Map parts to vehicle applications

Parts buyers need to know one thing: does it fit my vehicle? SKULaunch turns free-text application lists into structured vehicle applications on each part, so customers can filter by vehicle and trust the result.

The problem

Suppliers describe fitment in text: "Fits Ford Focus 2011 to 2018 1.6 petrol". Some put it in a description, some in a separate column, some in a PDF. Free text cannot drive a vehicle selector, cannot be checked for gaps, and is easy to misread. Wrong fitment means returns, bad reviews and lost trust.

Before and after

An illustrative example for a brake disc.

Before: the supplier's text

"Suitable for Focus Mk3 11-18, C-Max 10-19 (exc. ST)".

After: structured applications in SKULaunch

• Ford Focus, Mk3, 2011 to 2018
• Ford C-Max, 2010 to 2019, excluding ST variants
• Each application held as structured rows on the part
• Ready for a vehicle selector and marketplace compatibility fields

How it works in SKULaunch

1. Load your vehicle list. Use the make, model and year structure your site uses.
2. Extract applications. Text from descriptions, columns or documents is turned into structured application rows.
3. Match to vehicles. Applications are matched to your vehicle list, with exclusions kept.
4. Review and publish. Check applications, then publish to your site and channels.

Exclusions matter as much as inclusions

"Excluding ST" or "not for models with sports suspension" is where most fitment errors come from. If the exclusion is lost when text becomes data, the part shows as fitting a car it does not fit. Keep exclusions as structured data alongside each application, not as a note in the description.

What changes

Parts show against the right vehicles, customers can filter by their car, and fitment returns fall.

Common questions

Is fitment included?

Fitment and Applications are available as an add-on.

Which vehicle data do we use?

Your own vehicle list, or one aligned to the data source you use.

Can we export compatibility to marketplaces?

Yes, as structured data for channels that support it.

See fitment data, building fitment from supplier data, and ACES and PIES.