pre-sale questions and stop “I thought it came as a pair” returns.

1. Title includes: Part type + key qualifier + position (when needed).

2. Title indicates set vs single (Pair/Set/1 Pc) when relevant.

3. Title avoids confusing abbreviations shoppers don’t understand (or you standardize them).

4. Title does not promise features the product doesn’t have (heated, power fold, LED, etc.).

D) Attributes and Filtering (17-20)

Attributes drive marketplace filters, mapping, and shopper confidence.

1. You have a required attribute template by part type (mirrors ≠ lights ≠ suspension).

2. Attribute values are standardized (Yes/No, units, consistent naming).

3. Critical fitment-related attributes are captured (example: mirrors, heated/power/signal; lights, bulb type/DOT; suspension, diameter/mount).

4. No “junk attributes” (marketing text shoved into spec fields).

E) Images and Digital Assets (21-23)

Images are often the easiest return reducer.

1. Each SKU has a clean primary image that matches the exact variant.

2. You have supporting images that answer questions: connectors, brackets, mounting points, label/part number.

3. Complex parts include at least one detail or dimension-style image when it prevents mistakes.

F) Operational Launch Readiness (24-25)

These prevent “we listed it, but we can’t fulfill it cleanly” issues.

1. Inventory + lead time rules are defined (in-stock vs backorder behavior, oversell protection).

2. Shipping dimensions/weights are accurate enough to avoid cost surprises and late delivery defects.

What “ready” looks like

If you pass:

• 20+ of 25 → you’re in strong shape to scale

• 15-19 → launch is possible, but expect manual firefighting

• under 15 → you’ll likely create returns and listing failures faster than you create revenue

The fastest way to improve results (without rebuilding everything)

Focus your fixes on the areas that create the biggest downstream impact:

1. Fitment specificity + notes (prevents “doesn’t fit”)

2. Position/quantity/set clarity (prevents misorders)

3. Required attributes by part type (improves filters + mapping)

4. Image completeness (reduces uncertainty and returns)

These changes compound as you scale.