There are two ways fitment is wrong, and the fixes are opposite.

Any audit that hands you a single quality percentage has averaged away the only distinction that matters. Fitment fails in two directions, they cost you in completely different ways, and repairing one does nothing for the other.

Failure 01

Over-application: claims vehicles it does not fit

This is the returns driver. It costs freight, refunds, and buyer trust, and it stays invisible until the parts start coming back. The fix is removing applications.

Failure 02

Under-application: missing vehicles it does fit

This is the visibility driver. It costs sales you never knew you were eligible for, and it is invisible forever unless you go looking. The fix is adding applications.

Most catalogues have both, in different parts of the range. Keeping the two counts separate from the first minute is the single most important decision in an audit, because a combined number tells you the size of a problem you cannot act on.

Start with one row per vehicle, not one row per listing.

Every check below runs in a spreadsheet against an export of your own live listings. You do not need a data provider, an ACES subscription, or anyone’s permission to run them, and knowing the size of your problem is worth more than any proposal you will be sent.

The one thing that matters in how you export: a row per part-and-vehicle pair, not a row per part. A listing carrying four hundred vehicles is four hundred rows. That shape is what makes the distribution checks possible, and collapsing it to one row per SKU hides exactly what you are looking for.

Four numbers, one afternoon, no vendor. That is enough to decide whether you have a small problem or a large one.

Six checks you can run yourself.

In the order worth running them. None requires a tool you do not already have.

Count the listings with no compatibility at all

The baseline. What share of your active parts listings carry no vehicle list whatsoever?
These are not badly matched, they are absent, and they are the cheapest wins in the whole exercise.
Split them by whether the part has a readable manufacturer number, because that decides which are fixable automatically.

Check granularity, not just presence

Of the listings that do carry vehicles, how many stop at year, make and model, and how many carry submodel and engine?
A part that varies by engine and is published without an engine is over-applied by construction, not by accident.
This check usually finds the returns you have been blaming on buyers.

Sort by vehicles per listing and look at both tails

The average tells you nothing. The shape tells you where to look.
Listings with one or two vehicles are usually under-applied, especially where the part is a common wear item.
Listings near the top of your range are usually over-applied. A part claimed against a thousand vehicles rarely fits a thousand vehicles.

Grep your titles and descriptions for qualifier words

Search for "except", "with", "without", "up to", "from", and any date.
Every hit is a real fitment condition sitting in free text where no system can read it. To a buyer filtering by vehicle, it does not exist.
These are the listings most likely to be technically accurate and commercially invisible at the same time.

Find duplicate and overlapping vehicle rows

The same part against the same vehicle twice, or a broad entry that already contains a narrower one underneath it.
Both inflate your coverage number without adding a single sale, which is why coverage alone is a poor metric.

Check for superseded part numbers

Are you publishing against numbers that were replaced? A superseded number usually drags its old vehicle list along with it.
This is a quiet and common source of fitment that looks complete and is wrong, and it will not show up in any of the five checks above.
If the number printed on a part returns nothing when you search it, that is the signal: it was retired, and whatever you published against it inherited an outdated vehicle list.

Rank what you find by vehicles on the road, not by error count.

This is the step that turns an unusable spreadsheet into a work plan, and it is the most useful thing you can do with an audit result.

A missing application on a platform with millions of cars in service is worth more than two hundred missing applications spread across vehicles almost nobody drives any more. An error count treats those identically. Sorting by how many of each vehicle are actually on the road does not.

The same logic runs in reverse for over-application. A part wrongly claimed against a high-volume platform is generating returns this week. The same error on a rare vehicle is a rounding error. Fix the first, schedule the second, and ignore anyone who tells you to work through the list in the order the spreadsheet produced it.

Five questions for anyone who says their data is verified.

Verified is the most overused word in this market and it is almost never defined. These questions apply to every provider quoting a large number, and they apply to ours exactly as they apply to anyone else’s.

Question 01

Verified against what?

A manufacturer feed, a reference database, a competitor’s catalogue, or an internal rule? Those are four very different claims wearing one word.

Question 02

Verified to what depth?

A relationship verified to year, make and model is not the same object as one verified to an engine configuration, even though both count as one in a headline.

Question 03

Counted how?

One part against one base vehicle, or one part against every submodel and engine combination underneath it? The second inflates the total by an order of magnitude without adding information.

Question 04

As of when?

The vehicle database changes with every model year. A number published with no date attached is a number from an unknown year.

The fifth question is the one that separates providers: what happens when two sources disagree? A provider who has never had two sources conflict has not checked. Ask what the resolution rule is, and whether the conflict is surfaced to you or silently decided.

Applies to us too

We publish a figure of 1.17 billion verified vehicle-to-part relationships. Counting rules differ enough between vendors that comparing headline totals tells you very little, which is exactly why the five questions above are more useful than the number they are asked about. Our parent company works through the same questions against an ACES catalogue in how to audit your own fitment data quality.

Do not ask for a coverage number. Give them a coverage test.

Take fifty part numbers from your own catalogue, chosen deliberately rather than at random, and ask any provider what they return for each.

Group A

Ten common wear items

The easy ones. Everybody handles these, so they tell you nothing except that the provider is functional.

Group B

Ten from your worst-selling range

Slow stock is often slow because it is badly described, not because nobody wants it.

Group C

Ten where fitment turns on engine code

European and diesel platforms especially. This is where year, make and model stops being sufficient.

Group D

Ten with no manufacturer number, and ten you know are wrong

These last two groups are the entire test. Anyone handles the easy forty. What separates providers is what happens where there is nothing to look up, and whether they catch the ten you already know are broken or quietly hand them back to you.

If a provider returns confident answers for all fifty including the ten you planted, that is not a good result. It means nothing in their process notices when it is wrong, which is the property that matters most once you stop watching.

Caveat

These checks measure your data against itself and against what is on the road. They do not replace confirming a specific fit against the manufacturer catalogue, and no dataset, ours included, is complete.

Find out which half your problem is.

Over-applied and under-applied are different jobs. Run the six checks, then fix the half that is costing you. First fifty SKUs free.

Start listing free